Association Between Venetoclax Concentration and Febrile Neutropenia in Acute Leukemia: A Retrospective Analysis

Association Between Venetoclax Concentration and Febrile Neutropenia in Acute Leukemia: A Retrospective Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Venetoclax Concentration and Febrile Neutropenia in Acute Leukemia: A Retrospective Analysis Yuting yan, Jin Sun, Yujiao Guo, Jieyu Sun, Yu Zhu, Luning Sun, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8247030/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Venetoclax, a selective BCL-2 inhibitor, demonstrates efficacy in acute leukemia (AL) but variable pharmacokinetics. Therapeutic drug monitoring (TDM) serves as a critical tool for evaluating the efficacy and safety of venetoclax therapy. This study explored the factors influencing plasma venetoclax concentrations and their correlation with febrile neutropenia (FN), aiming to establish predictive thresholds. Methods AL patients receiving venetoclax between August 2023 and March 2025 were retrospectively analyzed. Plasma concentration, baseline characteristics, pharmacogenomics, and clinical parameters were collected. Univariate and multivariate linear regression analyses were performed to identify determinants of venetoclax concentration and their association with FN incidence Receiver’s operating characteristic (ROC) curve defined FN-predictive thresholds. Results Among 123 patients, median trough concentration (C 0 ) was 1409.88 ng/mL and peak concentration (C 6 ) was 2373.21 ng/mL. Multivariate analysis identified age, azoles, and transplantation status as C 0 determinants, and ABCB1 1236C>T (rs1128503) TT genotype predicted elevated C 6 . Body weight, azoles, and transplantation status independently affected C 0 /D (C0 normalized to the administered dose, D), while D-dimer (DD2) level and azoles were significant determinants of C 6 /D. Patients with venetoclax C 0 levels exceeding 1202.07 ng/mL or a C 0 /D ratio greater than 6.85 ng/mL per mg exhibited a significantly higher incidence of FN. Conclusion Higher venetoclax concentrations increase FN risk. Regular monitoring of blood venetoclax concentration using TDM can predict the risk of FN occurrence. The determination of this concentration threshold can provide a basis for optimizing the treatment quality of AL patients. Venetoclax therapeutic drug monitoring febrile neutropenia acute leukemia pharmacogenomics Figures Figure 1 Figure 2 Introduction Acute leukemia (AL) is a malignant clonal hematopoietic stem cell disorder characterized by excessive proliferation of blasts and immature cells in the bone marrow or peripheral blood as well as widespread into lymph nodes, liver, spleen, and other organs. Clinically, it manifests as suppressed normal hematopoiesis, infections, hemorrhage, and overall poor prognosis. Currently, AL is classified by bone marrow cytomorphology into acute myeloid leukemia (AML), acute lymphoblastic leukemia, and acute leukemia of ambiguous lineage [ 1 – 2 ]. Conventional therapeutic approaches for AL include intensive chemotherapy induction followed by consolidation therapy or allogeneic hematopoietic stem cell transplantation to eliminate minimal residual disease. Due to the increasing molecular heterogeneity among patients, however, these approaches no longer meet the current clinical demands, prompting the development of novel therapeutic strategies, particularly small-molecule targeted agents [ 3 ]. Venetoclax is a highly selective and potent small-molecule kinase inhibitor targeting BCL-2, and has demonstrated remarkable efficacy and manageable safety in various hematologic malignancies, especially AL [ 4 – 7 ]. Current clinical guidelines recommend its use not only in adults but also extend to pediatric and infant populations. Pharmacokinetic studies on venetoclax reveal significant interindividual variability in pharmacokinetic parameters under different dietary conditions, ethnic backgrounds, or concomitant medications [ 8 – 9 ]. Venetoclax, a P-glycoprotein (P-gp) substrate metabolized via CYP3A4/5, demonstrates marked interindividual exposure variation, largely driven by SNPs in P-gp and CYP3A4/5 genes. Notably, ethnically stratified CYP3A4/5 allele frequencies correlate with elevated plasma concentrations in Asian populations [ 10 ], implicating pharmacogenetic influences in population-specific pharmacokinetics of venetoclax..In addition, venetoclax exhibits clinically relevant drug-drug interactions with voriconazole, posaconazole, digoxin, and other agents, potentially altering its plasma concentration and affecting therapeutic outcomes and safety [ 11 – 13 ]. Consequently, therapeutic drug monitoring (TDM) of venetoclax is crucial for optimizing clinical decision-making. Febrile neutropenia (FN), defined as severe neutropenia accompanied by fever, is associated with the myelotoxicity of specific chemotherapeutic agents, dose intensity, patient-specific factors, and concomitant medications. FN develops in more than 80% of hematologic patients following the first cycle of chemotherapy [ 14 ]. However, 15–30% of cases remain without identifiable pathogens despite comprehensive diagnostic evaluation, and mortality rates remain substantial [ 15 ]. Consequently, FN represents a critical complication that requires vigilant management during treatment for hematologic malignancies. The current evidence suggests that venetoclax administration represents one of the high-risk factors for FN development [ 16 – 18 ]. Real-world data indicate that the incidence of grade ≥ 3 venetoclax-associated FN may reach 50%, and some patients even require treatment discontinuation due to FN [ 19 ]. The occurrence of this event may be attributed to the potent myelosuppressive effects of venetoclax. Thus, effective management of venetoclax-related adverse events is critical for ensuring long-term treatment benefits. However, no study has yet explored the specific plasma venetoclax concentration threshold that leads to the occurrence of FN. This real-world study is aimed to measure trough and peak concentrations of venetoclax in AL patients and analyze the influencing factors of its pharmacokinetics as well as the association between drug concentration and FN. The findings are expected to provide evidence for personalized venetoclax dosing strategies, thereby enhancing treatment safety in AL patients. Materials and Methods Patients Totally 123 AL adult patients who received venetoclax therapy at the First Affiliated Hospital of Nanjing Medical University between August 2023 and March 2025 were enrolled. Inclusion criteria comprised: (1) confirmed AL diagnosis via bone marrow cytomorphology, molecular biology, cytogenetics, and immunophenotyping; (2) age ≥ 18 years; (3) Eastern Cooperative Oncology Group performance status 0–2; (4) good treatment compliance; (5) no prior venetoclax treatment; (6) continuous venetoclax administration for ≥ 7 days before blood sampling for drug concentration analysis; (7) availability of pharmacogenetic data, including ABCB1 3435C>T (rs1045642) , ABCB1 1236C>T (rs1128503) , ABCB1 2677G>T/A (rs2032582) , CYP3A5 6986A>G (rs776746) , and CYP3A4 20230G>A (rs2242480) genetypes. Exclusion criteria consisted of (1) missing venetoclax concentration assessments; (2) pretreatment adverse drug reactions; (3) poor adherence; (4) incomplete medical records. This study was approved by the Ethics Committee of The First Affiliated Hospital with Nanjing Medical University (Approval Code: 2025-SR-244), and written informed consent was obtained from all participants. Data Collection Baseline demographic and clinical characteristics were recorded, including sex, age, weight, height, body mass index (BMI), and body surface area (BSA). Laboratory parameters including white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin (Hb), platelet count (PLT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (CREA), total bilirubin (TBIL), brain natriuretic peptide (BNP), and D-dimer (DD2) were monitored, and creatinine clearance (CrCl) was calculated using the Cockcroft-Gault equation. Comorbidities, concomitant azole antifungal use, disease classification, transplant status, and chemotherapy regimens were documented. Patients were risk-stratified according to the 2022 European LeukemiaNet (ELN) criteria based on cytogenetic and molecular profiling. All clinical data were extracted from electronic medical records. Quantification of Venetoclax by high-performance liquid chromatography - tandem mass spectrometry (HPLC-MS/MS) Trough (C 0 ) and peak (C 6 ) concentrations of venetoclax were measured after ≥ 5 consecutive days of treatment to ensure steady-state exposure [ 20 – 21 ]. Peripheral venous blood samples were collected in K2EDTA-anticoagulated heparinized vacuum tubes and then centrifuged at 4 ℃ and 4000 rpm for 8 minutes to obtain plasma samples. Venetoclax concentrations were determined via HPLC-MS/MS using [2H8]-venetoclax (venetoclax-d8) as the internal standard (IS). Chromatographic separation was achieved on an ACQUITY UPLC HSS T3 column (2.1×50 mm 2 , 1.8 µm) with gradient elution (mobile phase: 10 mM ammonium formate with 0.1% formic acid and acetonitrile, 40:60 v/v). Analytes were quantified in the multiple reaction monitoring (MRM) mode using a SCIEX Triple Quad™ 5500 + mass spectrometer and by monitoring mass transitions of m/z 868.3→636.3 (venetoclax) and m/z 876.3→644.3 (IS). This method demonstrated a linear dynamic range of 20.0-5000 ng/mL, with intra- and inter-assay precision (relative standard deviation, RSD) < 13.6% and accuracy within ± 11.9%. Recovery rates approximated 100% [ 22 ]. To account for interindividual dose variability, dose-adjusted venetoclax plasma concentrations were calculated as concentration per dose and denoted as C 0 /D for trough and C 6 /D for peak., which enabled comparative pharmacokinetic analysis across subjects. Pharmacogenomic Testing Genomic DNA was extracted from peripheral venous blood clots, which had been stored at -80℃, using the DP335-02 DNA extraction kit (KeyGEN BioTECH, Jiangsu, China) according to the manufacturer's protocol. Then variants ABCB1 3435C>T (rs1045642) , ABCB1 1236C>T (rs1128503) , ABCB1 2677G>T/A (rs2032582) , CYP3A5 6986A>G (rs776746) , and CYP3A4 20230G>A (rs2242480) were genotyped. These single-nucleotide polymorphisms (SNPs) exhibit relatively high allele frequencies in Asian populations [ 10 , 23 – 24 ]. The analysis utilized a MassARRAY SNP genotyping platform (Agena Bioscience), which is based on matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF MS). Evaluation of FN Prior to venetoclax initiation, all enrolled patients underwent comprehensive physical examinations and laboratory assessments, including cardiac function, hematologic, and biochemical profiling. Throughout the treatment period, daily temperatures were monitored with hematologic and biochemical evaluations performed every other day. FN was defined as an adverse event occurring post-venetoclax administration characterized by neutropenia concurrent with fever, by strictly adhering to the Common Terminology Criteria for Adverse Events (CTCAE 5.0) issued by the U.S. Department of Health and Human Services (2017). The diagnostic criteria required: 1) ANC 38.3℃(101℉) or sustained temperature ≥ 38℃ (100.4℉) for > 1 hour [ 25 ]. Statistical Analysis Descriptive statistics summarized baseline characteristics and event data. Normally distributed continuous variables were expressed as mean ± standard deviation (X ± S); non-normal data were shown as median (min-max). Categorical variables were presented as percentages (%). Hardy–Weinberg equilibrium was tested for each polymorphic locus. Associations between venetoclax concentrations (C 0 , C 0 /D, C 6 , C 6 /D) and categorical variables (e.g., gender, prognostic group, pharmacogenotypes) were analyzed using univariate analyses (Spearman rank correlation, Mann-Whitney U test, Kruskal-Wallis test for non-normal data; Student's t-test, ANOVA for normal data with homogeneity of variance) and multiple linear regression analyses. Continuous variable associations were assessed via Spearman correlation. Receiver’s operating characteristic (ROC) curves were plotted to define the cut-off values for predicting FN (maximizing Youden's index). A two-sided P-value < 0.05 was considered statistically significant. All analyses were finished on SPSS Statistics 27.0 and GraphPad Prism 8.0. Results Clinical Characteristics This study enrolled a total of 123 patients comprising 73 males and 50 females, with a median age of 55 years [interquartile range (IQR): 28]. A total of 66 patients were included in the pharmacogenomic analysis. All patients received venetoclax-based chemotherapy regimens and underwent TDM. A total of 98 trough-concentration and 101 peak-concentration samples were collected. The median concentrations of the included patients were as follows: C 0 = 1409.88 (IQR: 1317.95) ng/mL; C 6 = 2373.21 (IQR: 2047.76) ng/mL; C 0 /D = 4.02 (IQR: 10.00) ng/mL per mg, and C 6 /D = 7.62 (IQR: 13.82) ng/mL per mg. The detailed baseline characteristics of the 123 patients are presented in Table 1 . Table 1 Clinical Characteristics of Enrolled Patients (n = 123) Clinical Characteristics Value Gender (n) (%) Male 73(59.35%) Female 50(40.65%) Age (years) (mean ± SD) 53 ± 17 Height (cm) Median (mean ± SD) 167.16 ± 8.47 Weight (kg) Median (mean ± SD) 64.84 ± 11.78 BSA (m 2 ) Median (mean ± SD) 1.70 ± 0.19 BMI (kg/m 2 ) Median (mean ± SD) 23.13 ± 3.35 Chronic Disease (n,%) With comorbidities 37(30.08%) Without comorbidities 86(69.92%) Azoles (n,%) With combination 47(38.21%) Without combination 76(61.79%) Indication (n) (%) AML 82(66.67%) ALL 36(29.27%) ALAL 5(4.06%) Risk Stratified (n) (%) Good 50(40.65%) Moderate 27(21.95%) Poor 45(36.59%) Unknown 1(0.81%) Transplantation (n) (%) Transplanted 6(4.88%) Non-transplanted 117(95.12%) Chemotherapy Regimens (n) (%) Monotherapy 2(1.62%) Venetoclax and demethylation drugs 79(64.23%) Venetoclax and chemotherapeutic agents 23(18.70%) Venetoclax and antibody 10(8.13%) Venetoclax and kinase inhibitor 9(7.32%) Laboratory Data Median (range) WBC (10 9 /L) 7.42(0.31,133.08) ANC (10 9 /L) 1.01(0.00,52.70) Hb (g/d) 80.00(51.00,143.00) PLT (10 9 /L) 48.00(3.00,537.00) ALT (U/L) 20.00(7.00,132.70) AST (U/L) 20.30(7.00,365.00) CREA (µmol/L) 64.00(35.00,171.00) TBIL (µmol/L) 8.90(4.07,21.60) BNP (pg/mL) 144.00(14.06,1717.00) DD2 (mg/L) 1.72(0.1,36.54) Duration of Venetoclax Therapy (days) Median (range) ≤ 14 46(37.40%) >14 77(62.60%) ABCB1 3435C>T (rs1045642) Genotype (n) (%) TT 8(6.50%) TC 28(22.76%) CC 30(24.39%) Unknown 57(46.35%) ABCB1 1236C>T (rs1128503) Genotype (n) (%) TT 30(24.39%) TC 25(20.32%) CC 11(8.94%) Unknown 57(46.35%) ABCB1 2677G>T/A (rs2032582) Genotype (n) (%) TT 10(8.13%) TG 18(14.63%) TA 7(5.69%) GG 17(13.82%) GA 10(8.13%) AA 4(3.25%) Unknown 57(46.35%) CYP3A5 6986A>G (rs776746) Genotype (n) (%) GG 39(31.71%) AG 22(17.88%) AA 5(4.06%) Unknown 57(46.35%) CYP3A4 20230G>A (rs2242480) Genotype (n) (%) GG 44(35.77%) GA 18(14.63%) AA 4(3.25%) Unknown 57(46.35%) Venetoclax trough concentration (C 0 ) (ng/mL) Median (range) 1409.88(257.67,5477.95) Venetoclax peak concentration (C 6 ) (ng/mL) Median (range) 2373.21(1028.39,6850.88) C 0 /D Median (ng/mL per mg) (range) 4.02(0.3,48.08) C 6 /D Median (ng/mL per mg) (range) 7.62(1.01,68.51) BSA: body surface area; BMI: body mass index; AML: acute myeloid leukemia; ALL: Acute lymphoblastic leukemia; ALAL: acute leukemia of ambiguous lineage; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer. Data are median (range) or n (%). Hardy–Weinberg equilibrium was assessed for each variant using an exact test in SPSS Genetics module 27.0. No significant deviation was observed (all P_HWE ≥ 0.19), indicating absence of genotyping error or population stratification. The detailed results are presented in Table 2 . Table 2 Hardy–Weinberg equilibrium test for pharmacogenetic variants SNP Alleles (major/minor) MAF Call rate (%) Genotype counts (observed/expected) X 2 P_HWE rs1045642 C/T 0.33 100 CC 8/7.33; CT 28/29.33; TT 30/29.33 0.14 0.71 rs1128503 C/T 0.36 100 CC 30/27.37; CT 25/30.26; TT 11/8.37 2.00 0.19 rs2032582 G+/G- 0.47 100 G + 45/43.44; G−21/22.56 1.44 0.27 rs776746 A/G 0.24 100 AA 39/37.90; AG 22/24.20; GG 5/3.88 0.55 0.52 rs2242480 G/A 0.20 100 GG 44/42.55; GA 18/20.90; AA 4/2.56 1.26 0.32 SNP: single nucleotide polymorphism; MAF: minor allele frequency; P_HWE were obtained by Fisher exact test; G+: carriers of the 2677G allele; G-: non-carriers of the 2677G allele. Univariate Analysis The Spearman rank correlation analysis revealed a positive correlation between C 0 and age (r = 0.249, P = 0.014), but this association was attenuated after dose adjustment (C 0 /D vs. age: r = 0.189, P = 0.065). A negative correlation was observed between C 6 and baseline platelet counts (r = − 0.211, P = 0.037), which became more pronounced following dose correction (C 6 /D vs. baseline platelet counts: r = − 0.251, P = 0.013). Additionally, C 6 /D was negatively correlated with baseline neutrophil counts (r = − 0.2, P = 0.045). The Mann-Whitney U test demonstrated significant differences in both C 0 and C 0 /D among patients who had undergone hematopoietic stem cell transplantation (z = − 1.714, P = 0.020; z = − 1.714, P = 0.021). Similarly, patients receiving concomitant azole therapy significantly differed in C 0 /D and C 6 /D (z = − 2.934, P = 0.003; z = − 3.728, P < 0.001). Furthermore, C 6 was significantly different in patients with venetoclax treatment duration ≤ 14 days (z = 0.239, P = 0.016). Details are presented in Table 3 . Table 3 Univariate Analysis of Factors Influencing venetoclax blood concentration Independent Variables C 0 C5.5 C0/D C5.5/D z/r/H value P value z/r/H value P value z/r/H value P value z/r/H value P value Gender −0.018 0.986 −1.096 0.273 −0.466 0.641 −0.517 0.605 Age 0.249 0.014 0.069 0.493 0.189 0.065 0.094 0.352 Height 0.019 0.856 0.035 0.734 −0.002 0.984 0.093 0.362 Weight −0.067 0.509 0.036 0.719 −0.134 0.192 −0.023 0.818 BSA −0.049 0.635 0.044 0.659 −0.111 0.283 0.005 0.964 BMI −0.069 0.499 0.027 0.794 −0.14 0.177 −0.074 0.465 Chronic Disease −1.352 0.176 −0.156 0.876 −0.184 0.854 −0.624 0.533 Azoles −1.648 0.099 −1.085 0.278 −2.934 0.003 −3.728 <0.001 Indication 0.662 0.718 0.742 0.69 0.432 0.806 0.05 0.975 Risk Stratified 0.339 0.844 2.645 0.266 0.053 0.974 0.044 0.978 Transplantation −1.714 0.02 −0.79 0.429 −1.714 0.021 −1.623 0.104 Chemotherapy Regimens 4.351 0.226 0.824 0.935 2.771 0.428 1.035 0.904 WBC −0.189 0.063 −0.098 0.327 −0.106 0.303 −0.189 0.059 Ne −0.083 0.415 −0.008 0.938 −0.084 0.414 −0.2 0.045 Hb −0.029 0.774 −0.032 0.748 0.057 0.582 −0.03 0.765 PLT −0.089 0.387 −0.211 0.037 0.042 0.682 −0.251 0.013 ALT −0.049 0.629 −0.114 0.254 −0.042 0.684 −0.044 0.661 AST −0.044 0.664 −0.082 0.415 −0.101 0.327 −0.074 0.46 CREA 0.062 0.547 0.004 0.97 −0.004 0.973 −0.043 0.668 TBIL 0.057 0.606 −0.007 0.952 0.021 0.851 −0.01 0.93 BNP −0.006 0.96 −0.129 0.278 −0.06 0.614 −0.159 0.179 DD2 0.003 0.978 −0.148 0.153 −0.01 0.925 −0.154 0.136 Duration of Venetoclax Therapy 0.008 0.939 0.239 0.016 0.111 0.281 0.164 0.101 ABCB1 3435C>T (rs1045642) Genotype 2.127 0.345 0.015 0.993 1.550 0.461 0.287 0.866 ABCB1 1236C>T (rs1128503) Genotype 0.315 0.854 2.454 0.293 0.606 0.739 4.802 0.091 ABCB1 2677G>T/A (rs2032582) Genotype 0.779 0.978 1.031 0.960 1.016 0.961 0.863 0.973 CYP3A5 6986A>G (rs776746) Genotype 1.440 0.487 1.466 0.480 2.036 0.361 1.792 0.408 CYP3A4 20230G>A (rs2242480) Genotype 3.227 0.199 3.082 0.214 0.892 0.640 4.942 0.084 BSA: body surface area; BMI: body mass index; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer. P values were obtained using univariate analysis. All data were analyzed using the Spearman rank correlation (r refers to Spearman's correlation coefficient), Mann-Whitney U test (z refers to the standardized test statistic) and Kruskal-Wallis test (H refers to the standardized test statistic). Multiple Linear Regression Analysis To avoid the potential omission of confounding factors affecting plasma venetoclax concentrations, all variables with P < 0.2 in univariate analysis were incorporated into multivariate linear regression models. Given the limited number of significant variables identified for C 6 in univariate analysis, a more inclusive threshold of P < 0.3 was adopted for subsequent multivariate regression. The analyses revealed four significant regression models. For C 0 (F = 9.954, adjusted R²=0.217, P T (rs1128503) genotype emerged as the sole determinant. For C 0 /D (F = 19.084, adjusted R²=0.366, P < 0.001), body weight, azole coadministration and prior transplantation were significant. For C 6 /D (F = 4.656, adjusted R²=0.196, P = 0.018), both azoles and DD2 level demonstrated predictive values. Complete regression results are presented in Tables 4 – 7 . Multivariate analysis revealed that azole administration significantly influenced venetoclax concentrations (C 0 , C 0 /D, and C 6 /D) (all p < 0.05), highlighting the clinically important drug-drug interaction between azoles and venetoclax. These findings underscore the necessity for heightened vigilance regarding antifungal coadministration during venetoclax therapy, as it may lead to interpatient variability in treatment efficacy and safety outcomes. Table 4 Multiple Linear Regression Analysis of Factors Influencing Venetoclax C 0 . Independent Variables B Std.Error Beta T P value Age 17.202 6.495 0.241 2.648 0.009 Azoles −549.933 240.748 −0.207 −2.284 0.025 Transplantation −4749.779 1136.368 −0.384 −4.180 T (rs1128503) Genotype −823.986 286.064 −0.459 −2.880 0.007 Table 6 Multiple Linear Regression Analysis of Factors Influencing Venetoclax C 0 /D. Independent Variables B Std.Error Beta T P value Weight −0.158 0.084 −0.154 −1.870 0.014 Azoles −8.949 2.085 −0.357 −4.293 0.025 Transplantion −48.564 9.560 −0.422 −5.080 0.001 Table 7 Multiple Linear Regression Analysis of Factors Influencing Venetoclax C 6 /D. Independent Variables B Std.Error Beta T P value Azoles -8.274 3.577 −0.379 −2.313 0.028 DD2 0.638 0.311 0.335 2.049 0.050 DD2: D-dimer. Association between FN and plasma venetoclax concentration Among the 123 enrolled patients, FN occurred in 88 cases, but not in the remaining 35 cases. The median concentrations in the FN group were as follows: C 0 at 1519.55 (257.67–4808.41) ng/mL, C 6 at 2834.50 (1126.14–6850.88) ng/mL, C 0 /D at 12.68 (0.30–48.08) ng/mL per mg, and C 6 /D at 17.15 (1.01–68.51) ng/mL per mg. In contrast, non-FN patients showed median of 1400.32 (547.73–5477.95) ng/mL for C 0 , 2337.74 (1028.39–4912.70) ng/mL for C 6 , 3.68 (1.36–44.30) ng/mL per mg for C 0 /D, and 7.27 (2.57–48.62) ng/mL per mg for C 6 /D. Details are presented in Fig. 1 . The associations between FN occurrence and various parameters (e.g., baseline characteristics, genotypes, laboratory results, and plasma drug concentrations) were analyzed using Chi-square test for categorical variables and Mann-Whitney U test for continuous variables. The analysis identified several significant risk factors for FN in AL patients: sex (x²=4.524, P = 0.033), chemotherapy regimen (x²=13.080, P = 0.011), baseline white blood cell count (z=-2.733, P = 0.006), baseline platelet count (z=-3.023, P = 0.003), C 0 concentration (z=-2.572, P = 0.010), and C 0 /D ratio (z=-2.123, P = 0.034). Details are summarized in Table 8 . Table 8 Clinical Characteristics of Patients with Febrile Neutropenia(FN) Characteristics Patients Without FN (n = 88) Patients With FN (n = 35) t/z value P value Gender (n) (%) 4.524 0.033 Male 47(53.41%) 26(74.29%) Female 41(46.59%) 9(25.71%) Age (years) (mean ± SD) 55 ± 19 53 ± 16 -0.589 0.556 Height (cm) Median (mean ± SD) 166.11 ± 7.90 167.54 ± 8.80 −1.568 0.117 Weight (kg) Median (mean ± SD) 62.79 ± 11.59 64.29 ± 10.68 -0.508 0.611 BSA (m 2 ) Median (mean ± SD) 1.66 ± 0.19 1.69 ± 0.18 -0.788 0.431 BMI (kg/m 2 ) Median (mean ± SD) 22.61 ± 2.99 23.20 ± 2.42 -0.800 0.424 Chronic disease (n,%) 2.364 0.124 With comorbidities 30(34.09%) 7(20.00%) Without comorbidities 58(65.91%) 28(80.00%) Azoles (n,%) 0.319 0.572 With combination 35(39.77%) 12(34.29%) Without combination 53(60.23%) 23(65.71%) Indication (n) (%) 5.851 0.054 AML 53(60.23%) 29(82.86%) ALL 31(35.23%) 5(14.29%) ALAL 4(4.54%) 1(2.86%) Risk stratified (n) (%) 3.668 0.3 Good 35(39.77%) 15(42.86%) Moderate 33(37.50%) 8(22.86%) Poor 19(21.59%) 12(34.28%) Unknown 1(1.14%) 0(0.00%) Transplantation (n) (%) 0.431 0.512 Transplanted 5(5.68%) 1(2.86%) Non-transplanted 83(94.32%) 34(97.14%) Chemotherapy regimens (n) (%) 13.080 0.011 Monotherapy 2(2.27%) 0(0.00%) Venetoclax and demethylation drugs 59(67.05%) 20(57.14%) Venetoclax and chemotherapeutic agents 10(11.36%) 13(37.14%) Venetoclax and antibody 9(10.23%) 1(2.86%) Venetoclax and kinase inhibitor 8(9.09%) 1(2.86%) Laboratory data Median (range) WBC (10 9 /L) 6.17(0.31,133.08) 11.78(1.12,176.73) -2.733 0.006 ANC (10 9 /L) 1.07(0.03,52.70) 0.86(0.00,17.79) -1.087 0.277 Hb (g/d) 78.00(58.00,143.00) 74.50(51.00,132.00) -0.973 0.331 PLT (10 9 /L) 53.00(9.00,537.00) 39.00(3.00,134.00) -3.023 0.003 ALT (U/L) 17.20(7.00,132.70) 24.20(8.50,114.10) -1.253 0.210 AST (U/L) 18.30(8.30,94.90) 24.90(7.00,365.000) -1.351 0.177 CREA (µmol/L) 63.00(35.00,171.00) 69.65(43.10,104.00) -1.172 0.241 TBIL (µmol/L) 8.82(4.09,21.60) 9.90(4.07,23.00) -0.064 0.949 BNP (pg/mL) 144.00(14.06,1160.00) 377.60(50.35,2565.50) -1.472 0.141 DD2 (mg/L) 1.00(0.10,11.90) 3.20(0.22,40.00) -1.592 0.111 Duration of Venetoclax therapy (days) Median (range) 1.445 0.229 ≤ 14 30(34.09%) 16(45.71%) >14 58(65.91%) 19(54.29%) ABCB1 3435C>T (rs1045642) Genotype 1.196 0.550 TT 6(11.76%) 2(13.33%) TC 20(39.22%) 8(53.33%) CC 25(49.02%) 5(33.34%) ABCB1 1236C>T (rs1128503) Genotype 1.815 0.403 TT 25(49.02%) 5(33.34%) TC 19(37.25%) 6(40.44%) CC 7(13.73%) 4(26.66%) ABCB1 2677G>T/A (rs2032582) Genotype 0.648 0.986 TT 7(13.73%) 3(20.00%) TG 14(27.45%) 4(26.67%) TA 6(11.76%) 1(6.67%) GG 13(25.49%) 4(26.67%) GA 8(15.69%) 2(13.32%) AA 3(5.88%) 1(6.67%) CYP3A5 6986A>G (rs776746) Genotype GG 29(56.86%) 10(66.67%) 2.073 0.355 AG 19(37.25%) 3(20.00%) AA 3(5.89%) 2(13.33%) CYP3A4 20230G>A (rs2242480) Genotype GG 33(64.71%) 11(73.33%) 1.308 0.520 GA 14(27.45%) 4(26.67%) AA 4(7.84%) 0(0.00%) Venetoclax trough concentration (C 0 ) (ng/mL) Median (range) 1400.32(542.73,5477.95) 1822.60(257.67,6367.88) -2.572 0.010 Venetoclax peak concentration (C 6 ) (ng/mL) Median (range) 2337.74(1028.39,4912.70) 3054.01(1126.14,6850.88) -1.010 0.313 C 0 /D Median (range) 3.68(1.36,44.30) 7.70(0.3,63.68) -2.123 0.034 C 6 /D Median (range) 7.27(2.57,48.62) 12.16(1.01,68.51) -1.623 0.105 BSA: body surface area; BMI: body mass index; AML: acute myeloid leukemia; ALL: Acute lymphoblastic leukemia; ALAL: acute leukemia of ambiguous lineage; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer. Data are median (range) or n (%). P values were obtained using univariate analysis. All data were analyzed using the Chi-square test and Mann Whitney U test. ROC Curve-determined Venetoclax Cut-off Values for FN ROC curves were plotted to determine the optimal plasma concentration thresholds of venetoclax for predicting FN. The analysis identified cut-off values of 1202.07ng/mL for C 0 and 6.85 ng/mL per mg for C 0 /D as discriminative thresholds. For C 0 , the area under the ROC curve (AUC) was 0.656 (P = 0.019). At the threshold of 1202.07 ng/mL, the sensitivity and specificity were 83.30% and 51.60%, respectively. Similarly, for C 0 /D, the AUC was 0.640 (P = 0.034). At the cutoff of 6.85 ng/mL per mg, the corresponding sensitivity was 51.90%, with a specificity of 78.30%. Details are presented in Fig. 2 . Discussion Venetoclax, the first-in-class BCL-2 inhibitor, induces apoptosis in malignant cells and shows significant efficacy across hematologic malignancies. However, dietary factors, drug interactions, and significant interindividual pharmacokinetic (PK) variability can lead to abnormal concentrations of venetoclax, impacting efficacy and safety [ 26 – 27 ]. While peak concentrations may correlate with efficacy, data linking concentrations to specific toxicities, particularly FN, are limited. Our real-world analysis of 123 AL patients identified age, body weight, DD2 level, concomitant azoles, transplantation status, and the ABCB1 1236C>T (rs1128503) TT genotype as significant PK modifiers. Critical thresholds for FN risk were C 0 > 1202.07 ng/mL and C 0 /D > 6.85 ng/mL per mg. Median C 0 (1409.88 ng/mL) and C 6 (2373.21 ng/mL) showed wider ranges than a prior report in Chinese populations [ 28 ], underscoring the impact of real-world variability (e.g., dosing, patient heterogeneity) and the importance of TDM. Concomitant potent/moderate CYP3A inhibitors such as voriconazole (4.5-9.6x AUC increase) and posaconazole (~ 2x increase) necessitate venetoclax dose reductions. Our analysis confirmed azoles significantly increased the C 0 , C 0 /D, and C 6 /D of venetoclax, supporting PK interactions, though the lack of significant effect on raw C 6 differed slightly from reports and suggests azoles may preferentially impact trough concentrations. Multiple regression revealed that older age and transplantation status independently influenced C 0 , while body weight became an additional significant predictor for C 0 /D (with older, lower-weight patients potentially at higher risk for elevated levels), possibly reflecting altered metabolic rates. Higher baseline DD2 level, indicative of hypercoagulability, was associated with lower C 6 /D, suggesting coagulation status should be evaluated before venetoclax-based regimens. A novel key finding was the significantly higher C 6 in ABCB1 1236C>T (rs1128503) TT genotype carriers, aligning with previous reports demonstrating the impact of ABCB1 1236C>T (rs1128503) on plasma concentrations of rivaroxaban and imatinib [ 29 – 30 ]. As venetoclax is a P-gp substrate/inhibitor [ 31 ] and ABCB1 1236C>T (rs1128503) is a synonymous SNP potentially affecting P-gp expression/function, this variant may alter cellular efflux and bioavailability. Although venetoclax is nominally a P-gp substrate, its predominant CYP3A4-mediated hepatic metabolism [ 32 ]. This metabolic dominance likely attenuates ABCB1 genotype effects conpaed to classical P-gp-dependent drugs. This metabolic divergence may contribute to the observed rs1128503 TT -associated concentration elevation. Although CYP3A5 6986A>G (rs776746) GG has been linked to higher venetoclax concentrations [ 33 ], it was not a significant predictor here. This discrepancy may stem from differences in patient cohorts, including sample size (55 vs. 66 patients undergoing SNP analysis) and population characteristics, which could affect genetic associations with drug metabolism. Studies specifically evaluating venetoclax-associated FN prevalence factors are scarce, and shorter venetoclax cycles may reduce risk [ 34 ]. We identified male gender, concomitant hypomethylating agents, higher baseline WBC, lower baseline platelets, C 0 and C 0 /D as risk factors of FN, highlighting the relevance of trough-level monitoring. The wide concentration distributions in FN patients underscore interpatient variability. The significant association between higher C 0 and C 0 /D levels with increased FN risk reveals C0 > 1202.07 ng/mL or C0/D > 6.85 ng/mL per mg as critical thresholds meriting vigilant concentration monitoring to prevent severe FN and treatment disruption. Conclusions Our study demonstrates substantial interindividual variability in venetoclax concentrations among AL patients, owing to influence by diverse demographic, clinical, pharmacogenetic, and drug-drug interaction factors. Critically, we establish a significant association between trough venetoclax concentrations (C 0 and C 0 /D) and the incidence of FN, identifying predictive thresholds. This association underscores the value of TDM in clinical practice to optimize safety. However, there are two limitations. 1) The limited sample size may restrict the identification of complex factors beyond the primary predictors revealed in multivariate analysis, despite numerous univariate associations (e.g., baseline platelets/ANC). 2) The moderate discriminative power of the PK thresholds for FN prediction may relate to untimely blood sampling relative to FN onset. Further large-scale, multi-center validation is warranted. Declarations Conflict of interest disclosure All authors disclosed no relevant relationships. Competing interests The authors declare no competing interests. Funding statement This work was supported by the Establishment of molecularly targeted drug adverse reactions based on artificial intelligence technology [grant numbers JSHD2021004] and the Outstanding Young and Middle-aged Talents Support Program of the First Affiliated Hospital with Nanjing Medical University (Jiangsu Province Hospital) [grant numbers YNRCQN025]. Author Contribution Yuting Yan: conceived the article concept; drafted, critically revised, and approved the manuscript. Jin Sun: performed the data analysis; drafted, critically revised, and approved the manuscript. Yujiao Guo and Jieyu Sun: critically revised, and approved the manuscript. Yu Zhu, Luning Sun and Yongqing Wang: critically revised and approved the manuscript. All authors contributed to manuscript development and provided their approval to submit. Data Availability The data supporting the findings of this study are available from the corresponding author upon reasonable request. References Rose-Inman H, Kuehl D (2017) Acute Leukemia[J]. Hematol Oncol Clin North Am 31(6):1011–1028 Alexander TB, Orgel E (2021) Mixed Phenotype Acute Leukemia: Current Approaches to Diagnosis and Treatment[J]. Curr Oncol Rep 23(2):22 Thol F, Döhner H, Ganser A (2024) How I treat refractory and relapsed acute myeloid leukemia[J]. Blood 143(1):11–20 Goulart H, Kantarjian H, Pemmaraju N et al (2025) Venetoclax-Based Combination Regimens in Acute Myeloid Leukemia[J]. Blood Cancer Discov 6(1):23–37 Pollyea DA, Pratz K, Letai A et al (2021) Venetoclax with azacitidine or decitabine in patients with newly diagnosed acute myeloid leukemia: long term follow⁃up from a phase 1b study[J]. Am J Hematol 96(2):208–217 Wei AH, Strickland SA Jr, Hou JZ et al (2019) Venetoclax combined with low⁃dose cytarabine for previously untreated patients with acute myeloid leukemia: results from a phase Ⅰ b/Ⅱ study[J]. J Clin Oncol 37(15):1277–1284 Wei AH, Montesinos P, Ivanov V et al (2020) Venetoclax plus LDAC for newly diagnosed AML ineligible for intensive chemotherapy: a phase 3 randomized placebo-controlled trial[J]. Blood 135(24):2137–2145 Salem AH, Agarwal SK, Dunbar M et al (2016) Effect of low and high-fat meals on the pharmacokinetics of venetoclax, a selective first-in-class BCL-2 inhibitor[J]. J Clin Pharmacol 56(11):1355–1361 Cheung TT, Salem AH, Menon RM et al (2018) Pharmacokinetics of the BCL-2 Inhibitor Venetoclax in Healthy Chinese Subjects[J]. Clin Pharmacol Drug Dev 7(4):435–440 1000 Genomes Project Consortium, Auton A, Brooks LD et al (2015) A global reference for human genetic variation[J]. Nature 526(7571):68–74 Dong J, Liu SB, Rasheduzzaman JM et al (2022) Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax[J]. Pharm Res 39(8):1921–1933 Mukherjee D, Brackman DJ, Suleiman AA et al (2023) Impact of Multiple Concomitant CYP3A Inhibitors on Venetoclax Pharmacokinetics: A PBPK and Population PK-Informed Analysis[J]. J Clin Pharmacol 63(1):119–125 Chiney MS, Menon RM, Bueno OF et al (2018) Clinical evaluation of P-glycoprotein inhibition by venetoclax: a drug interaction study with digoxin[J]. Xenobiotica 48(9):904–910 Rajme-López S, Tello-Mercado AC, Ortíz-Brizuela E et al (2024) Clinical and Microbiological Characteristics of Febrile Neutropenia During Induction Chemotherapy in Adults With Acute Leukemia[J]. Cancer Rep (Hoboken) 7(8):e2129 Pal KV, Othus M, Ali Z et al (2024) Identification of factors predicting low-risk febrile neutropenia admissions in adults with acute myeloid leukemia[J]. Blood Adv 8(24):6161–6170 Matsuda K, Yoshida T, Sugimoto K (2023) High susceptibility of febrile neutropenia in Japanese patients receiving venetoclax plus azacitidine therapy for acute myeloid leukemia[J]. Ann Hematol 102(4):971–972 Kobayashi T, Sato H, Miura M et al (2024) Overexposure to venetoclax is associated with prolonged-duration of neutropenia during venetoclax and azacitidine therapy in Japanese patients with acute myeloid leukemia[J]. Cancer Chemother Pharmacol 94(2):285–296 Kt MF, Semwal M, Yoosuf BT et al (2024) Venetoclax adverse event monitoring: a safety meta-analysis of randomized controlled trials and a retrospective evaluation of the FAERS [J]. Ann Hematol 103(8):3179–3191 DiNardo CD, Lachowiez CA, Takahashi K et al (2021) Venetoclax Combined With FLAG-IDA Induction and Consolidation in Newly Diagnosed and Relapsed or Refractory Acute Myeloid Leukemia[J]. J Clin Oncol 39(25):2768–2778 Wen X, Lu Y, Li Y et al (2024) Remission rate, toxicity and pharmacokinetics of venetoclax-based induction regimens in untreated pediatric acute myeloid leukemia[J]. NPJ Precis Oncol 8(1):248 Davids MS, Roberts AW, Seymour JF et al (2017) Phase I First-in-Human Study of Venetoclax in Patients With Relapsed or Refractory Non-Hodgkin Lymphoma[J]. J Clin Oncol 35(8):826–833 Yang YL, Qian ZY, Zhao Y et al (2023) LC-MS/MS methods for determination of venetoclax in human plasma and cerebrospinal fluid[J]. Biomed Chromatogr 37(12):e5738 Hu GX, Dai DP, Wang H et al (2017) Systematic screening for CYP3A4 genetic polymorphisms in a Han Chinese population[J]. Pharmacogenomics 18(4):369–379 Park HJ, Shinn HK, Ryu SH et al (2007) Genetic polymorphisms in the ABCB1 gene and the effects of fentanyl in Koreans[J]. Clin Pharmacol Ther 81(4):539–546 Boccia R, Glaspy J, Crawford J et al (2022) Chemotherapy-Induced Neutropenia and Febrile Neutropenia in the US: A Beast of Burden That Needs to Be Tamed?[J]. Oncologist 27(8):625–636 Hagihara M, Yasu T, Gando Y et al (2024) Increased trough concentration of venetoclax when combined with itraconazole for acute myeloid leukemia[J]. Ann Hematol 103(11):4497–4502 Wang L, Gao L, Liang Z et al (2024) Efficacy and safety of coadministration of venetoclax and anti-fungal agents under therapeutic drug monitor in unfit acute myeloid leukemia and high-risk myelodysplastic syndrome with neutropenia: a single-center retrospective study. Leuk Lymphoma 65(3):353–362 Yang X, Mei C, He X et al (2022) Quantification of Venetoclax for Therapeutic Drug Monitoring in Chinese Acute Myeloid Leukemia Patients by a Validated UPLC-MS/MS Method [J]. Molecules 27(5):1607 Wang Y, Chen M, Chen H et al (2021) Influence of ABCB1 Gene Polymorphism on Rivaroxaban Blood Concentration and Hemorrhagic Events in Patients With Atrial Fibrillation[J]. Front Pharmacol 12:639854 Cheng F, Cui Z, Li Q et al (2024) Influence of genetic polymorphisms on imatinib concentration and therapeutic response in patients with chronic-phase chronic myeloid leukemia[J]. Int Immunopharmacol 133:112090 Liu H, Michmerhuizen MJ, Lao Y et al (2017) Metabolism and Disposition of a Novel B-Cell Lymphoma-2 Inhibitor Venetoclax in Human and Characterization of Its Unusual Metabolites [J]. Drug Metab Dispos 45:294–305 Megías-Vericat JE, Solana-Altabella A, Ballesta-López O et al (2020) Drug-drug interactions of newly approved small molecule inhibitors for acute myeloid leukemia[J]. Ann Hematol 99(9):1989–2007 Li Y, Wan Q, Wan J et al (2024) Plasma concentrations of venetoclax and Pharmacogenetics correlated with drug efficacy in treatment naive leukemia patients: a retrospective study [J]. Pharmacogenomics J 24(6):37 Aiba M, Shigematsu A, Suzuki T et al (2023) Shorter duration of venetoclax administration to 14 days has same efficacy and better safety profile in treatment of acute myeloid leukemia [J]. Ann Hematol 102(3):541–546 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Apr, 2026 Reviews received at journal 31 Dec, 2025 Reviewers agreed at journal 20 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 10 Dec, 2025 First submitted to journal 01 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8247030","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562072720,"identity":"68320d12-9108-446b-b33f-ab87337a7998","order_by":0,"name":"Yuting yan","email":"","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"yan","suffix":""},{"id":562072721,"identity":"80cc48a6-fa9a-4684-9dd7-737cadd44130","order_by":1,"name":"Jin Sun","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Sun","suffix":""},{"id":562072726,"identity":"28f5d902-bcde-4c1b-8189-3a00fbd2d243","order_by":2,"name":"Yujiao Guo","email":"","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujiao","middleName":"","lastName":"Guo","suffix":""},{"id":562072727,"identity":"20fde581-f8c4-48a0-ba84-004d30f28392","order_by":3,"name":"Jieyu Sun","email":"","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jieyu","middleName":"","lastName":"Sun","suffix":""},{"id":562072728,"identity":"28caf909-8ad8-49ce-b779-d4e2157b1a39","order_by":4,"name":"Yu Zhu","email":"","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhu","suffix":""},{"id":562072729,"identity":"45405d48-2410-47d3-ba08-2c7a890ef08c","order_by":5,"name":"Luning Sun","email":"","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Luning","middleName":"","lastName":"Sun","suffix":""},{"id":562072733,"identity":"04bcbe92-31fe-46b0-958a-c60548def0cb","order_by":6,"name":"Yongqing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYJACAwaGA3Js7O0HSNNizMdzJoEkiw4kzpNwMCDShhvJGwp/7riT3ibBkMDwo2IbMVrSCox5zzzLbZNuPMDYc+Y2YS1mN3IMjBnbDue2yRxIYGZsI1KL4c+2w+lsEgkGxGsx4G07nEC8Fvszz0B+OWzYBgzkg0T5RbI9eZvhzx2H5eXb2w8++FFBhBYGgQQzA8YGCPsAEeqBgP+A8QOYllEwCkbBKBgFWAEASRdCIgPmaMMAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital With Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yongqing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-12-01 07:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8247030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8247030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98764036,"identity":"76d17ed9-d242-47d2-b2d2-117e31237e5d","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"jpg","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24045,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2ROC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/ce722f0a9c92a1dcd2296f2b.jpg"},{"id":98764039,"identity":"5d6d6df2-18d5-4e97-a5c1-3464bfa0f46c","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":680208,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1A.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/2ed0f7c0659989f78d551044.tif"},{"id":98764041,"identity":"393dc6b3-15a3-432a-9cce-bc2ddbdb3786","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":144808,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/9ec46821a51d699b9a52f240.docx"},{"id":98764044,"identity":"053ca684-1933-4a5c-968e-fcdbe763da0c","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":677266,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1B.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/584ddd1bde76bb7a40232078.tif"},{"id":98779444,"identity":"5cfc400e-2f0a-480b-b34f-b6ff321f5d58","added_by":"auto","created_at":"2025-12-22 12:30:21","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":50034,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/ceb9a4bb29cad204dca18346.docx"},{"id":98764053,"identity":"6a437836-1aa1-40ca-9944-2cb673f659a5","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":653874,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1C.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/290382ab20d26ae1445073b8.tif"},{"id":98764047,"identity":"d875a711-cc8d-482f-a07d-f6a6cb289bee","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":660172,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1D.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/83d0fcc1dd48d6940c96023a.tif"},{"id":98764045,"identity":"f78cce4e-6a2e-43d5-8d19-3a94e42447c6","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":556880,"visible":true,"origin":"","legend":"","description":"","filename":"Figures.docx","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/a8b4b7131ef38eb61b43c1b7.docx"},{"id":98764040,"identity":"9b4e754e-2f42-4437-8af4-a2d5718addd5","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"json","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8470,"visible":true,"origin":"","legend":"","description":"","filename":"c816e949b5cc46808599f7a4bdd3d939.json","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/d959327bb6f07a578009ad4a.json"},{"id":98780749,"identity":"0ca1191d-4dca-4c11-af55-75649d426788","added_by":"auto","created_at":"2025-12-22 12:31:37","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172065,"visible":true,"origin":"","legend":"","description":"","filename":"c816e949b5cc46808599f7a4bdd3d9391enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/8b1c5e6b7b37ffaeef7b51a9.xml"},{"id":98764043,"identity":"01c464e4-2107-4385-834a-073061bf56bb","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"jpg","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24045,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2ROC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/1c95f599c7c520725ce0ee19.jpg"},{"id":98778771,"identity":"09802188-b2cd-4d68-9bd7-89f3ba5a8a91","added_by":"auto","created_at":"2025-12-22 12:29:38","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":680208,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1A.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/cb06002f8aa0398219d4d48b.tif"},{"id":98780743,"identity":"7065e50e-6afc-4b1a-8983-a1d2930445fe","added_by":"auto","created_at":"2025-12-22 12:31:37","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":677266,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1B.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/356048556483a44ba710e26d.tif"},{"id":98764056,"identity":"8b77dc3f-e2b1-497f-b3a4-af16e91b83e7","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":653874,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1C.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/5fd77f5358b9ed12d6cd4ae7.tif"},{"id":98780780,"identity":"eafcbd1b-8f97-4461-bba1-343cdaa6e61e","added_by":"auto","created_at":"2025-12-22 12:31:39","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":660172,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1D.tif","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/1f312aec0a3f211c46034194.tif"},{"id":98780340,"identity":"44aa7d63-8d55-47e4-874d-c5323ed6b0e8","added_by":"auto","created_at":"2025-12-22 12:31:14","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":125019,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/0554741c7358d75aa76968aa.png"},{"id":98780250,"identity":"8ceb2180-5a1b-409e-a16a-11f01626afd9","added_by":"auto","created_at":"2025-12-22 12:31:11","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11414,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/da53b9759780f797a2a90129.png"},{"id":98779589,"identity":"9805f54e-7eb6-4155-a195-13fe9778b968","added_by":"auto","created_at":"2025-12-22 12:30:29","extension":"jpeg","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":680208,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1A.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/5ef70dc2a479716b81342b73.jpeg"},{"id":98764055,"identity":"268bd96c-281a-477a-b28e-e51a2208681d","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"jpeg","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":677266,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1B.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/0ec32b61b52c9169cd7fd68a.jpeg"},{"id":98779949,"identity":"052a0625-47b5-49cc-820a-a685de9fb7c1","added_by":"auto","created_at":"2025-12-22 12:30:57","extension":"jpeg","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":653874,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1C.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/433780dd9edb0230435410e3.jpeg"},{"id":98779622,"identity":"7db3243d-4737-415e-84b7-7d0947064b8c","added_by":"auto","created_at":"2025-12-22 12:30:33","extension":"jpeg","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":660172,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1D.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/dc4e39354851edc8c4483124.jpeg"},{"id":98778829,"identity":"9f5600a5-f6f4-4afd-9e6b-b37914e1bbb1","added_by":"auto","created_at":"2025-12-22 12:29:44","extension":"png","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11414,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/7ac69b04c604ee20f4957b68.png"},{"id":98764050,"identity":"c908774b-30ad-4e06-9065-8edb7c53c829","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7636,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2ROC.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/68f3f32a804274c8a7685e74.png"},{"id":98764059,"identity":"4ed83921-6b57-419d-b95f-55ae8fb5fd4d","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69300,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1A.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/9cdc6666e6876d073f4c87ea.png"},{"id":98778870,"identity":"5f403288-9976-4550-9e61-55a2c7c5a6eb","added_by":"auto","created_at":"2025-12-22 12:29:46","extension":"png","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68255,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1B.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/53e0577ae7fc3d4aea9a03ca.png"},{"id":98779195,"identity":"e4a411c2-5d18-4c42-b252-fc884ee2b914","added_by":"auto","created_at":"2025-12-22 12:30:04","extension":"png","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61866,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1C.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/3e93fe2391baaa57ecece3f4.png"},{"id":98778753,"identity":"09c057b5-78c9-4a97-8453-d518e4afeab9","added_by":"auto","created_at":"2025-12-22 12:29:38","extension":"png","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65706,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1D.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/19209b9f8e6b89b628512698.png"},{"id":98764065,"identity":"50f2b5b8-dad6-4dc0-a1e9-208767039003","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":30422,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/0998890716279ebc22b06e2e.png"},{"id":98764070,"identity":"4eb60247-c329-4451-97fe-da28ce9cbcdc","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6101,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/1278ea4a707bd7da57187a87.png"},{"id":98779633,"identity":"928a567e-f4de-4f73-8cfd-21238c1efe41","added_by":"auto","created_at":"2025-12-22 12:30:34","extension":"png","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69300,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1A.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/157ab204a24ab7b737478822.png"},{"id":98779634,"identity":"6d7a5575-bcd0-476f-8a7b-f6dc39a58a56","added_by":"auto","created_at":"2025-12-22 12:30:34","extension":"png","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68255,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1B.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/340383173c21854046135b7d.png"},{"id":98764069,"identity":"3cecb5b2-c8e8-46cb-ad29-2f1d2a5a95bd","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61866,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/5cb720266d185510114adbae.png"},{"id":98764063,"identity":"c1de9ad7-08bc-4c2e-901d-a8df287a6da1","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":32,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":65706,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1D.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/5da39b0177e71e7c7da89538.png"},{"id":98764067,"identity":"08f05fb8-8894-4e68-a778-5cda9e1bead6","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"png","order_by":33,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6101,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/5ab105e7dfb44cf4fecda847.png"},{"id":98764074,"identity":"e627679c-a552-4581-be48-6fd36fd9f5f5","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"xml","order_by":34,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":172606,"visible":true,"origin":"","legend":"","description":"","filename":"c816e949b5cc46808599f7a4bdd3d9391structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/807ad89da63e3ee941b5ccf4.xml"},{"id":98764073,"identity":"9be5ed42-8485-471a-bc50-a69e8fb90228","added_by":"auto","created_at":"2025-12-22 10:07:50","extension":"html","order_by":35,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187997,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/3eb37e8a94fb09b1082b12e8.html"},{"id":98764037,"identity":"248738c9-c04e-4a1a-8168-ecbd12fa4d7b","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":209052,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of febrile neutropenia (FN) patients across different venetoclax plasma concentration. (\u003cstrong\u003eA\u003c/strong\u003e) Distribution of FN and non-FN patients by venetoclax C\u003csub\u003e0\u003c/sub\u003e. (\u003cstrong\u003eB\u003c/strong\u003e) Distribution of FN and non-FN patients by venetoclax C\u003csub\u003e6\u003c/sub\u003e. (\u003cstrong\u003eC\u003c/strong\u003e) Distribution of FN and non-FN patients by venetoclax C\u003csub\u003e0\u003c/sub\u003e/D. (\u003cstrong\u003eD\u003c/strong\u003e) Distribution of FN and non-FN patients by venetoclax C\u003csub\u003e6\u003c/sub\u003e/D. *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ns, p \u0026gt; 0.05.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/78d028dde79d1a745e667a0c.jpg"},{"id":98764038,"identity":"91341700-bd15-44c8-9216-170620dc11bd","added_by":"auto","created_at":"2025-12-22 10:07:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24045,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis of venetoclax C\u003csub\u003e0\u003c/sub\u003e and C\u003csub\u003e0\u003c/sub\u003e/D for predicting febrile neutropenia. (C0: AUC=0.656, P=0.019; C0/D: AUC=0.640, P=0.034)\u003c/p\u003e","description":"","filename":"Figure2ROC.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/d873d0814de249ffbdaea4a4.jpg"},{"id":98783352,"identity":"57fd1350-5648-46c0-9c66-9c425d46b46b","added_by":"auto","created_at":"2025-12-22 12:41:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1935615,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8247030/v1/99186637-6042-4820-a90b-fce10aa00101.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Venetoclax Concentration and Febrile Neutropenia in Acute Leukemia: A Retrospective Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute leukemia (AL) is a malignant clonal hematopoietic stem cell disorder characterized by excessive proliferation of blasts and immature cells in the bone marrow or peripheral blood as well as widespread into lymph nodes, liver, spleen, and other organs. Clinically, it manifests as suppressed normal hematopoiesis, infections, hemorrhage, and overall poor prognosis. Currently, AL is classified by bone marrow cytomorphology into acute myeloid leukemia (AML), acute lymphoblastic leukemia, and acute leukemia of ambiguous lineage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Conventional therapeutic approaches for AL include intensive chemotherapy induction followed by consolidation therapy or allogeneic hematopoietic stem cell transplantation to eliminate minimal residual disease. Due to the increasing molecular heterogeneity among patients, however, these approaches no longer meet the current clinical demands, prompting the development of novel therapeutic strategies, particularly small-molecule targeted agents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVenetoclax is a highly selective and potent small-molecule kinase inhibitor targeting BCL-2, and has demonstrated remarkable efficacy and manageable safety in various hematologic malignancies, especially AL [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Current clinical guidelines recommend its use not only in adults but also extend to pediatric and infant populations. Pharmacokinetic studies on venetoclax reveal significant interindividual variability in pharmacokinetic parameters under different dietary conditions, ethnic backgrounds, or concomitant medications [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Venetoclax, a P-glycoprotein (P-gp) substrate metabolized via CYP3A4/5, demonstrates marked interindividual exposure variation, largely driven by SNPs in P-gp and CYP3A4/5 genes. Notably, ethnically stratified CYP3A4/5 allele frequencies correlate with elevated plasma concentrations in Asian populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], implicating pharmacogenetic influences in population-specific pharmacokinetics of venetoclax..In addition, venetoclax exhibits clinically relevant drug-drug interactions with voriconazole, posaconazole, digoxin, and other agents, potentially altering its plasma concentration and affecting therapeutic outcomes and safety [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Consequently, therapeutic drug monitoring (TDM) of venetoclax is crucial for optimizing clinical decision-making.\u003c/p\u003e \u003cp\u003eFebrile neutropenia (FN), defined as severe neutropenia accompanied by fever, is associated with the myelotoxicity of specific chemotherapeutic agents, dose intensity, patient-specific factors, and concomitant medications. FN develops in more than 80% of hematologic patients following the first cycle of chemotherapy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, 15\u0026ndash;30% of cases remain without identifiable pathogens despite comprehensive diagnostic evaluation, and mortality rates remain substantial [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Consequently, FN represents a critical complication that requires vigilant management during treatment for hematologic malignancies. The current evidence suggests that venetoclax administration represents one of the high-risk factors for FN development [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Real-world data indicate that the incidence of grade\u0026thinsp;\u0026ge;\u0026thinsp;3 venetoclax-associated FN may reach 50%, and some patients even require treatment discontinuation due to FN [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The occurrence of this event may be attributed to the potent myelosuppressive effects of venetoclax. Thus, effective management of venetoclax-related adverse events is critical for ensuring long-term treatment benefits. However, no study has yet explored the specific plasma venetoclax concentration threshold that leads to the occurrence of FN.\u003c/p\u003e \u003cp\u003eThis real-world study is aimed to measure trough and peak concentrations of venetoclax in AL patients and analyze the influencing factors of its pharmacokinetics as well as the association between drug concentration and FN. The findings are expected to provide evidence for personalized venetoclax dosing strategies, thereby enhancing treatment safety in AL patients.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eTotally 123 AL adult patients who received venetoclax therapy at the First Affiliated Hospital of Nanjing Medical University between August 2023 and March 2025 were enrolled. Inclusion criteria comprised: (1) confirmed AL diagnosis via bone marrow cytomorphology, molecular biology, cytogenetics, and immunophenotyping; (2) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (3) Eastern Cooperative Oncology Group performance status 0\u0026ndash;2; (4) good treatment compliance; (5) no prior venetoclax treatment; (6) continuous venetoclax administration for \u0026ge;\u0026thinsp;7 days before blood sampling for drug concentration analysis; (7) availability of pharmacogenetic data, including \u003cem\u003eABCB1 3435C\u0026gt;T (rs1045642)\u003c/em\u003e, \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503)\u003c/em\u003e, \u003cem\u003eABCB1 2677G\u0026gt;T/A (rs2032582)\u003c/em\u003e, \u003cem\u003eCYP3A5 6986A\u0026gt;G (rs776746)\u003c/em\u003e, and \u003cem\u003eCYP3A4 20230G\u0026gt;A (rs2242480)\u003c/em\u003e genetypes. Exclusion criteria consisted of (1) missing venetoclax concentration assessments; (2) pretreatment adverse drug reactions; (3) poor adherence; (4) incomplete medical records. This study was approved by the Ethics Committee of The First Affiliated Hospital with Nanjing Medical University (Approval Code: 2025-SR-244), and written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eBaseline demographic and clinical characteristics were recorded, including sex, age, weight, height, body mass index (BMI), and body surface area (BSA). Laboratory parameters including white blood cell count (WBC), absolute neutrophil count (ANC), hemoglobin (Hb), platelet count (PLT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (CREA), total bilirubin (TBIL), brain natriuretic peptide (BNP), and D-dimer (DD2) were monitored, and creatinine clearance (CrCl) was calculated using the Cockcroft-Gault equation. Comorbidities, concomitant azole antifungal use, disease classification, transplant status, and chemotherapy regimens were documented. Patients were risk-stratified according to the 2022 European LeukemiaNet (ELN) criteria based on cytogenetic and molecular profiling. All clinical data were extracted from electronic medical records.\u003c/p\u003e\n\u003ch3\u003eQuantification of Venetoclax by high-performance liquid chromatography - tandem mass spectrometry (HPLC-MS/MS)\u003c/h3\u003e\n\u003cp\u003eTrough (C\u003csub\u003e0\u003c/sub\u003e) and peak (C\u003csub\u003e6\u003c/sub\u003e) concentrations of venetoclax were measured after \u0026ge;\u0026thinsp;5 consecutive days of treatment to ensure steady-state exposure [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Peripheral venous blood samples were collected in K2EDTA-anticoagulated heparinized vacuum tubes and then centrifuged at 4 ℃ and 4000 rpm for 8 minutes to obtain plasma samples.\u003c/p\u003e \u003cp\u003eVenetoclax concentrations were determined via HPLC-MS/MS using [2H8]-venetoclax (venetoclax-d8) as the internal standard (IS). Chromatographic separation was achieved on an ACQUITY UPLC HSS T3 column (2.1\u0026times;50 mm\u003csup\u003e2\u003c/sup\u003e, 1.8 \u0026micro;m) with gradient elution (mobile phase: 10 mM ammonium formate with 0.1% formic acid and acetonitrile, 40:60 v/v). Analytes were quantified in the multiple reaction monitoring (MRM) mode using a SCIEX Triple Quad\u0026trade; 5500\u0026thinsp;+\u0026thinsp;mass spectrometer and by monitoring mass transitions of m/z 868.3\u0026rarr;636.3 (venetoclax) and m/z 876.3\u0026rarr;644.3 (IS). This method demonstrated a linear dynamic range of 20.0-5000 ng/mL, with intra- and inter-assay precision (relative standard deviation, RSD)\u0026thinsp;\u0026lt;\u0026thinsp;13.6% and accuracy within \u0026plusmn;\u0026thinsp;11.9%. Recovery rates approximated 100% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To account for interindividual dose variability, dose-adjusted venetoclax plasma concentrations were calculated as concentration per dose and denoted as C\u003csub\u003e0\u003c/sub\u003e/D for trough and C\u003csub\u003e6\u003c/sub\u003e/D for peak., which enabled comparative pharmacokinetic analysis across subjects.\u003c/p\u003e\n\u003ch3\u003ePharmacogenomic Testing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was extracted from peripheral venous blood clots, which had been stored at -80℃, using the DP335-02 DNA extraction kit (KeyGEN BioTECH, Jiangsu, China) according to the manufacturer's protocol. Then variants \u003cem\u003eABCB1 3435C\u0026gt;T (rs1045642)\u003c/em\u003e, \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503)\u003c/em\u003e, \u003cem\u003eABCB1 2677G\u0026gt;T/A (rs2032582)\u003c/em\u003e, \u003cem\u003eCYP3A5 6986A\u0026gt;G (rs776746)\u003c/em\u003e, and \u003cem\u003eCYP3A4 20230G\u0026gt;A (rs2242480)\u003c/em\u003e were genotyped. These single-nucleotide polymorphisms (SNPs) exhibit relatively high allele frequencies in Asian populations [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The analysis utilized a MassARRAY SNP genotyping platform (Agena Bioscience), which is based on matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry (MALDI-TOF MS).\u003c/p\u003e\n\u003ch3\u003eEvaluation of FN\u003c/h3\u003e\n\u003cp\u003ePrior to venetoclax initiation, all enrolled patients underwent comprehensive physical examinations and laboratory assessments, including cardiac function, hematologic, and biochemical profiling. Throughout the treatment period, daily temperatures were monitored with hematologic and biochemical evaluations performed every other day. FN was defined as an adverse event occurring post-venetoclax administration characterized by neutropenia concurrent with fever, by strictly adhering to the Common Terminology Criteria for Adverse Events (CTCAE 5.0) issued by the U.S. Department of Health and Human Services (2017). The diagnostic criteria required: 1) ANC\u0026thinsp;\u0026lt;\u0026thinsp;1,000/mm\u0026sup3;, concurrent with 2) either a single temperature\u0026thinsp;\u0026gt;\u0026thinsp;38.3℃(101℉) or sustained temperature\u0026thinsp;\u0026ge;\u0026thinsp;38℃ (100.4℉) for \u0026gt;\u0026thinsp;1 hour [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics summarized baseline characteristics and event data. Normally distributed continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (X\u0026thinsp;\u0026plusmn;\u0026thinsp;S); non-normal data were shown as median (min-max). Categorical variables were presented as percentages (%). Hardy\u0026ndash;Weinberg equilibrium was tested for each polymorphic locus. Associations between venetoclax concentrations (C\u003csub\u003e0\u003c/sub\u003e, C\u003csub\u003e0\u003c/sub\u003e/D, C\u003csub\u003e6\u003c/sub\u003e, C\u003csub\u003e6\u003c/sub\u003e/D) and categorical variables (e.g., gender, prognostic group, pharmacogenotypes) were analyzed using univariate analyses (Spearman rank correlation, Mann-Whitney U test, Kruskal-Wallis test for non-normal data; Student's t-test, ANOVA for normal data with homogeneity of variance) and multiple linear regression analyses. Continuous variable associations were assessed via Spearman correlation. Receiver\u0026rsquo;s operating characteristic (ROC) curves were plotted to define the cut-off values for predicting FN (maximizing Youden's index). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were finished on SPSS Statistics 27.0 and GraphPad Prism 8.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical Characteristics\u003c/h2\u003e \u003cp\u003eThis study enrolled a total of 123 patients comprising 73 males and 50 females, with a median age of 55 years [interquartile range (IQR): 28]. A total of 66 patients were included in the pharmacogenomic analysis. All patients received venetoclax-based chemotherapy regimens and underwent TDM. A total of 98 trough-concentration and 101 peak-concentration samples were collected. The median concentrations of the included patients were as follows: C\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1409.88 (IQR: 1317.95) ng/mL; C\u003csub\u003e6\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2373.21 (IQR: 2047.76) ng/mL; C\u003csub\u003e0\u003c/sub\u003e/D\u0026thinsp;=\u0026thinsp;4.02 (IQR: 10.00) ng/mL per mg, and C\u003csub\u003e6\u003c/sub\u003e/D\u0026thinsp;=\u0026thinsp;7.62 (IQR: 13.82) ng/mL per mg. The detailed baseline characteristics of the 123 patients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics of Enrolled Patients (n\u0026thinsp;=\u0026thinsp;123)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eClinical Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eGender (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(59.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(40.65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAge (years) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eHeight (cm) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eWeight (kg) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBSA (m\u003csup\u003e2\u003c/sup\u003e) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eChronic Disease (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWith comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(30.08%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWithout comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86(69.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAzoles (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWith combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47(38.21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWithout combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76(61.79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eIndication (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(66.67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(29.27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eALAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(4.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eRisk Stratified (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(40.65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(21.95%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(36.59%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTransplantation (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTransplanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(4.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNon-transplanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e117(95.12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy Regimens (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMonotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(1.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVenetoclax and demethylation drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79(64.23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVenetoclax and chemotherapeutic agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(18.70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVenetoclax and antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(8.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eVenetoclax and kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(7.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eLaboratory Data Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.42(0.31,133.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eANC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.01(0.00,52.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHb (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80.00(51.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.00(3.00,537.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.00(7.00,132.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.30(7.00,365.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCREA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.00(35.00,171.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.90(4.07,21.60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144.00(14.06,1717.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eDD2 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.72(0.1,36.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDuration of Venetoclax Therapy (days) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46(37.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u0026gt;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77(62.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eABCB1 3435C\u0026gt;T (rs1045642) Genotype (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(6.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(22.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30(24.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(46.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eABCB1 1236C\u0026gt;T (rs1128503) Genotype (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30(24.39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(20.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(8.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(46.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eABCB1 2677G\u0026gt;T/A (rs2032582) Genotype (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(8.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(14.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(5.69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(13.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(8.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(3.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(46.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCYP3A5 6986A\u0026gt;G (rs776746) Genotype (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(31.71%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(17.88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(4.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(46.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCYP3A4 20230G\u0026gt;A (rs2242480) Genotype (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(35.77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(14.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(3.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57(46.35%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVenetoclax trough concentration (C\u003csub\u003e0\u003c/sub\u003e) (ng/mL) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1409.88(257.67,5477.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVenetoclax peak concentration (C\u003csub\u003e6\u003c/sub\u003e) (ng/mL) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2373.21(1028.39,6850.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e0\u003c/sub\u003e/D Median (ng/mL per mg) (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.02(0.3,48.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003e/D Median (ng/mL per mg) (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.62(1.01,68.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eBSA: body surface area; BMI: body mass index; AML: acute myeloid leukemia; ALL: Acute lymphoblastic leukemia; ALAL: acute leukemia of ambiguous lineage; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are median (range) or n (%).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHardy\u0026ndash;Weinberg equilibrium was assessed for each variant using an exact test in SPSS Genetics module 27.0. No significant deviation was observed (all P_HWE\u0026thinsp;\u0026ge;\u0026thinsp;0.19), indicating absence of genotyping error or population stratification. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHardy\u0026ndash;Weinberg equilibrium test for pharmacogenetic variants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlleles (major/minor)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCall rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenotype counts (observed/expected)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP_HWE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1045642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC 8/7.33; CT 28/29.33; TT 30/29.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers1128503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC 30/27.37; CT 25/30.26; TT 11/8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2032582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG+/G-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eG\u0026thinsp;+\u0026thinsp;45/43.44; G\u0026minus;21/22.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers776746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA 39/37.90; AG 22/24.20; GG 5/3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers2242480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG 44/42.55; GA 18/20.90; AA 4/2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSNP: single nucleotide polymorphism; MAF: minor allele frequency; P_HWE were obtained by Fisher exact test; G+: carriers of the 2677G allele; G-: non-carriers of the 2677G allele.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Analysis\u003c/h2\u003e \u003cp\u003eThe Spearman rank correlation analysis revealed a positive correlation between C\u003csub\u003e0\u003c/sub\u003e and age (r\u0026thinsp;=\u0026thinsp;0.249, P\u0026thinsp;=\u0026thinsp;0.014), but this association was attenuated after dose adjustment (C\u003csub\u003e0\u003c/sub\u003e/D vs. age: r\u0026thinsp;=\u0026thinsp;0.189, P\u0026thinsp;=\u0026thinsp;0.065). A negative correlation was observed between C\u003csub\u003e6\u003c/sub\u003e and baseline platelet counts (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.211, P\u0026thinsp;=\u0026thinsp;0.037), which became more pronounced following dose correction (C\u003csub\u003e6\u003c/sub\u003e/D vs. baseline platelet counts: r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.251, P\u0026thinsp;=\u0026thinsp;0.013). Additionally, C\u003csub\u003e6\u003c/sub\u003e/D was negatively correlated with baseline neutrophil counts (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.2, P\u0026thinsp;=\u0026thinsp;0.045).\u003c/p\u003e \u003cp\u003eThe Mann-Whitney U test demonstrated significant differences in both C\u003csub\u003e0\u003c/sub\u003e and C\u003csub\u003e0\u003c/sub\u003e/D among patients who had undergone hematopoietic stem cell transplantation (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.714, P\u0026thinsp;=\u0026thinsp;0.020; z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.714, P\u0026thinsp;=\u0026thinsp;0.021). Similarly, patients receiving concomitant azole therapy significantly differed in C\u003csub\u003e0\u003c/sub\u003e/D and C\u003csub\u003e6\u003c/sub\u003e/D (z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.934, P\u0026thinsp;=\u0026thinsp;0.003; z\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.728, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, C\u003csub\u003e6\u003c/sub\u003e was significantly different in patients with venetoclax treatment duration\u0026thinsp;\u0026le;\u0026thinsp;14 days (z\u0026thinsp;=\u0026thinsp;0.239, P\u0026thinsp;=\u0026thinsp;0.016). Details are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Analysis of Factors Influencing venetoclax blood concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eC\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eC5.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eC0/D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eC5.5/D\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ez/r/H value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez/r/H value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ez/r/H value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ez/r/H value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.065\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.192\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.177\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.099\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;3.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Stratified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;1.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.104\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy Regimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.063\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.059\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.179\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of Venetoclax Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.101\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCB1 3435C\u0026gt;T (rs1045642) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCB1 1236C\u0026gt;T (rs1128503) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.293\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.091\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCB1 2677G\u0026gt;T/A (rs2032582) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP3A5 6986A\u0026gt;G (rs776746) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP3A4 20230G\u0026gt;A (rs2242480) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.199\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.214\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.084\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eBSA: body surface area; BMI: body mass index; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eP values were obtained using univariate analysis. All data were analyzed using the Spearman rank correlation (r refers to Spearman's correlation coefficient), Mann-Whitney U test (z refers to the standardized test statistic) and Kruskal-Wallis test (H refers to the standardized test statistic).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultiple Linear Regression Analysis\u003c/h2\u003e \u003cp\u003eTo avoid the potential omission of confounding factors affecting plasma venetoclax concentrations, all variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.2 in univariate analysis were incorporated into multivariate linear regression models. Given the limited number of significant variables identified for C\u003csub\u003e6\u003c/sub\u003e in univariate analysis, a more inclusive threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.3 was adopted for subsequent multivariate regression. The analyses revealed four significant regression models. For C\u003csub\u003e0\u003c/sub\u003e (F\u0026thinsp;=\u0026thinsp;9.954, adjusted R\u0026sup2;=0.217, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age, concomitant azole therapy, and prior hematopoietic stem cell transplantation were involved as independent predictors. For C\u003csub\u003e6\u003c/sub\u003e (F\u0026thinsp;=\u0026thinsp;8.297, adjusted R\u0026sup2;=0.186, P\u0026thinsp;=\u0026thinsp;0.007), \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503)\u003c/em\u003e genotype emerged as the sole determinant. For C\u003csub\u003e0\u003c/sub\u003e/D (F\u0026thinsp;=\u0026thinsp;19.084, adjusted R\u0026sup2;=0.366, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), body weight, azole coadministration and prior transplantation were significant. For C\u003csub\u003e6\u003c/sub\u003e/D (F\u0026thinsp;=\u0026thinsp;4.656, adjusted R\u0026sup2;=0.196, P\u0026thinsp;=\u0026thinsp;0.018), both azoles and DD2 level demonstrated predictive values. Complete regression results are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Multivariate analysis revealed that azole administration significantly influenced venetoclax concentrations (C\u003csub\u003e0\u003c/sub\u003e, C\u003csub\u003e0\u003c/sub\u003e/D, and C\u003csub\u003e6\u003c/sub\u003e/D) (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), highlighting the clinically important drug-drug interaction between azoles and venetoclax. These findings underscore the necessity for heightened vigilance regarding antifungal coadministration during venetoclax therapy, as it may lead to interpatient variability in treatment efficacy and safety outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Analysis of Factors Influencing Venetoclax C\u003csub\u003e0\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;549.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;4749.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1136.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Analysis of Factors Influencing Venetoclax C\u003csub\u003e6\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABCB1 1236C\u0026gt;T (rs1128503) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;823.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Analysis of Factors Influencing Venetoclax C\u003csub\u003e0\u003c/sub\u003e/D.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;1.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;8.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplantion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;48.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;5.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Analysis of Factors Influencing Venetoclax C\u003csub\u003e6\u003c/sub\u003e/D.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd.Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eDD2: D-dimer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between FN and plasma venetoclax concentration\u003c/h2\u003e \u003cp\u003eAmong the 123 enrolled patients, FN occurred in 88 cases, but not in the remaining 35 cases. The median concentrations in the FN group were as follows: C\u003csub\u003e0\u003c/sub\u003e at 1519.55 (257.67\u0026ndash;4808.41) ng/mL, C\u003csub\u003e6\u003c/sub\u003e at 2834.50 (1126.14\u0026ndash;6850.88) ng/mL, C\u003csub\u003e0\u003c/sub\u003e/D at 12.68 (0.30\u0026ndash;48.08) ng/mL per mg, and C\u003csub\u003e6\u003c/sub\u003e/D at 17.15 (1.01\u0026ndash;68.51) ng/mL per mg. In contrast, non-FN patients showed median of 1400.32 (547.73\u0026ndash;5477.95) ng/mL for C\u003csub\u003e0\u003c/sub\u003e, 2337.74 (1028.39\u0026ndash;4912.70) ng/mL for C\u003csub\u003e6\u003c/sub\u003e, 3.68 (1.36\u0026ndash;44.30) ng/mL per mg for C\u003csub\u003e0\u003c/sub\u003e/D, and 7.27 (2.57\u0026ndash;48.62) ng/mL per mg for C\u003csub\u003e6\u003c/sub\u003e/D. Details are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe associations between FN occurrence and various parameters (e.g., baseline characteristics, genotypes, laboratory results, and plasma drug concentrations) were analyzed using Chi-square test for categorical variables and Mann-Whitney U test for continuous variables. The analysis identified several significant risk factors for FN in AL patients: sex (x\u0026sup2;=4.524, P\u0026thinsp;=\u0026thinsp;0.033), chemotherapy regimen (x\u0026sup2;=13.080, P\u0026thinsp;=\u0026thinsp;0.011), baseline white blood cell count (z=-2.733, P\u0026thinsp;=\u0026thinsp;0.006), baseline platelet count (z=-3.023, P\u0026thinsp;=\u0026thinsp;0.003), C\u003csub\u003e0\u003c/sub\u003e concentration (z=-2.572, P\u0026thinsp;=\u0026thinsp;0.010), and C\u003csub\u003e0\u003c/sub\u003e/D ratio (z=-2.123, P\u0026thinsp;=\u0026thinsp;0.034). Details are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical Characteristics of Patients with Febrile Neutropenia(FN)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatients Without FN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatients With FN\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et/z value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47(53.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(74.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(46.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(25.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge (years) (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHeight (cm) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166.11\u0026thinsp;\u0026plusmn;\u0026thinsp;7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e167.54\u0026thinsp;\u0026plusmn;\u0026thinsp;8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;1.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWeight (kg) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.79\u0026thinsp;\u0026plusmn;\u0026thinsp;11.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64.29\u0026thinsp;\u0026plusmn;\u0026thinsp;10.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBSA (m\u003csup\u003e2\u003c/sup\u003e) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e) Median (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChronic disease (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWith comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(34.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWithout comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(65.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(80.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAzoles (n,%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWith combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(39.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(34.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWithout combination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(60.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(65.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIndication (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(60.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(82.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(35.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eALAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(4.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRisk stratified (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(39.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(42.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(37.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(22.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(21.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(34.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(1.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTransplantation (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTransplanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(5.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNon-transplanted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83(94.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(97.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy regimens (n) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMonotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVenetoclax and demethylation drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59(67.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(57.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVenetoclax and chemotherapeutic agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(11.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(37.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVenetoclax and antibody\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(10.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eVenetoclax and kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(9.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLaboratory data Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.17(0.31,133.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.78(1.12,176.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eANC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.03,52.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86(0.00,17.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHb (g/d)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.00(58.00,143.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.50(51.00,132.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.00(9.00,537.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.00(3.00,134.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-3.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.20(7.00,132.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.20(8.50,114.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.30(8.30,94.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.90(7.00,365.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCREA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.00(35.00,171.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.65(43.10,104.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTBIL (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82(4.09,21.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.90(4.07,23.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBNP (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144.00(14.06,1160.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e377.60(50.35,2565.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eDD2 (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00(0.10,11.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.20(0.22,40.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDuration of Venetoclax therapy (days) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(34.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(45.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u0026gt;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58(65.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19(54.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eABCB1 3435C\u0026gt;T (rs1045642) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(39.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(53.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(49.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(33.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eABCB1 1236C\u0026gt;T (rs1128503) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(49.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(33.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(37.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(40.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(13.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(26.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eABCB1 2677G\u0026gt;T/A (rs2032582) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(13.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(27.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(26.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(11.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(25.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(26.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(15.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(13.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(5.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCYP3A5 6986A\u0026gt;G (rs776746) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(56.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(37.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(20.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(5.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCYP3A4 20230G\u0026gt;A (rs2242480) Genotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(64.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(73.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(27.45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(26.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(7.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVenetoclax trough concentration (C\u003csub\u003e0\u003c/sub\u003e) (ng/mL) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1400.32(542.73,5477.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1822.60(257.67,6367.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVenetoclax peak concentration (C\u003csub\u003e6\u003c/sub\u003e) (ng/mL) Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2337.74(1028.39,4912.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3054.01(1126.14,6850.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e0\u003c/sub\u003e/D Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.68(1.36,44.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.70(0.3,63.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eC\u003csub\u003e6\u003c/sub\u003e/D Median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.27(2.57,48.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.16(1.01,68.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBSA: body surface area; BMI: body mass index; AML: acute myeloid leukemia; ALL: Acute lymphoblastic leukemia; ALAL: acute leukemia of ambiguous lineage; WBC: white blood cell count; ANC: absolute neutrophil count; Hb: hemoglobin; PLT: platelet count; ALT: alanine aminotransferase; AST: aspartate aminotransferase; CREA: serum creatinine; TBIL: total bilirubin; BNP: brain natriuretic peptide; DD2: D-dimer.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are median (range) or n (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP values were obtained using univariate analysis. All data were analyzed using the Chi-square test and Mann Whitney U test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eROC Curve-determined Venetoclax Cut-off Values for FN\u003c/h2\u003e \u003cp\u003eROC curves were plotted to determine the optimal plasma concentration thresholds of venetoclax for predicting FN. The analysis identified cut-off values of 1202.07ng/mL for C\u003csub\u003e0\u003c/sub\u003e and 6.85 ng/mL per mg for C\u003csub\u003e0\u003c/sub\u003e/D as discriminative thresholds. For C\u003csub\u003e0\u003c/sub\u003e, the area under the ROC curve (AUC) was 0.656 (P\u0026thinsp;=\u0026thinsp;0.019). At the threshold of 1202.07 ng/mL, the sensitivity and specificity were 83.30% and 51.60%, respectively. Similarly, for C\u003csub\u003e0\u003c/sub\u003e/D, the AUC was 0.640 (P\u0026thinsp;=\u0026thinsp;0.034). At the cutoff of 6.85 ng/mL per mg, the corresponding sensitivity was 51.90%, with a specificity of 78.30%. Details are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eVenetoclax, the first-in-class BCL-2 inhibitor, induces apoptosis in malignant cells and shows significant efficacy across hematologic malignancies. However, dietary factors, drug interactions, and significant interindividual pharmacokinetic (PK) variability can lead to abnormal concentrations of venetoclax, impacting efficacy and safety [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While peak concentrations may correlate with efficacy, data linking concentrations to specific toxicities, particularly FN, are limited. Our real-world analysis of 123 AL patients identified age, body weight, DD2 level, concomitant azoles, transplantation status, and the \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503) TT\u003c/em\u003e genotype as significant PK modifiers. Critical thresholds for FN risk were C\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1202.07 ng/mL and C\u003csub\u003e0\u003c/sub\u003e/D\u0026thinsp;\u0026gt;\u0026thinsp;6.85 ng/mL per mg.\u003c/p\u003e \u003cp\u003eMedian C\u003csub\u003e0\u003c/sub\u003e (1409.88 ng/mL) and C\u003csub\u003e6\u003c/sub\u003e (2373.21 ng/mL) showed wider ranges than a prior report in Chinese populations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], underscoring the impact of real-world variability (e.g., dosing, patient heterogeneity) and the importance of TDM.\u003c/p\u003e \u003cp\u003eConcomitant potent/moderate CYP3A inhibitors such as voriconazole (4.5-9.6x AUC increase) and posaconazole (~\u0026thinsp;2x increase) necessitate venetoclax dose reductions. Our analysis confirmed azoles significantly increased the C\u003csub\u003e0\u003c/sub\u003e, C\u003csub\u003e0\u003c/sub\u003e/D, and C\u003csub\u003e6\u003c/sub\u003e/D of venetoclax, supporting PK interactions, though the lack of significant effect on raw C\u003csub\u003e6\u003c/sub\u003e differed slightly from reports and suggests azoles may preferentially impact trough concentrations.\u003c/p\u003e \u003cp\u003eMultiple regression revealed that older age and transplantation status independently influenced C\u003csub\u003e0\u003c/sub\u003e, while body weight became an additional significant predictor for C\u003csub\u003e0\u003c/sub\u003e/D (with older, lower-weight patients potentially at higher risk for elevated levels), possibly reflecting altered metabolic rates. Higher baseline DD2 level, indicative of hypercoagulability, was associated with lower C\u003csub\u003e6\u003c/sub\u003e/D, suggesting coagulation status should be evaluated before venetoclax-based regimens. A novel key finding was the significantly higher C\u003csub\u003e6\u003c/sub\u003e in \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503) TT\u003c/em\u003e genotype carriers, aligning with previous reports demonstrating the impact of \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503)\u003c/em\u003e on plasma concentrations of rivaroxaban and imatinib [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As venetoclax is a P-gp substrate/inhibitor [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503)\u003c/em\u003e is a synonymous SNP potentially affecting P-gp expression/function, this variant may alter cellular efflux and bioavailability. Although venetoclax is nominally a P-gp substrate, its predominant CYP3A4-mediated hepatic metabolism [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This metabolic dominance likely attenuates ABCB1 genotype effects conpaed to classical P-gp-dependent drugs. This metabolic divergence may contribute to the observed \u003cem\u003ers1128503 TT\u003c/em\u003e-associated concentration elevation. Although \u003cem\u003eCYP3A5 6986A\u0026gt;G (rs776746) GG\u003c/em\u003e has been linked to higher venetoclax concentrations [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], it was not a significant predictor here. This discrepancy may stem from differences in patient cohorts, including sample size (55 vs. 66 patients undergoing SNP analysis) and population characteristics, which could affect genetic associations with drug metabolism.\u003c/p\u003e \u003cp\u003eStudies specifically evaluating venetoclax-associated FN prevalence factors are scarce, and shorter venetoclax cycles may reduce risk [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. We identified male gender, concomitant hypomethylating agents, higher baseline WBC, lower baseline platelets, C\u003csub\u003e0\u003c/sub\u003e and C\u003csub\u003e0\u003c/sub\u003e/D as risk factors of FN, highlighting the relevance of trough-level monitoring. The wide concentration distributions in FN patients underscore interpatient variability. The significant association between higher C\u003csub\u003e0\u003c/sub\u003e and C\u003csub\u003e0\u003c/sub\u003e/D levels with increased FN risk reveals C0\u0026thinsp;\u0026gt;\u0026thinsp;1202.07 ng/mL or C0/D\u0026thinsp;\u0026gt;\u0026thinsp;6.85 ng/mL per mg as critical thresholds meriting vigilant concentration monitoring to prevent severe FN and treatment disruption.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study demonstrates substantial interindividual variability in venetoclax concentrations among AL patients, owing to influence by diverse demographic, clinical, pharmacogenetic, and drug-drug interaction factors. Critically, we establish a significant association between trough venetoclax concentrations (C\u003csub\u003e0\u003c/sub\u003e and C\u003csub\u003e0\u003c/sub\u003e/D) and the incidence of FN, identifying predictive thresholds. This association underscores the value of TDM in clinical practice to optimize safety. However, there are two limitations. 1) The limited sample size may restrict the identification of complex factors beyond the primary predictors revealed in multivariate analysis, despite numerous univariate associations (e.g., baseline platelets/ANC). 2) The moderate discriminative power of the PK thresholds for FN prediction may relate to untimely blood sampling relative to FN onset. Further large-scale, multi-center validation is warranted.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003e \u003cb\u003eConflict of interest disclosure\u003c/b\u003e \u003c/h2\u003e \u003cp\u003eAll authors disclosed no relevant relationships.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThis work was supported by the Establishment of molecularly targeted drug adverse reactions based on artificial intelligence technology [grant numbers JSHD2021004] and the Outstanding Young and Middle-aged Talents Support Program of the First Affiliated Hospital with Nanjing Medical University (Jiangsu Province Hospital) [grant numbers YNRCQN025].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYuting Yan: conceived the article concept; drafted, critically revised, and approved the manuscript. Jin Sun: performed the data analysis; drafted, critically revised, and approved the manuscript. Yujiao Guo and Jieyu Sun: critically revised, and approved the manuscript. Yu Zhu, Luning Sun and Yongqing Wang: critically revised and approved the manuscript. All authors contributed to manuscript development and provided their approval to submit.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRose-Inman H, Kuehl D (2017) Acute Leukemia[J]. Hematol Oncol Clin North Am 31(6):1011\u0026ndash;1028\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander TB, Orgel E (2021) Mixed Phenotype Acute Leukemia: Current Approaches to Diagnosis and Treatment[J]. Curr Oncol Rep 23(2):22\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThol F, D\u0026ouml;hner H, Ganser A (2024) How I treat refractory and relapsed acute myeloid leukemia[J]. Blood 143(1):11\u0026ndash;20\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoulart H, Kantarjian H, Pemmaraju N et al (2025) Venetoclax-Based Combination Regimens in Acute Myeloid Leukemia[J]. Blood Cancer Discov 6(1):23\u0026ndash;37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePollyea DA, Pratz K, Letai A et al (2021) Venetoclax with azacitidine or decitabine in patients with newly diagnosed acute myeloid leukemia: long term follow⁃up from a phase 1b study[J]. Am J Hematol 96(2):208\u0026ndash;217\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei AH, Strickland SA Jr, Hou JZ et al (2019) Venetoclax combined with low⁃dose cytarabine for previously untreated patients with acute myeloid leukemia: results from a phase Ⅰ b/Ⅱ study[J]. J Clin Oncol 37(15):1277\u0026ndash;1284\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei AH, Montesinos P, Ivanov V et al (2020) Venetoclax plus LDAC for newly diagnosed AML ineligible for intensive chemotherapy: a phase 3 randomized placebo-controlled trial[J]. Blood 135(24):2137\u0026ndash;2145\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalem AH, Agarwal SK, Dunbar M et al (2016) Effect of low and high-fat meals on the pharmacokinetics of venetoclax, a selective first-in-class BCL-2 inhibitor[J]. J Clin Pharmacol 56(11):1355\u0026ndash;1361\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheung TT, Salem AH, Menon RM et al (2018) Pharmacokinetics of the BCL-2 Inhibitor Venetoclax in Healthy Chinese Subjects[J]. Clin Pharmacol Drug Dev 7(4):435\u0026ndash;440\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e1000 Genomes Project Consortium, Auton A, Brooks LD et al (2015) A global reference for human genetic variation[J]. Nature 526(7571):68\u0026ndash;74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong J, Liu SB, Rasheduzzaman JM et al (2022) Development of Physiology Based Pharmacokinetic Model to Predict the Drug Interactions of Voriconazole and Venetoclax[J]. Pharm Res 39(8):1921\u0026ndash;1933\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee D, Brackman DJ, Suleiman AA et al (2023) Impact of Multiple Concomitant CYP3A Inhibitors on Venetoclax Pharmacokinetics: A PBPK and Population PK-Informed Analysis[J]. J Clin Pharmacol 63(1):119\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiney MS, Menon RM, Bueno OF et al (2018) Clinical evaluation of P-glycoprotein inhibition by venetoclax: a drug interaction study with digoxin[J]. Xenobiotica 48(9):904\u0026ndash;910\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajme-L\u0026oacute;pez S, Tello-Mercado AC, Ort\u0026iacute;z-Brizuela E et al (2024) Clinical and Microbiological Characteristics of Febrile Neutropenia During Induction Chemotherapy in Adults With Acute Leukemia[J]. Cancer Rep (Hoboken) 7(8):e2129\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal KV, Othus M, Ali Z et al (2024) Identification of factors predicting low-risk febrile neutropenia admissions in adults with acute myeloid leukemia[J]. Blood Adv 8(24):6161\u0026ndash;6170\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuda K, Yoshida T, Sugimoto K (2023) High susceptibility of febrile neutropenia in Japanese patients receiving venetoclax plus azacitidine therapy for acute myeloid leukemia[J]. Ann Hematol 102(4):971\u0026ndash;972\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKobayashi T, Sato H, Miura M et al (2024) Overexposure to venetoclax is associated with prolonged-duration of neutropenia during venetoclax and azacitidine therapy in Japanese patients with acute myeloid leukemia[J]. Cancer Chemother Pharmacol 94(2):285\u0026ndash;296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKt MF, Semwal M, Yoosuf BT et al (2024) Venetoclax adverse event monitoring: a safety meta-analysis of randomized controlled trials and a retrospective evaluation of the FAERS [J]. Ann Hematol 103(8):3179\u0026ndash;3191\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo CD, Lachowiez CA, Takahashi K et al (2021) Venetoclax Combined With FLAG-IDA Induction and Consolidation in Newly Diagnosed and Relapsed or Refractory Acute Myeloid Leukemia[J]. J Clin Oncol 39(25):2768\u0026ndash;2778\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen X, Lu Y, Li Y et al (2024) Remission rate, toxicity and pharmacokinetics of venetoclax-based induction regimens in untreated pediatric acute myeloid leukemia[J]. NPJ Precis Oncol 8(1):248\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavids MS, Roberts AW, Seymour JF et al (2017) Phase I First-in-Human Study of Venetoclax in Patients With Relapsed or Refractory Non-Hodgkin Lymphoma[J]. J Clin Oncol 35(8):826\u0026ndash;833\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang YL, Qian ZY, Zhao Y et al (2023) LC-MS/MS methods for determination of venetoclax in human plasma and cerebrospinal fluid[J]. Biomed Chromatogr 37(12):e5738\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu GX, Dai DP, Wang H et al (2017) Systematic screening for CYP3A4 genetic polymorphisms in a Han Chinese population[J]. Pharmacogenomics 18(4):369\u0026ndash;379\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark HJ, Shinn HK, Ryu SH et al (2007) Genetic polymorphisms in the ABCB1 gene and the effects of fentanyl in Koreans[J]. Clin Pharmacol Ther 81(4):539\u0026ndash;546\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoccia R, Glaspy J, Crawford J et al (2022) Chemotherapy-Induced Neutropenia and Febrile Neutropenia in the US: A Beast of Burden That Needs to Be Tamed?[J]. Oncologist 27(8):625\u0026ndash;636\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagihara M, Yasu T, Gando Y et al (2024) Increased trough concentration of venetoclax when combined with itraconazole for acute myeloid leukemia[J]. Ann Hematol 103(11):4497\u0026ndash;4502\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Gao L, Liang Z et al (2024) Efficacy and safety of coadministration of venetoclax and anti-fungal agents under therapeutic drug monitor in unfit acute myeloid leukemia and high-risk myelodysplastic syndrome with neutropenia: a single-center retrospective study. Leuk Lymphoma 65(3):353\u0026ndash;362\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang X, Mei C, He X et al (2022) Quantification of Venetoclax for Therapeutic Drug Monitoring in Chinese Acute Myeloid Leukemia Patients by a Validated UPLC-MS/MS Method [J]. Molecules 27(5):1607\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Chen M, Chen H et al (2021) Influence of ABCB1 Gene Polymorphism on Rivaroxaban Blood Concentration and Hemorrhagic Events in Patients With Atrial Fibrillation[J]. Front Pharmacol 12:639854\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng F, Cui Z, Li Q et al (2024) Influence of genetic polymorphisms on imatinib concentration and therapeutic response in patients with chronic-phase chronic myeloid leukemia[J]. Int Immunopharmacol 133:112090\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Michmerhuizen MJ, Lao Y et al (2017) Metabolism and Disposition of a Novel B-Cell Lymphoma-2 Inhibitor Venetoclax in Human and Characterization of Its Unusual Metabolites [J]. Drug Metab Dispos 45:294\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeg\u0026iacute;as-Vericat JE, Solana-Altabella A, Ballesta-L\u0026oacute;pez O et al (2020) Drug-drug interactions of newly approved small molecule inhibitors for acute myeloid leukemia[J]. Ann Hematol 99(9):1989\u0026ndash;2007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Wan Q, Wan J et al (2024) Plasma concentrations of venetoclax and Pharmacogenetics correlated with drug efficacy in treatment naive leukemia patients: a retrospective study [J]. Pharmacogenomics J 24(6):37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiba M, Shigematsu A, Suzuki T et al (2023) Shorter duration of venetoclax administration to 14 days has same efficacy and better safety profile in treatment of acute myeloid leukemia [J]. Ann Hematol 102(3):541\u0026ndash;546\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejcl","sideBox":"Learn more about [European Journal of Clinical Pharmacology](http://link.springer.com/journal/228)","snPcode":"228","submissionUrl":"https://submission.nature.com/new-submission/228/3","title":"European Journal of Clinical Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Venetoclax, therapeutic drug monitoring, febrile neutropenia, acute leukemia, pharmacogenomics","lastPublishedDoi":"10.21203/rs.3.rs-8247030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8247030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003ePurpose\u003c/b\u003e Venetoclax, a selective BCL-2 inhibitor, demonstrates efficacy in acute leukemia (AL) but variable pharmacokinetics. Therapeutic drug monitoring (TDM) serves as a critical tool for evaluating the efficacy and safety of venetoclax therapy. This study explored the factors influencing plasma venetoclax concentrations and their correlation with febrile neutropenia (FN), aiming to establish predictive thresholds.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e AL patients receiving venetoclax between August 2023 and March 2025 were retrospectively analyzed. Plasma concentration, baseline characteristics, pharmacogenomics, and clinical parameters were collected. Univariate and multivariate linear regression analyses were performed to identify determinants of venetoclax concentration and their association with FN incidence Receiver\u0026rsquo;s operating characteristic (ROC) curve defined FN-predictive thresholds.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e Among 123 patients, median trough concentration (C\u003csub\u003e0\u003c/sub\u003e) was 1409.88 ng/mL and peak concentration (C\u003csub\u003e6\u003c/sub\u003e) was 2373.21 ng/mL. Multivariate analysis identified age, azoles, and transplantation status as C\u003csub\u003e0\u003c/sub\u003e determinants, and \u003cem\u003eABCB1 1236C\u0026gt;T (rs1128503) TT\u003c/em\u003e genotype predicted elevated C\u003csub\u003e6\u003c/sub\u003e. Body weight, azoles, and transplantation status independently affected C\u003csub\u003e0\u003c/sub\u003e/D (C0 normalized to the administered dose, D), while D-dimer (DD2) level and azoles were significant determinants of C\u003csub\u003e6\u003c/sub\u003e/D. Patients with venetoclax C\u003csub\u003e0\u003c/sub\u003e levels exceeding 1202.07 ng/mL or a C\u003csub\u003e0\u003c/sub\u003e/D ratio greater than 6.85 ng/mL per mg exhibited a significantly higher incidence of FN.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e Higher venetoclax concentrations increase FN risk. Regular monitoring of blood venetoclax concentration using TDM can predict the risk of FN occurrence. The determination of this concentration threshold can provide a basis for optimizing the treatment quality of AL patients.\u003c/p\u003e","manuscriptTitle":"Association Between Venetoclax Concentration and Febrile Neutropenia in Acute Leukemia: A Retrospective Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 10:07:44","doi":"10.21203/rs.3.rs-8247030/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-23T20:40:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T20:01:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3550858841618867866802729676980028863","date":"2025-12-20T10:20:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-17T13:08:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T07:53:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-10T07:51:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Pharmacology","date":"2025-12-01T06:55:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-pharmacology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejcl","sideBox":"Learn more about [European Journal of Clinical Pharmacology](http://link.springer.com/journal/228)","snPcode":"228","submissionUrl":"https://submission.nature.com/new-submission/228/3","title":"European Journal of Clinical Pharmacology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"7aac5f39-2d96-4705-b479-280bc6d0284a","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T13:10:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 10:07:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8247030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8247030","identity":"rs-8247030","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}