Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukemia

Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukemia | 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 Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukemia Yufeng Du, Chunhong Li, Yonghong Chen, Fang Xie, Jinsong Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5949741/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective This study aims to develop a clinical prediction model for sensitivity to Bcl-2 inhibitors combined with hypomethylating agents (HMAs) in elderly/unfit patients with acute myeloid leukemia (AML). Methods Clinical data, including French-American-British (FAB) classification, chromosomal karyotype, and second-generation sequencing results, were retrospectively collected from consecutive elderly/unfit patients with AML treated with Bcl-2 inhibitors in combination with HMAs between September 2019 and March 2024. Treatment efficacy was assessed in all patients. Logistic regression and Akaike information criterion were used to identify risk variables affecting efficacy. A nomogram was developed based on these variables to assess patient sensitivity to the treatment regimen. The performance of the nomogram was evaluated using a receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Results This study included 209 patients with AML. The FAB classification, AML type, AML status, prior HMAs exposure, chromosomal karyotype, and mutations in ASXL1 , FLT3 , IDH , NPM1 , and CEBPA were screened to develop the nomogram. The area under the ROC curve indicated a discriminatory power of 0.900 (95% CI, 0.860–0.941). The calibration curve suggested favorable concordance between the predicted and actual occurrence probabilities ( P = 0.849). DCA revealed a net clinical benefit when the threshold probability ranged from 0 to 0.98. Internal validation, performed 500 times using the bootstrap method, demonstrated a satisfactory model performance in the validation set. Conclusion A prediction model was developed and validated to serve as a decision-making tool for physicians treating elderly/unfit patients with AML prior to initiating therapy with Bcl-2 inhibitors combined with HMAs. Acute myeloid leukemia Bcl-2 inhibitor Hypomethylating agents Nomogram Sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Acute myeloid leukemia (AML) is a highly invasive hematological malignancy with the highest incidence and mortality rates among adult patients with leukemia. It predominantly affects the elderly population (with a median age of onset of 69 years) [ 1 , 2 ]. The standard therapeutic approach for AML involves intensive chemotherapy (IC) for remission induction followed by sequential consolidation chemotherapy or hematopoietic stem cell transplantation. This regimen enables approximately 40% of young patients (< 60 years of age) to achieve long-term survival [ 3 ]. However, elderly/unfit AML patients often have poor physical conditions or comorbidities, resulting in reduced tolerance to IC and an increased risk of chemotherapy-related deaths. The early mortality rate is 25–30% for patients aged 60–69 years and exceeds 50% for those aged ≥ 70 years [ 4 ]. To mitigate treatment-related mortality (TRM), researchers have explored alternative approaches for elderly/unfit AML patients, such as low-dose cytarabine (LDAC) or palliative treatments (PT) (such as hydroxyurea or blood transfusion). Although these strategies resulted in a lower TRM than IC, their efficacy was limited. The complete response (CR) rate for LDAC was merely 7.7– 9%, with a 1-year overall survival (OS) rate of 13%, indicating minimal benefits from LDAC or PT for AML treatment [ 5 , 6 ]. Subsequently, hypomethylating agents (HMAs) have been introduced for AML treatment. Two key phase III clinical trials demonstrated CR/CRi (CR with incomplete blood count recovery) rates of 17.8% and 27.8% and 1-year OS rates of 32.1% and 46.5%, respectively [ 7 , 8 ]. Real-world studies have reported a CR rate of 15.6% [ 9 ]. Although HMAs showed improved efficacy compared to LDAC or PT without significantly increasing adverse reactions, their effectiveness remains suboptimal [ 10 ]. Consequently, enhancing the prognosis of elderly/unfit patients with AML remains an urgent challenge that requires immediate attention. The B-cell lymphoma-2 (Bcl-2) protein family plays a crucial role in the regulation of apoptosis via mitochondrial outer membrane permeability. Overexpression of Bcl-2 proteins is associated with resistance to apoptosis in tumor cells and contributes to the pathogenesis of various hematological malignancies [ 11 ]. Bcl-2 family of proteins are categorized into three types based on their structure and function: anti-apoptotic proteins, pro-apoptotic effector proteins (BAX/BAK), and BH3-only pro-apoptotic proteins. Under normal conditions, anti-apoptotic proteins inhibit BAX/BAK in the mitochondrial outer membrane and suppress apoptosis. In response to stress, BH3-only pro-apoptotic proteins bind to anti-apoptotic Bcl-2 family proteins, alleviating their inhibition of BAX/BAK, and ultimately activating the caspase cascade to facilitate apoptosis [ 12 , 13 ]. Venetoclax (Ven) is a selective Bcl-2 inhibitor that binds to Bcl-2 protein, reactivating the mitochondrial apoptotic process [ 14 ]. A phase II clinical trial conducted by Konopleva et al. demonstrated that Ven monotherapy had limited therapeutic efficacy in AML [ 15 ]. However, subsequent phase Ib and III clinical trials of Ven combined with HMAs (Ven-HMAs) for the treatment of newly diagnosed elderly/unfit AML showed promising results. These trials reported CR/CRi rates of 67% and 66.4%, respectively, with median OS periods of 17.5 months and 14.7 months [ 16 , 17 ]. AML comprises a group of highly heterogeneous myeloid tumors with different subgroups exhibiting substantial variations in therapeutic responses to Ven-HMAs treatment. DiNardo et al [ 18 ]. found that patients with NPM1 and IDH molecular mutations demonstrated high response rates and prolonged remission periods when they were treated with Ven-HMAs. In contrast, patients with TP53 deletions or biallelic mutations and FLT3 mutations were less responsive to Ven-HMAs treatment. The researchers also preliminarily explored the mechanisms of Ven resistance. In addition to DiNardo's findings, other studies have indicated factors influencing therapeutic efficacy, including AML type (de novo or secondary AML), AML status (newly diagnosed or refractory/relapsed AML), French-American-British (FAB) type, chromosomal karyotype, and other molecular mutations [ 19 – 22 ]. These studies revealed that different AML subgroups exhibit varying benefits from Ven-HMAs treatment. Although these findings provide some guidance for clinical practice, a method for identifying patients more likely to benefit from this treatment has not yet been established. To address this gap and facilitate the identification of AML patients who are more likely to benefit from Ven-HMAs treatment, there is an urgent need to construct a clinical prediction model to predict the sensitivity (CR/CRi) of AML patients to this treatment regimen. Previously, Zong et al. established a model for predicting AML resistance to Ven-HMAs, which holds a certain referential significance for screening patients with AML resistant to Ven-HMAs [ 23 ]. However, this model does not comprehensively incorporate other factors with predictive value, potentially reducing its predictive efficiency and clinical applicability. Furthermore, in clinical practice, physicians might be more concerned about the sensitivity of treatment rather than therapeutic resistance. Therefore, based on a comprehensive analysis of the factors influencing the efficacy of Ven-HMAs in elderly/unfit AML patients, we developed and validated a clinical model for predicting the sensitivity of AML to the Ven-HMAs treatment regimen. 2. Materials and Methods This retrospective study was conducted in accordance with the Declaration of Helsinki principles and was approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University (Protocol No.: KY2024-183-01). 2.1 Participants and drug administration This retrospective investigation analyzed the clinical data of elderly/unfit AML patients treated with Ven-HMAs at the Second Affiliated Hospital of Dalian Medical University and Yichang Central People's Hospital between June 2019 and March 2024. Patient information was extracted from the electronic medical records. AML classification and risk stratification followed the World Health Organization (WHO) 2016 criteria and 2022 European Leukemia Network (ELN) guidelines [ 24 , 25 ]. The drug regimen consisted of Ven 100 mg d 1, 200 mg d 2, and 400 mg from d 3–28; azacitidine 75 mg/m 2 d 1–7, or decitabine 20 mg/m 2 d 1–5. The selection of HMAs was based on patient preferences or financial considerations. Ven dosage was reduced in patients concurrently receiving azoles (CYP3A inhibitors) [ 26 ]. 2.2 Chromosomal karyotype analysis and detection of AML gene mutations Conventional karyotype analysis was conducted using the R-chromosome banding technique, with at least 20 metaphase divisions analyzed for each patient. Bone marrow fluid samples were analyzed by Shanghai Rightongene Biotechnology Co., Ltd., China, using next-generation sequencing to detect 62 common AML mutated genes (Supplemental table S1 ). 2.3 Data collection and definition of efficacy Information regarding gender, age, blood cell count, AML type, FAB classification, AML status, chromosomal karyotype, and AML mutation genes of the enrolled patients was collected. Bone marrow morphology was assessed based on the FAB classification system. Refractory AML was defined as persistent leukemia without remission after at least two cycles of induction chemotherapy. Relapsed AML was defined as a recurrence of > 5% of bone marrow blasts after achieving CR/CRi. Secondary AML (S-AML) was a myelodysplastic syndrome/myeloproliferative neoplasm transformation or therapy-related AML. Treatment response outcomes were classified according to the ELN-2022 standards, including CR/CRi, Partial response (PR), overall response rate (ORR, CR/CRi + PR), and no response (NR). Patients achieving CR/CRi were categorized as "sensitive", while those with PR/NR were deemed "insensitive". 2.4 Development, assessment, and validation of the nomogram Model development and evaluation were performed using the R (Version 4.3.1) software. Batch univariate logistic regression was conducted using custom functions, and variables (influencing factors) with P < 0.1 in the univariate analysis were selected for multivariate regression. Multivariate models were constructed using "forced entry, forward, backward, and forward-backward stepwise (both)" methods. The Akaike information criterion (AIC) was applied to compare the superiority and inferiority of each model. Eventually, the "forward-backward method" was adopted to construct the model, and the "regplot" package was used to present the nomogram of the model. Internal validation was performed by bootstrap resampling (500 iterations) to evaluate prediction accuracy. The "pROC" package was used to draw the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC) to assess the discrimination of the model. The "fbroc" package was used to generate the bootstrap ROC. The "HLtest.R" package was used for the Hosmer-Lemeshow test and calibration curve for calibration evaluation. The "rms" package was used to draw the calibration curve and bootstrap resampling was conducted to draw the corrected curve. The net benefit of the model was evaluated by decision curve analysis (DCA) and verified by bootstrap, using the "rmda" package, which also generated Clinical Impact Curve (CIC). 2.5 Statistical analysis Data analysis and figure plotting were performed using R (version 4.3.1) and GraphPad Prism 9.2.0. Normally distributed continuous variables were presented as mean ± standard deviation, and comparisons between two groups were conducted using independent sample t-tests. Non-normally distributed continuous variables were presented as medians (P 25 , P 75 , interquartile range), and between-group comparisons were performed using the Mann-Whitney U test. Categorical data were expressed as frequencies (percentages), and differences between groups were assessed using the chi-square test. Binary Logistic regression analysis was applied to the dichotomous outcomes. Survival curves were constructed using the Kaplan-Meier method, and between-group differences in survival were evaluated using the log-rank test. A P -value < 0.05 was considered statistically significant for all analyses. 3. Result 3.1 Patient characteristics The clinical information of 219 patients with AML was collected in this study. After applying the inclusion and exclusion criteria (Fig. 1 and Supplemental table S2 ), 209 patients were included in the predictive model. The cohort comprised 122 males (58.4%) and 87 females (41.6%) with a median age of 66 (57–71) years. Among them, de novo AML accounted for 142 cases (67.9%), while S-AML accounted for 67 cases (32.1%). Newly diagnosed (ND) AML constituted 151 cases (72.2%) and refractory/relapsed (R/R) AML comprised 58 cases (27.8%). Cytogenetic analysis revealed normal karyotypes in 121 cases (57.9%), hyperdiploid or polyploid karyotypes in 30 cases (14.4%), and karyotypes with adverse prognosis in 58 cases (27.8%). Genes with mutation frequencies exceeding 20% included ASXL1 , DNMT3A , K/NRAS , RUNX1 , TET2 , and FLT3 , whereas IDH gene mutations were observed in 19.6% of cases. Risk stratification according to the European LeukemiaNet 2022 (ELN-2022) criteria classified 16 cases (7.7%) as low-risk, 51 cases (24.4%) as intermediate-risk, and 142 cases (67.9%) as high-risk. Additionally, 21 patients (10.0%) had concurrent malignancies and 55 patients (26.3%) had previously received HMAs treatment. (Fig. 2 and Supplemental table S3 ). 3.2 Assessment of therapeutic efficacy Following one to two courses of Ven-HMAs treatment, the CR/CRi rate was 45.9% (96/209). The rate of achieving measurable residual disease (MRD) negativity was 35.4% (74/209), while the PR rate was 10.5% (22/209). The ORR was 56.5% (118/209). Notably, the median survival of the treatment-sensitive group was significantly higher than that of the treatment-insensitive group (HR 0.19, 95% CI 0.13–0.26, P < 0.01). (Fig. 3 A - B). 3.3 Selection of model predictive variables Potential predictive variables for treatment sensitivity (CR/CRi) encompassed demographic, clinical, and genetic factors. These included age, gender, Eastern Cooperative Oncology Group (ECOG) score, FAB classification, AML type, AML status, concurrent malignancies, prior HMAs exposure (prior_HMAs), HMAs type, chromosomal karyotype, myeloid gene mutations (molecular mutations with more than 6 cases in the cohort occurring in more than six cases in the cohort), and MLL fusion gene status. Univariate logistic regression analysis was performed for these variables (Supplemental table S4 ). Subsequently 14 variables (FAB classification, AML type, AML status, prior_HMAs, chromosomal karyotype, ASXL1 , FLT3 , IDH , NPM1 , CEBPA , PTPN11 , K/NRAS , SF3B1 , and TP53 ) were screened out with a threshold of P < 0.1 for inclusion in the multivariate logistic regression. Subsequently, a model was developed based on minimum AIC (AIC = 191.47). The final model incorporated 10 variables: FAB Classification, AML type, AML status, prior_HMAs, chromosomal karyotype, and mutations in ASXL1 , FLT3 , IDH , NPM1 , and CEBPA (Table 1 ). Table 1 Model variables were selected using univariate and multivariate regression based on the AIC minimum criterion. Variables Univariate logistic regression ( P < 0.1) Multivariate logistic regression and minimum AIC OR(95% CI) P value OR(95% CI) P value FAB type M1/M2 * 0.008 0.006 M4/M5 0.394 (0.212–0.731) 0.003 0.397 (0.166–0.951) 0.038 M0/M6/M7 0.449 (0.156–1.291) 0.137 0.086 (0.015–0.482) 0.005 S-AML, yes 0.301 (0.160–0.568) < 0.001 0.267 (0.102–0.696) 0.007 R/R AML, yes 0.135 (0.062–0.295) < 0.001 0.123 (0.039–0.386) < 0.001 Chromosome karyotype Diploid Karyotype * < 0.001 < 0.001 Hyperdiploid/polyploid karyotype 2.078 (0.881–4.904) 0.095 3.517 (0.932–13.263) 0.063 Adverse karyotype 0.208 (0.099–0.440) < 0.001 0.198 (0.072–0.544) 0.002 Prior_HMAs, yes 0.128 (0.057–0.288) < 0.001 0.352 (0.111–1.120) 0.077 Gene mutations ASXL1 0.487 (0.258–0.920) 0.027 0.483 (0.178–1.311) 0.153 CEBPA 5.046 (1.614–15.775) 0.005 7.031 (1.321–37.423) 0.022 FLT3 0.433 (0.211–0.889) 0.023 0.197 (0.070–0.554) 0.002 IDH 4.912 (2.258–10.689) < 0.001 2.879 (1.002–8.270) 0.049 NPM1 5.624 (2.316–13.662) < 0.001 6.144 (1.728–21.843) 0.005 PTPN11 0.299 (0.081–1.105) 0.070 0.318 (0.035–2.873) 0.307 K/NRAS 0.514 (0.269–0.985) 0.045 0.572 (0.210–1.556) 0.274 SF3B1 0.389 (0.135–1.122) 0.080 0.968 (0.237–3.945) 0.964 TP53 0.251 (0.104–0.606) 0.002 0.443 (0.120–1.633) 0.221 Abbreviations: *, reference; S-AML, Secondary-AML; R/R AML, Refractory/Relapsed AML; Prior_HMAs, Prior hypomethylating agents exposure; AIC: Akaike Information Criterion. 3.4 Establishment, assessment, and validation of the model Based on the final model variables, a nomogram incorporating ten predictive factors was established (Fig. 4 ). To further assess the performance of the predictive model, we plotted the ROC, calibration, DCA, and clinical impact curves. In addition, we conducted 500 repeated internal validations using the bootstrap method. The AUC of the model's ROC curve was 0.900 (95% CI: 0.860–0.941). The ROC curve of the validation group was plotted with an AUC value of 0.900 (95% CI: 0.852–0.941) (Fig. 5 A - B), demonstrating that the model has robust discriminatory ability. The calibration curve of the model was plotted, and the results indicated good correspondence between the predicted probability of the model and the actual occurrence probabilities (Fig. 6 ). The Hosmer-Lemeshow test yielded a chi-square value of 4.82 ( P = 0.849), indicating no significant difference between the predicted and actual probabilities. The DCA curve was used to evaluate the net clinical benefit of the model. The applicable threshold probability range of the DCA for the model in this study was 0-0.98, whereas the internal validation group suggested a range of 0.03–0.9 (Fig. 7 A - B). Within the 0-0.98 threshold probability range, the net clinical benefit of intervention based on the model's predicted probability was higher than that of no intervention (None) and intervention for all patients (All). The corresponding CIC is shown in Fig. 8 . 4. Discussion In this study, we developed and validated a clinical model to predict the sensitivity of Ven-HMAs treatment in AML patients. The model incorporates AML gene mutations at diagnosis and other clinical information, including morphology and cytogenetics, as prediction parameters. The performance of the model was evaluated using the AUC index, calibration curve, DCA, and CIC, demonstrating good discrimination, accuracy, and net clinical benefit. Moreover, internal validation through 500 bootstrap resamplings further confirmed the excellent performance of the model. Ven-HMAs have been approved for the treatment of newly diagnosed elderly or unfit AML patients, marking a significant shift in therapeutic modalities for this subgroup [ 27 ]. In recent years, the majority of efficacy reports on Ven-HMAs in AML have primarily originated from retrospective real-world studies. A systematic literature review revealed considerable variations in CR/CRi rates across hematological centers (14.0–75%), with inconsistent findings on factors influencing therapeutic efficacy [ 22 , 28 – 30 ]. These discrepancies may be attributed to the distinct characteristics of the cohorts, sample sizes, and the treatment attributes of each study. Our study, encompassing 209 patients, reported CR/CRi and ORR rates of 45.6% and 56.5%, respectively, with a 35.4% rate of achieving minimal residual disease negativity [MRD(-)]. These outcomes were similar to those of Feld, Short, and Matthews et al. [ 31 – 33 ], but lower than those of Gangat, Chojecki, and Yu et al. [ 34 , 35 ], possibly due to the high proportion of high-risk patients (67.2%) and R/R AML (27.8%) in our cohort. This study further explored the factors that influence the therapeutic effects of Ven-HMAs in AML. Univariate analysis was used to identify several risk factors for CR/CRi. These included specific FAB classification (M1/M2, M4/M5 and M0/M6/M7), S-AML, R/R AML, prior_HMAs, adverse chromosomal karyotypes, and mutations in the ASXL1 , FLT3 , TP53 , and K/NRAS genes. Conversely, M1/M2 FAB classifications and mutations in IDH , NPM1 , and CEBPA were associated with higher CR/CRi rates, consistent with previous studies [ 18 – 20 , 36 – 39 ]. Subsequent multivariate analysis refined these findings, revealing that the ASXL1 , TP53 , and K/NRAS mutations did not significantly influence the CR/CRi rate. This study corroborates previous findings regarding the resistance of AML cells with monocytic differentiation to Ven. Additionally, it supports recent studies suggesting that AML with erythroid/megakaryocytic differentiation is also resistant to Ven. This resistance is proposed to be related to the high expression of Bcl-XL, which differs from the monocytic resistance mechanism [ 38 ]. The variables screened by multivariate regression analysis served as the foundation for constructing a model of sensitivity to Ven-HMAs treatment in patients with AML. We employed multivariate regression to identify eight statistically significant variables. Subsequently, prior_HMAs and ASXL1 mutations were incorporated into the final model, following the AIC minimum optimization model. However, the influence of ASXL1 mutations on remission rates remains controversial. While Johnson and Gangat et al. [ 34 , 40 ] argued that ASXL1 mutation is a protective factor against CR/CRi, Winters et al. [ 36 ] presented contradictory findings. Our research suggests that Prior_HMAs and ASXL1 mutations tend to lower the CR/CRi rate. The prediction model for Ven-HMAs treatment sensitivity in AML established in this study can estimate the probability of achieving CR/CRi in patients with AML based on their clinical characteristics. The AUC value of the model was relatively high, and the calibration curve revealed strong concordance between the predicted probabilities and actual event occurrence rates, indicating a considerable probability of accurately differentiating between sensitive and insensitive patients. Clinical net benefit evaluation using the DCA curve showed that adopting Ven-HMAs treatment within the threshold probability range of 0 to 0.98 yields clinical net benefits. The CIC curve further indicated good consistency between the predicted model probabilities and the actual occurrence probabilities when the risk threshold exceeded 0.6. Considering the current sample size limitations, we opted for internal validation using the bootstrap method, rather than data splitting [ 41 ]. The results suggest a robust model performance. Zong et al. established a resistance prediction model for Ven-HMAs in AML treatment incorporating four variables: FAB-M5 type, S-AML, FTL3-ITD , and RUNX1-RUNX1T1 [ 23 ]. This contrasts our "sensitivity" model. While the first three variables were common to both studies, our research did not identify RUNX1-RUNX1T1 as a predictor of CR/CRi. Our model demonstrated superior AUC performance, indicating higher discriminatory power. A comprehensive comparison of DCA between the two studies requires further investigation. In summary, this study constructed and validated a prediction model for the sensitivity of Ven-HMAs to AML treatment. The model aims to assist clinicians in accurately identifying patients with AML likely to benefit from Ven-HMAs treatment, potentially improving patient outcomes and resource allocation. However, this study has several limitations that warrant consideration. First, this prediction model is based on a retrospective study, which might limit the level of evidence and require prospective studies to corroborate these findings. Second, the validation of this prediction model is internal, which to a certain extent may restrict its generalizability and applicability. External validation is necessary to further verify the predictive ability of the model. Third, as a prediction model, this study required an expansion of the sample size. Although efforts were made to include a substantial number of samples, this study would benefit from a larger dataset to optimize the model's performance and draw more robust conclusions. Future research directions should focus on conducting prospective studies to validate the model's predictive accuracy, performing external validation across diverse patient populations and clinical settings, expanding the sample size to enhance the model's reliability and generalizability, and incorporating emerging biomarkers or genetic factors that may improve the model's predictive power. These efforts will contribute to refining the prediction model and enhancing its clinical utility in guiding Ven-HMAs treatment decisions for patients with AML. 5. Conclusion In this study, we developed and validated a clinical model to predict the sensitivity of elderly or unfit AML patients to combination therapy with Ven-HMAs. This model demonstrates excellent discriminatory power, calibration, and clinical net benefit, potentially enhancing the precision of Ven-HMAs treatment in AML. This can potentially optimize treatment decisions, improve patient outcomes, and advance personalized medicine in this challenging-to-treat population. Declarations Informed Consent Statement: Written informed consent was obtained from the patient for the publication of this paper. Conflicts of Interest: The authors declare no conflicts of interest. Funding: This study was supported by grants from the Science and Technology Innovation Foundation of Dalian (No. 2024JJ13PT069). Author Contribution Y. D. undertook data collection, data analysis, patient follow-up, and manuscript drafting; C. L. was accountable for data analysis and verification; Y. C. was responsible for data collection and verification; F. X. and J. Y. were responsible for supervision, review, and editing. All authors have read and consented to the publication version of the manuscript. Acknowledgement We expressed our gratitude to Bullet Edits for the linguistic refinement of the manuscript. Data Availability Statement: The data provided in this study are available upon request from the corresponding author. References DiNardo CD, Erba HP, Freeman SD, Wei AH. Acute myeloid leukaemia. Lancet. 2023;401(10393):2073–86. 10.1016/S0140-6736(23)00108-3 . Acute Myeloid Leukemia-Cancer Stat Facts. SEER. https://seer.cancer.gov/statf-acts/html/amyl.html Kantarjian H, Kadia T, DiNardo C, Daver N, Borthakur G, Jabbour E, et al. Acute myeloid leukemia: current progress and future directions. Blood Cancer J. 2021;11(2):41. 10.1038/s41408-021-00425-3 . Sasaki K, Ravandi F, Kadia TM, DiNardo CD, Short NJ, Borthakur G, et al. De novo acute myeloid leukemia: A population-based study of outcome in the United States based on the Sur-veillance, Epidemiology, and End Results (SEER) database, 1980 to 2017. Cancer. 2021;127(12):2049–61. 10.1002/cncr.33458 . Burnett AK, Milligan D, Prentice AG, Goldstone AH, McMullin MF, Hills RK, et al. A comparison of low-dose cytarabine and hydroxyurea with or without all-trans retinoic acid for acute myeloid leukemia and high-risk myelodysplastic syndrome in patients not considered fit for intensive treatment. Cancer. 2007;109(6):1114–24. 10.1002/cncr.22496 . Talati C, Dhulipala VC, Extermann MT, Ali NA, Kim J, Komrokji R, et al. Comparisons of commonly used front-line regimens on survival outcomes in patients aged 70 years and older with acute myeloid leukemia. Haematologica. 2020;105(2):398–406. 10.3324/haematol.2018.208637 . Dombret H, Seymour JF, Butrym A, Wierzbowska A, Selleslag D, Jang JH, et al. International phase 3 study of azacitidine vs conventional care regimens in older patients with newly diagnosed AML with > 30% blasts. Blood. 2015;126(3):291–9. 10.1182/blood-2015-01-621664 . Kantarjian HM, Thomas XG, Dmoszynska A, Wierzbowska A, Mazur G, Mayer J, et al. Multicenter, randomized, open-label, phase III trial of decitabine versus patient choice, with physician advice, of either supportive care or low-dose cytarabine for the treatment of older patients with newly diagnosed acute myeloid leukemia. J Clin Oncol. 2012;30(21):2670–7. 10.1200/JCO.2011.38.9429 . Dargenio M, Tarantini G, Cascavilla N, Pavone E, Musto P, Mazza P, et al. Long-Term Follow-Up of Elderly Patients with Acute Myeloid Leukemia Treated with Decitabine: A Real World Study of the Apulian Hematological Network. Cancers (Basel). 2022;14(3):826. 10.3390/cancers14030826 . Gadaud N, Leroy H, Bérard E, Tavitian S, Leguay T, Dimicoli-Salazar S, et al. Azacitidine, intensive chemotherapy or best supportive care in relapsed or refractory acute myeloid leukemia, a DATAML registry study. Leuk Lymphoma. 2022;63(6):1398–406. 10.1080/10428194.2021.2022140 . Roberts AW. Therapeutic development and current uses of BCL-2 inhibition. Hematol Am Soc Hematol Educ Program. 2020;2020(1):1–9. 10.1182/hematology.2020000154 . Pfeffer CM, Singh ATK, Apoptosis. A Target for Anticancer Therapy. Int J Mol Sci. 2018;19:448. 10.3390/ijms19020448 . McBride A, Houtmann S, Wilde L, Vigil C, Eischen CM, Kasner M, et al. The Role of Inhibition of Apoptosis in Acute Leukemias and Myelodysplastic Syndrome. Front Oncol. 2019;9:192. 10.3389/fonc.2019.00192 . Souers AJ, Leverson JD, Boghaert ER, Ackler SL, Catron ND, Chen J, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med. 2013;19(2):202–8. 10.1038/nm.3048 . Konopleva M, Pollyea DA, Potluri J, Chyla B, Hogdal L, Busman T, et al. Efficacy and Biological Correlates of Response in a Phase II Study of Venetoclax Monotherapy in Patients with Acute Myelogenous Leukemia. Cancer Discov. 2016;6(10):1106–17. 10.1158/2159-8290 . DiNardo CD, Pratz K, Pullarkat V, Jonas BA, Arellano M, Becker PS, et al. Venetoclax combined with decitabine or azacitidine in treatment-naive, elderly patients with acute myeloid leukemia. Blood. 2019;133(1):7–17. 10.1182/blood-2018-08-868752 . DiNardo CD, Jonas BA, Pullarkat V, Thirman MJ, Garcia JS, Wei AH, et al. Azacitidine and Venetoclax in Previously Untreated Acute Myeloid Leukemia. N Engl J Med. 2020;383(7):617–29. 10.1056/NEJMoa2012971 . DiNardo CD, Tiong IS, Quaglieri A, MacRaild S, Loghavi S, Brown FC, et al. Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML. Blood. 2020;135(11):791–803. 10.1182/blood.2019003988 . Venugopal S, Maiti A, DiNardo CD, Qiao W, Ning J, Loghavi S, et al. Prognostic impact of conventional cytogenetics in acute myeloid leukemia treated with venetoclax and decitabine. Leuk Lymphoma. 2021;62(14):3501–5. 10.1080/10428194.2021.1973675 . Aldoss I, Yang D, Aribi A, Ali H, Sandhu K, Al Malki MM, et al. Efficacy of the combination of venetoclax and hypomethylating agents in relapsed/refractory acute myeloid leukemia. Haematologica. 2018;103(9):e404–7. 10.3324/haematol.2018.188094 . Aldoss I, Yang D, Pillai R, Sanchez JF, Mei M, Aribi A, et al. Association of leukemia genetics with response to venetoclax and hypomethylating agents in relapsed/refractory acute myeloid leukemia. Am J Hematol. 2019;94(10):E253–5. 10.1002/ajh.25567 . Labrador J, Saiz-Rodríguez M, de Miguel D, de Laiglesia A, Rodríguez-Medina C, Vidriales MB, et al. Use of Venetoclax in Patients with Relapsed or Refractory Acute Myeloid Leukemia: The PETHEMA Registry Experience. Cancers (Basel). 2022;14(7):1734. 10.3390/cancers14071734 . Zong L, Yin M, Kong J, Zhang J, Song B, Zhu J, et al. Development of a scoring system for predicting primary resistance to venetoclax plus hypomethylating agents (HMAs) in acute myeloid leukemia patients. Mol Carcinog. 2023;62(10):1572–84. 10.1002/mc.23600 . Arber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391–405. 10.1182/blood-2016-03-643544 . Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345–77. 10.1182/blood.2022016867 . Azanza JR, Mensa J, Barberán J, Vázquez L, Pérez de Oteyza J, Kwon M, et al. Recommendations on the use of azole antifungals in hematology-oncology patients. Rev Esp Quimioter. 2023;36(3):236–58. 10.37201/req/013.2023 . Alsouqi A, Geramita E, Im A. Treatment of Acute Myeloid Leukemia in Older Adults. Cancers (Basel). 2023;15(22):5409. 10.3390/cancers15225409 . Mirgh S, Sharma A, Shaikh MRMA, Kadian K, Agrawal N, Khushoo V, et al. Hypomethylating agents + venetoclax induction therapy in acute myeloid leukemia unfit for intensive chemotherapy novel avenues for lesser venetoclax duration and patients with baseline infections from a developing country. Am J Blood Res. 2021;11(3):290–302. Otoukesh S, Zhang J, Nakamura R, Stein AS, Forman SJ, Marcucci G, et al. The efficacy of venetoclax and hypomethylating agents in acute myeloid leukemia with extramedullary involvement. Leuk Lymphoma. 2020;61(8):2020–3. 10.1080/10428194.2020.1742908 . Byrne M, Danielson N, Sengsayadeth S, Rasche A, Culos K, Gatwood K, et al. The use of venetoclax-based salvage therapy for post-hematopoietic cell transplantation relapse of acute myeloid leukemia. Am J Hematol. 2020;95(9):1006–14. 10.1002/ajh.25859 . Feld J, Tremblay D, Dougherty M, Czaplinska T, Sanchez G, Brady C, et al. Safety and Efficacy: Clinical Experience of Venetoclax in Combination With Hypomethylating Agents in Both Newly Diagnosed and Relapsed/Refractory Advanced Myeloid Malignancies. Hemasphere. 2021;5(4):e549. 10.1097/HS9.0000000000000549 . Short NJ, Venugopal S, Qiao W, Kadia TM, Ravandi F, Macaron W, et al. Impact of frontline treatment approach on outcomes in patients with secondary AML with prior hypomethylating agent exposure. J Hematol Oncol. 2022;15(1):12. 10.1186/s13045-022-01229-z . Matthews AH, Perl AE, Luger SM, Loren AW, Gill SI, Porter DL, et al. Real-world effectiveness of CPX-351 vs venetoclax and azacitidine in acute myeloid leukemia. Blood Adv. 2022;6(13):3997–4005. 10.1182/bloodadvances.2022007265 . Gangat N, Johnson I, McCullough K, Farrukh F, Al-Kali A, Alkhateeb H, et al. Molecular predictors of response to venetoclax plus hypomethylating agent in treatment-naïve acute myeloid leukemia. Haematologica. 2022;107(10):2501–5. 10.3324/hae-matol.2022.281214 . Chojecki AL, Arnall J, Boselli D, Patel R, Chiad Z, DiSogra KY, et al. Outcomes and hospitalization patterns of patients with acute myelogenous leukemia treated with frontline CPX-351 or HMA/venetoclax. Leuk Res. 2022;119:106904. 10.1016/j. leukres.2022.106904. Winters AC, Gutman JA, Purev E, Nakic M, Tobin J, Chase S, et al. Real-world experience of venetoclax with azacitidine for untreated patients with acute myeloid leukemia. Blood Adv. 2019;3(20):2911–9. 10.1182/bloodadvances.2019000243 . Pei S, Pollyea DA, Gustafson A, Stevens BM, Minhajuddin M, Fu R, et al. Monocytic Subclones Confer Resistance to Venetoclax-Based Therapy in Patients with Acute Myeloid Leukemia. Cancer Discov. 2020;10(4):536–51. 10.1158/2159-8290.CD-19-0710 . Zhao L, Yang J, Chen M, Xiang X, Ma H, Niu T, et al. Myelomonocytic and monocytic acute myeloid leukemia demonstrate comparable poor outcomes with venetoclax-based treatment: a monocentric real-world study. Ann Hematol. 2024;103(4):1197–209. 10.1007/s00277-024-05646-7 . Morsia E, McCullough K, Joshi M, Cook J, Alkhateeb HB, Al-Kali A, et al. Venetoclax and hypomethylating agents in acute myeloid leukemia: Mayo Clinic series on 86 patients. Am J Hematol. 2020;95(12):1511–21. 10.1002/ajh.25978 . Johnson IM, Ilyas R, McCullough K, Al-Kali A, Alkhateeb HB, Begna K, et al. Molecular Predictors of Response and Survival in Patients with Relapsed/Refractory Acute Myeloid Leukemia Following Venetoclax Plus Hypomethylating Agent Therapy. Blood. 2022;140(Suppl 1):3233–4. 10.1182/blood-2022-167967 . Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ. 2024;386:e078276. 10.1136/bmj-2023-078276 . Additional Declarations No competing interests reported. Supplementary Files SupplementaltableS1.docx SupplementaltableS2.docx SupplementaltableS3.docx SupplementaltableS4.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2025 Reviews received at journal 16 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviews received at journal 20 Feb, 2025 Reviewers agreed at journal 18 Feb, 2025 Reviewers invited by journal 17 Feb, 2025 Editor assigned by journal 14 Feb, 2025 Submission checks completed at journal 12 Feb, 2025 First submitted to journal 03 Feb, 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. 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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-5949741","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":414744599,"identity":"67364783-dcd3-4db1-b7e0-84923532af35","order_by":0,"name":"Yufeng Du","email":"","orcid":"","institution":"Department of Hematology, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, the Second Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yufeng","middleName":"","lastName":"Du","suffix":""},{"id":414744603,"identity":"e646e133-11f9-4250-986b-5ae46c4a2b53","order_by":1,"name":"Chunhong Li","email":"","orcid":"","institution":"Department of Hematology, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, the Second Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunhong","middleName":"","lastName":"Li","suffix":""},{"id":414744604,"identity":"7c7a135c-c8a1-4525-9af5-8d9de5a2c48d","order_by":2,"name":"Yonghong Chen","email":"","orcid":"","institution":"Yichang Central People's Hospital, the First College of Clinical Medical Science, three Gorges University","correspondingAuthor":false,"prefix":"","firstName":"Yonghong","middleName":"","lastName":"Chen","suffix":""},{"id":414744605,"identity":"1c05e1f4-071f-400b-b30d-a4ee002bee7e","order_by":3,"name":"Fang Xie","email":"","orcid":"","institution":"Department of Hematology, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, the Second Hospital of Dalian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xie","suffix":""},{"id":414744607,"identity":"cdbd1235-6dd1-41f1-bcf8-32dc2fe854df","order_by":4,"name":"Jinsong Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYJACAwY2BgZ+hgQQm5kELZINpGhhAGkxOECsFoPjZw8U/ijblrj5eHaaBEOFdWID+9kD+LWcyUsw5jl3O3HbmbfbJBjOpCc28OQl4NdyIMfAmLENqOVG7jYJxrbDiQ0SPAb4tZx/Y2D4E6hl8wyQln/EaLmRY2DAC9SyQQKkpYEILZI33hiA/GI848zbzRYJx9KN23hy8GvhO59jZvij7LZsf3vuxhsfaqxl+9nP4NeicICBDaEigQEcR/iBfAMD8wNCikbBKBgFo2CEAwDXIUusIoH0AgAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Hematology, Liaoning Medical Center for Hematopoietic Stem Cell Transplantation, the Second Hospital of Dalian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2025-02-03 09:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5949741/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5949741/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76661200,"identity":"867d0d0e-11ed-4d7e-91b7-f9b3198072de","added_by":"auto","created_at":"2025-02-19 12:04:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1980116,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patients inclusion and exclusion criteria. (FAB classification: French-American-British classification system, AML type: de novo or secondary AML, AML status: newly diagnosed or refractory/relapsed AML, Prior HMAs: Prior hypomethylating agents exposure).\u003c/p\u003e","description":"","filename":"Fig1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/6bdfa16e35a91f4c74906414.jpg"},{"id":76664920,"identity":"20f48f6c-2fe4-4b0e-a17c-23cb4f472c84","added_by":"auto","created_at":"2025-02-19 12:36:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6523479,"visible":true,"origin":"","legend":"\u003cp\u003eThe clinical characteristics of the enrolled patients (including treatment response, AML type, AML status, FAB classification, ELN-2022 risk stratification, and next-generation sequencing) were analyzed.\u003c/p\u003e","description":"","filename":"Fig2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/d3c4e2979ad1e37c819be50d.jpg"},{"id":76663379,"identity":"d967d42f-5ac8-4550-8c83-918c31f14072","added_by":"auto","created_at":"2025-02-19 12:20:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":938455,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival curves for the sensitive (CR/CRi) group and insensitive (PR/NR) group, with the dashed line representing the 50% probability of overall survival and the checkmark symbol denoting censored data.\u003c/p\u003e","description":"","filename":"Fig3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/f5b47d3480d8e26fed9f9dc6.jpg"},{"id":76664680,"identity":"567d40fb-d22e-43fd-a72e-84a51496f38c","added_by":"auto","created_at":"2025-02-19 12:28:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":263362,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting sensitivity to Ven-HMAs in elderly/unfit Patients with AML. Nomograms were developed using multivariate logistic regression, with variable selection guided by the AIC. The nomogram incorporates the following prognostic factors to generate a total score: FAB classification, AML type, AML status, prior_HMAs, chromosomal karyotype and mutational status of \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eIDH\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, and \u003cem\u003eCEBPA\u003c/em\u003e. Ven, Venetoclax; HMAs, Hypomethylating agents; AIC, Akaike Information Criterion; prior_HMAs, Prior hypomethylating agents exposure.\u003c/p\u003e","description":"","filename":"Fig4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/bf51de12281a2ca37e81eff9.jpg"},{"id":76663404,"identity":"6bef6715-7d9f-4c42-a439-bc2ddf6d8ff1","added_by":"auto","created_at":"2025-02-19 12:20:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":636299,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for the nomogram. ROC curves for the column line graphs. Figure A represents the ROC curve for the training set (AUC = 0.90) and Figure B represents the internal validation set. ROC, Receiver operating characteristic; AUC, Area under curve.\u003c/p\u003e","description":"","filename":"Fig5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/33cc728dd1e36a535028f8c1.jpg"},{"id":76664682,"identity":"8fecd1e7-7946-4a45-8380-71d7dfc5872e","added_by":"auto","created_at":"2025-02-19 12:28:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":210258,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves for the nomogram. Ideal represents the ideal reference line, Apparent represents the model prediction curve, and Bias-correct represents the internal validation curve (bootstrap 500 times).\u003c/p\u003e","description":"","filename":"Fig6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/42a878f4666ca9e4d3c4c6e4.jpg"},{"id":76661209,"identity":"1d4b6ba5-99f7-4c11-a0e5-4ab8f41be7f5","added_by":"auto","created_at":"2025-02-19 12:04:45","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":551732,"visible":true,"origin":"","legend":"\u003cp\u003eDecision analysis curves for the nomogram. Figure A represents the decision analysis curve for the training set and Figure B represents the decision analysis curve for the internal validation set (bootstrap 500 times).\u003c/p\u003e","description":"","filename":"Fig7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/61e8655764283872cc0fa593.jpg"},{"id":76661212,"identity":"aef7ea5f-27fe-4352-87f5-14fabf9d773f","added_by":"auto","created_at":"2025-02-19 12:04:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":343357,"visible":true,"origin":"","legend":"\u003cp\u003eClinical impact curves for the nomogram. The red line indicates the predicted number of outcome events according to the model, while the blue line represents the actual number of events that occurred.\u003c/p\u003e","description":"","filename":"Fig8.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/d7dc2134490d28c6bae9a6e9.jpg"},{"id":76674837,"identity":"c57acff0-8e21-49a5-be35-bf1de100a62f","added_by":"auto","created_at":"2025-02-19 14:17:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12373818,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/e8f92e3c-9885-43c2-9f3f-9e3b8770c119.pdf"},{"id":76661199,"identity":"dbc9c3a1-d808-42f8-9cc7-bc3b9b90b9fb","added_by":"auto","created_at":"2025-02-19 12:04:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15242,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaltableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/f26676f8e9d6c8f57fad3c73.docx"},{"id":76661203,"identity":"5014fa91-395b-485f-8c08-7f19097a3c19","added_by":"auto","created_at":"2025-02-19 12:04:45","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14530,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaltableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/2e6e0b43f75673129580a5b3.docx"},{"id":76663027,"identity":"f1bc0045-587d-4921-b8bd-66c214d0db27","added_by":"auto","created_at":"2025-02-19 12:12:45","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19145,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaltableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/0f3788fb81f02b66d11c7ec3.docx"},{"id":76661206,"identity":"23d77c7c-ae8f-4f72-8ce7-f799a088c40d","added_by":"auto","created_at":"2025-02-19 12:04:45","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":27262,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaltableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-5949741/v1/24f644c09e8b57b1f24b2109.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukemia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute myeloid leukemia (AML) is a highly invasive hematological malignancy with the highest incidence and mortality rates among adult patients with leukemia. It predominantly affects the elderly population (with a median age of onset of 69 years) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The standard therapeutic approach for AML involves intensive chemotherapy (IC) for remission induction followed by sequential consolidation chemotherapy or hematopoietic stem cell transplantation. This regimen enables approximately 40% of young patients (\u0026lt;\u0026thinsp;60 years of age) to achieve long-term survival [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, elderly/unfit AML patients often have poor physical conditions or comorbidities, resulting in reduced tolerance to IC and an increased risk of chemotherapy-related deaths. The early mortality rate is 25\u0026ndash;30% for patients aged 60\u0026ndash;69 years and exceeds 50% for those aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. To mitigate treatment-related mortality (TRM), researchers have explored alternative approaches for elderly/unfit AML patients, such as low-dose cytarabine (LDAC) or palliative treatments (PT) (such as hydroxyurea or blood transfusion). Although these strategies resulted in a lower TRM than IC, their efficacy was limited. The complete response (CR) rate for LDAC was merely 7.7\u0026ndash; 9%, with a 1-year overall survival (OS) rate of 13%, indicating minimal benefits from LDAC or PT for AML treatment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Subsequently, hypomethylating agents (HMAs) have been introduced for AML treatment. Two key phase III clinical trials demonstrated CR/CRi (CR with incomplete blood count recovery) rates of 17.8% and 27.8% and 1-year OS rates of 32.1% and 46.5%, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Real-world studies have reported a CR rate of 15.6% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although HMAs showed improved efficacy compared to LDAC or PT without significantly increasing adverse reactions, their effectiveness remains suboptimal [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Consequently, enhancing the prognosis of elderly/unfit patients with AML remains an urgent challenge that requires immediate attention.\u003c/p\u003e \u003cp\u003eThe B-cell lymphoma-2 (Bcl-2) protein family plays a crucial role in the regulation of apoptosis via mitochondrial outer membrane permeability. Overexpression of Bcl-2 proteins is associated with resistance to apoptosis in tumor cells and contributes to the pathogenesis of various hematological malignancies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Bcl-2 family of proteins are categorized into three types based on their structure and function: anti-apoptotic proteins, pro-apoptotic effector proteins (BAX/BAK), and BH3-only pro-apoptotic proteins. Under normal conditions, anti-apoptotic proteins inhibit BAX/BAK in the mitochondrial outer membrane and suppress apoptosis. In response to stress, BH3-only pro-apoptotic proteins bind to anti-apoptotic Bcl-2 family proteins, alleviating their inhibition of BAX/BAK, and ultimately activating the caspase cascade to facilitate apoptosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Venetoclax (Ven) is a selective Bcl-2 inhibitor that binds to Bcl-2 protein, reactivating the mitochondrial apoptotic process [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A phase II clinical trial conducted by Konopleva et al. demonstrated that Ven monotherapy had limited therapeutic efficacy in AML [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, subsequent phase Ib and III clinical trials of Ven combined with HMAs (Ven-HMAs) for the treatment of newly diagnosed elderly/unfit AML showed promising results. These trials reported CR/CRi rates of 67% and 66.4%, respectively, with median OS periods of 17.5 months and 14.7 months [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. AML comprises a group of highly heterogeneous myeloid tumors with different subgroups exhibiting substantial variations in therapeutic responses to Ven-HMAs treatment. DiNardo et al [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. found that patients with \u003cem\u003eNPM1\u003c/em\u003e and \u003cem\u003eIDH\u003c/em\u003e molecular mutations demonstrated high response rates and prolonged remission periods when they were treated with Ven-HMAs. In contrast, patients with \u003cem\u003eTP53\u003c/em\u003e deletions or biallelic mutations and \u003cem\u003eFLT3\u003c/em\u003e mutations were less responsive to Ven-HMAs treatment. The researchers also preliminarily explored the mechanisms of Ven resistance.\u003c/p\u003e \u003cp\u003eIn addition to DiNardo's findings, other studies have indicated factors influencing therapeutic efficacy, including AML type (de novo or secondary AML), AML status (newly diagnosed or refractory/relapsed AML), French-American-British (FAB) type, chromosomal karyotype, and other molecular mutations [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These studies revealed that different AML subgroups exhibit varying benefits from Ven-HMAs treatment. Although these findings provide some guidance for clinical practice, a method for identifying patients more likely to benefit from this treatment has not yet been established.\u003c/p\u003e \u003cp\u003eTo address this gap and facilitate the identification of AML patients who are more likely to benefit from Ven-HMAs treatment, there is an urgent need to construct a clinical prediction model to predict the sensitivity (CR/CRi) of AML patients to this treatment regimen. Previously, Zong et al. established a model for predicting AML resistance to Ven-HMAs, which holds a certain referential significance for screening patients with AML resistant to Ven-HMAs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, this model does not comprehensively incorporate other factors with predictive value, potentially reducing its predictive efficiency and clinical applicability. Furthermore, in clinical practice, physicians might be more concerned about the sensitivity of treatment rather than therapeutic resistance. Therefore, based on a comprehensive analysis of the factors influencing the efficacy of Ven-HMAs in elderly/unfit AML patients, we developed and validated a clinical model for predicting the sensitivity of AML to the Ven-HMAs treatment regimen.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e This retrospective study was conducted in accordance with the Declaration of Helsinki principles and was approved by the Ethics Committee of the Second Affiliated Hospital of Dalian Medical University (Protocol No.: KY2024-183-01).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants and drug administration\u003c/h2\u003e \u003cp\u003eThis retrospective investigation analyzed the clinical data of elderly/unfit AML patients treated with Ven-HMAs at the Second Affiliated Hospital of Dalian Medical University and Yichang Central People's Hospital between June 2019 and March 2024. Patient information was extracted from the electronic medical records. AML classification and risk stratification followed the World Health Organization (WHO) 2016 criteria and 2022 European Leukemia Network (ELN) guidelines [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The drug regimen consisted of Ven 100 mg d 1, 200 mg d 2, and 400 mg from d 3–28; azacitidine 75 mg/m\u003csup\u003e2\u003c/sup\u003e d 1–7, or decitabine 20 mg/m\u003csup\u003e2\u003c/sup\u003e d 1–5. The selection of HMAs was based on patient preferences or financial considerations. Ven dosage was reduced in patients concurrently receiving azoles (CYP3A inhibitors) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Chromosomal karyotype analysis and detection of AML gene mutations\u003c/h3\u003e\n\u003cp\u003eConventional karyotype analysis was conducted using the R-chromosome banding technique, with at least 20 metaphase divisions analyzed for each patient. Bone marrow fluid samples were analyzed by Shanghai Rightongene Biotechnology Co., Ltd., China, using next-generation sequencing to detect 62 common AML mutated genes (Supplemental table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e2.3 Data collection and definition of efficacy\u003c/h3\u003e\n\u003cp\u003eInformation regarding gender, age, blood cell count, AML type, FAB classification, AML status, chromosomal karyotype, and AML mutation genes of the enrolled patients was collected. Bone marrow morphology was assessed based on the FAB classification system. Refractory AML was defined as persistent leukemia without remission after at least two cycles of induction chemotherapy. Relapsed AML was defined as a recurrence of \u0026gt; 5% of bone marrow blasts after achieving CR/CRi. Secondary AML (S-AML) was a myelodysplastic syndrome/myeloproliferative neoplasm transformation or therapy-related AML. Treatment response outcomes were classified according to the ELN-2022 standards, including CR/CRi, Partial response (PR), overall response rate (ORR, CR/CRi + PR), and no response (NR). Patients achieving CR/CRi were categorized as \"sensitive\", while those with PR/NR were deemed \"insensitive\".\u003c/p\u003e\n\u003ch3\u003e2.4 Development, assessment, and validation of the nomogram\u003c/h3\u003e\n\u003cp\u003eModel development and evaluation were performed using the R (Version 4.3.1) software. Batch univariate logistic regression was conducted using custom functions, and variables (influencing factors) with \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1 in the univariate analysis were selected for multivariate regression. Multivariate models were constructed using \"forced entry, forward, backward, and forward-backward stepwise (both)\" methods. The Akaike information criterion (AIC) was applied to compare the superiority and inferiority of each model. Eventually, the \"forward-backward method\" was adopted to construct the model, and the \"regplot\" package was used to present the nomogram of the model. Internal validation was performed by bootstrap resampling (500 iterations) to evaluate prediction accuracy. The \"pROC\" package was used to draw the receiver operating characteristic (ROC) curve and calculate the area under the curve (AUC) to assess the discrimination of the model. The \"fbroc\" package was used to generate the bootstrap ROC. The \"HLtest.R\" package was used for the Hosmer-Lemeshow test and calibration curve for calibration evaluation. The \"rms\" package was used to draw the calibration curve and bootstrap resampling was conducted to draw the corrected curve. The net benefit of the model was evaluated by decision curve analysis (DCA) and verified by bootstrap, using the \"rmda\" package, which also generated Clinical Impact Curve (CIC).\u003c/p\u003e\n\u003ch3\u003e2.5 Statistical analysis\u003c/h3\u003e\n\u003cp\u003eData analysis and figure plotting were performed using R (version 4.3.1) and GraphPad Prism 9.2.0. Normally distributed continuous variables were presented as mean ± standard deviation, and comparisons between two groups were conducted using independent sample t-tests. Non-normally distributed continuous variables were presented as medians (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e, interquartile range), and between-group comparisons were performed using the Mann-Whitney U test. Categorical data were expressed as frequencies (percentages), and differences between groups were assessed using the chi-square test. Binary Logistic regression analysis was applied to the dichotomous outcomes. Survival curves were constructed using the Kaplan-Meier method, and between-group differences in survival were evaluated using the log-rank test. A \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05 was considered statistically significant for all analyses.\u003c/p\u003e"},{"header":"3. Result","content":"\u003ch2\u003e3.1 Patient characteristics\u003c/h2\u003e\u003cp\u003eThe clinical information of 219 patients with AML was collected in this study. After applying the inclusion and exclusion criteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplemental table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), 209 patients were included in the predictive model. The cohort comprised 122 males (58.4%) and 87 females (41.6%) with a median age of 66 (57–71) years. Among them, de novo AML accounted for 142 cases (67.9%), while S-AML accounted for 67 cases (32.1%). Newly diagnosed (ND) AML constituted 151 cases (72.2%) and refractory/relapsed (R/R) AML comprised 58 cases (27.8%). Cytogenetic analysis revealed normal karyotypes in 121 cases (57.9%), hyperdiploid or polyploid karyotypes in 30 cases (14.4%), and karyotypes with adverse prognosis in 58 cases (27.8%). Genes with mutation frequencies exceeding 20% included \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eDNMT3A\u003c/em\u003e, \u003cem\u003eK/NRAS\u003c/em\u003e, \u003cem\u003eRUNX1\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, and \u003cem\u003eFLT3\u003c/em\u003e, whereas \u003cem\u003eIDH\u003c/em\u003e gene mutations were observed in 19.6% of cases. Risk stratification according to the European LeukemiaNet 2022 (ELN-2022) criteria classified 16 cases (7.7%) as low-risk, 51 cases (24.4%) as intermediate-risk, and 142 cases (67.9%) as high-risk. Additionally, 21 patients (10.0%) had concurrent malignancies and 55 patients (26.3%) had previously received HMAs treatment. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplemental table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003ch3\u003e3.2 Assessment of therapeutic efficacy\u003c/h3\u003e\u003cp\u003eFollowing one to two courses of Ven-HMAs treatment, the CR/CRi rate was 45.9% (96/209). The rate of achieving measurable residual disease (MRD) negativity was 35.4% (74/209), while the PR rate was 10.5% (22/209). The ORR was 56.5% (118/209). Notably, the median survival of the treatment-sensitive group was significantly higher than that of the treatment-insensitive group (HR 0.19, 95% CI 0.13–0.26, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA - B).\u003c/p\u003e\u003ch2\u003e3.3 Selection of model predictive variables\u003c/h2\u003e\u003cp\u003ePotential predictive variables for treatment sensitivity (CR/CRi) encompassed demographic, clinical, and genetic factors. These included age, gender, Eastern Cooperative Oncology Group (ECOG) score, FAB classification, AML type, AML status, concurrent malignancies, prior HMAs exposure (prior_HMAs), HMAs type, chromosomal karyotype, myeloid gene mutations (molecular mutations with more than 6 cases in the cohort occurring in more than six cases in the cohort), and \u003cem\u003eMLL\u003c/em\u003e fusion gene status. Univariate logistic regression analysis was performed for these variables (Supplemental table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Subsequently 14 variables (FAB classification, AML type, AML status, prior_HMAs, chromosomal karyotype, \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eIDH\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, \u003cem\u003eCEBPA\u003c/em\u003e, \u003cem\u003ePTPN11\u003c/em\u003e, \u003cem\u003eK/NRAS\u003c/em\u003e, \u003cem\u003eSF3B1\u003c/em\u003e, and \u003cem\u003eTP53\u003c/em\u003e) were screened out with a threshold of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1 for inclusion in the multivariate logistic regression. Subsequently, a model was developed based on minimum AIC (AIC = 191.47). The final model incorporated 10 variables: FAB Classification, AML type, AML status, prior_HMAs, chromosomal karyotype, and mutations in \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eIDH\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, and \u003cem\u003eCEBPA\u003c/em\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eModel variables were selected using univariate and multivariate regression based on the AIC minimum criterion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate logistic regression (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.1)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate logistic regression and minimum AIC\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95% CI)\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\u003eOR(95% CI)\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\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eFAB type\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1/M2 *\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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 \u003cp\u003eM4/M5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.394 (0.212–0.731)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.397 (0.166–0.951)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0/M6/M7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.449 (0.156–1.291)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086 (0.015–0.482)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-AML, yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.301 (0.160–0.568)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267 (0.102–0.696)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR/R AML, yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135 (0.062–0.295)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123 (0.039–0.386)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChromosome karyotype\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiploid Karyotype *\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\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\u003eHyperdiploid/polyploid karyotype\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.078 (0.881–4.904)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.517 (0.932–13.263)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse karyotype\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208 (0.099–0.440)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.198 (0.072–0.544)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior_HMAs, yes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.128 (0.057–0.288)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352 (0.111–1.120)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.077\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eGene mutations\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eASXL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.487 (0.258–0.920)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.483 (0.178–1.311)\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCEBPA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.046 (1.614–15.775)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.031 (1.321–37.423)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFLT3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.433 (0.211–0.889)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197 (0.070–0.554)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIDH\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.912 (2.258–10.689)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.879 (1.002–8.270)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNPM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.624 (2.316–13.662)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.144 (1.728–21.843)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePTPN11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.299 (0.081–1.105)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318 (0.035–2.873)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eK/NRAS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.514 (0.269–0.985)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.572 (0.210–1.556)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSF3B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.389 (0.135–1.122)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.968 (0.237–3.945)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTP53\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251 (0.104–0.606)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443 (0.120–1.633)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: *, reference; S-AML, Secondary-AML; R/R AML, Refractory/Relapsed AML; Prior_HMAs, Prior hypomethylating agents exposure; AIC: Akaike Information Criterion.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003e3.4 Establishment, assessment, and validation of the model\u003c/h2\u003e\u003cp\u003eBased on the final model variables, a nomogram incorporating ten predictive factors was established (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). To further assess the performance of the predictive model, we plotted the ROC, calibration, DCA, and clinical impact curves. In addition, we conducted 500 repeated internal validations using the bootstrap method. The AUC of the model's ROC curve was 0.900 (95% CI: 0.860–0.941). The ROC curve of the validation group was plotted with an AUC value of 0.900 (95% CI: 0.852–0.941) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA - B), demonstrating that the model has robust discriminatory ability. The calibration curve of the model was plotted, and the results indicated good correspondence between the predicted probability of the model and the actual occurrence probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The Hosmer-Lemeshow test yielded a chi-square value of 4.82 (\u003cem\u003eP\u003c/em\u003e = 0.849), indicating no significant difference between the predicted and actual probabilities. The DCA curve was used to evaluate the net clinical benefit of the model. The applicable threshold probability range of the DCA for the model in this study was 0-0.98, whereas the internal validation group suggested a range of 0.03–0.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA - B). Within the 0-0.98 threshold probability range, the net clinical benefit of intervention based on the model's predicted probability was higher than that of no intervention (None) and intervention for all patients (All). The corresponding CIC is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we developed and validated a clinical model to predict the sensitivity of Ven-HMAs treatment in AML patients. The model incorporates AML gene mutations at diagnosis and other clinical information, including morphology and cytogenetics, as prediction parameters. The performance of the model was evaluated using the AUC index, calibration curve, DCA, and CIC, demonstrating good discrimination, accuracy, and net clinical benefit. Moreover, internal validation through 500 bootstrap resamplings further confirmed the excellent performance of the model.\u003c/p\u003e\u003cp\u003eVen-HMAs have been approved for the treatment of newly diagnosed elderly or unfit AML patients, marking a significant shift in therapeutic modalities for this subgroup [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In recent years, the majority of efficacy reports on Ven-HMAs in AML have primarily originated from retrospective real-world studies. A systematic literature review revealed considerable variations in CR/CRi rates across hematological centers (14.0–75%), with inconsistent findings on factors influencing therapeutic efficacy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These discrepancies may be attributed to the distinct characteristics of the cohorts, sample sizes, and the treatment attributes of each study. Our study, encompassing 209 patients, reported CR/CRi and ORR rates of 45.6% and 56.5%, respectively, with a 35.4% rate of achieving minimal residual disease negativity [MRD(-)]. These outcomes were similar to those of Feld, Short, and Matthews et al. [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e–\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], but lower than those of Gangat, Chojecki, and Yu et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], possibly due to the high proportion of high-risk patients (67.2%) and R/R AML (27.8%) in our cohort.\u003c/p\u003e\u003cp\u003eThis study further explored the factors that influence the therapeutic effects of Ven-HMAs in AML. Univariate analysis was used to identify several risk factors for CR/CRi. These included specific FAB classification (M1/M2, M4/M5 and M0/M6/M7), S-AML, R/R AML, prior_HMAs, adverse chromosomal karyotypes, and mutations in the \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003eK/NRAS\u003c/em\u003e genes. Conversely, M1/M2 FAB classifications and mutations in \u003cem\u003eIDH\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, and \u003cem\u003eCEBPA\u003c/em\u003e were associated with higher CR/CRi rates, consistent with previous studies [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR37 CR38\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Subsequent multivariate analysis refined these findings, revealing that the \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, and \u003cem\u003eK/NRAS\u003c/em\u003e mutations did not significantly influence the CR/CRi rate. This study corroborates previous findings regarding the resistance of AML cells with monocytic differentiation to Ven. Additionally, it supports recent studies suggesting that AML with erythroid/megakaryocytic differentiation is also resistant to Ven. This resistance is proposed to be related to the high expression of Bcl-XL, which differs from the monocytic resistance mechanism [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The variables screened by multivariate regression analysis served as the foundation for constructing a model of sensitivity to Ven-HMAs treatment in patients with AML.\u003c/p\u003e\u003cp\u003eWe employed multivariate regression to identify eight statistically significant variables. Subsequently, prior_HMAs and \u003cem\u003eASXL1\u003c/em\u003e mutations were incorporated into the final model, following the AIC minimum optimization model. However, the influence of \u003cem\u003eASXL1\u003c/em\u003e mutations on remission rates remains controversial. While Johnson and Gangat et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] argued that \u003cem\u003eASXL1\u003c/em\u003e mutation is a protective factor against CR/CRi, Winters et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] presented contradictory findings. Our research suggests that Prior_HMAs and \u003cem\u003eASXL1\u003c/em\u003e mutations tend to lower the CR/CRi rate. The prediction model for Ven-HMAs treatment sensitivity in AML established in this study can estimate the probability of achieving CR/CRi in patients with AML based on their clinical characteristics. The AUC value of the model was relatively high, and the calibration curve revealed strong concordance between the predicted probabilities and actual event occurrence rates, indicating a considerable probability of accurately differentiating between sensitive and insensitive patients. Clinical net benefit evaluation using the DCA curve showed that adopting Ven-HMAs treatment within the threshold probability range of 0 to 0.98 yields clinical net benefits. The CIC curve further indicated good consistency between the predicted model probabilities and the actual occurrence probabilities when the risk threshold exceeded 0.6. Considering the current sample size limitations, we opted for internal validation using the bootstrap method, rather than data splitting [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The results suggest a robust model performance. Zong et al. established a resistance prediction model for Ven-HMAs in AML treatment incorporating four variables: FAB-M5 type, S-AML, \u003cem\u003eFTL3-ITD\u003c/em\u003e, and \u003cem\u003eRUNX1-RUNX1T1\u003c/em\u003e [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This contrasts our \"sensitivity\" model. While the first three variables were common to both studies, our research did not identify \u003cem\u003eRUNX1-RUNX1T1\u003c/em\u003e as a predictor of CR/CRi. Our model demonstrated superior AUC performance, indicating higher discriminatory power. A comprehensive comparison of DCA between the two studies requires further investigation.\u003c/p\u003e\u003cp\u003eIn summary, this study constructed and validated a prediction model for the sensitivity of Ven-HMAs to AML treatment. The model aims to assist clinicians in accurately identifying patients with AML likely to benefit from Ven-HMAs treatment, potentially improving patient outcomes and resource allocation.\u003c/p\u003e\u003cp\u003eHowever, this study has several limitations that warrant consideration. First, this prediction model is based on a retrospective study, which might limit the level of evidence and require prospective studies to corroborate these findings. Second, the validation of this prediction model is internal, which to a certain extent may restrict its generalizability and applicability. External validation is necessary to further verify the predictive ability of the model. Third, as a prediction model, this study required an expansion of the sample size. Although efforts were made to include a substantial number of samples, this study would benefit from a larger dataset to optimize the model's performance and draw more robust conclusions.\u003c/p\u003e\u003cp\u003eFuture research directions should focus on conducting prospective studies to validate the model's predictive accuracy, performing external validation across diverse patient populations and clinical settings, expanding the sample size to enhance the model's reliability and generalizability, and incorporating emerging biomarkers or genetic factors that may improve the model's predictive power. These efforts will contribute to refining the prediction model and enhancing its clinical utility in guiding Ven-HMAs treatment decisions for patients with AML.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we developed and validated a clinical model to predict the sensitivity of elderly or unfit AML patients to combination therapy with Ven-HMAs. This model demonstrates excellent discriminatory power, calibration, and clinical net benefit, potentially enhancing the precision of Ven-HMAs treatment in AML. This can potentially optimize treatment decisions, improve patient outcomes, and advance personalized medicine in this challenging-to-treat population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eInformed Consent Statement:\u003c/h2\u003e \u003cp\u003e Written informed consent was obtained from the patient for the publication of this paper.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis study was supported by grants from the Science and Technology Innovation Foundation of Dalian (No. 2024JJ13PT069).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. D. undertook data collection, data analysis, patient follow-up, and manuscript drafting; C. L. was accountable for data analysis and verification; Y. C. was responsible for data collection and verification; F. X. and J. Y. were responsible for supervision, review, and editing. All authors have read and consented to the publication version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe expressed our gratitude to Bullet Edits for the linguistic refinement of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data provided in this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDiNardo CD, Erba HP, Freeman SD, Wei AH. Acute myeloid leukaemia. Lancet. 2023;401(10393):2073\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)00108-3\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)00108-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAcute Myeloid Leukemia-Cancer Stat Facts. SEER. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://seer.cancer.gov/statf-acts/html/amyl.html\u003c/span\u003e\u003cspan address=\"https://seer.cancer.gov/statf-acts/html/amyl.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantarjian H, Kadia T, DiNardo C, Daver N, Borthakur G, Jabbour E, et al. Acute myeloid leukemia: current progress and future directions. Blood Cancer J. 2021;11(2):41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41408-021-00425-3\u003c/span\u003e\u003cspan address=\"10.1038/s41408-021-00425-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki K, Ravandi F, Kadia TM, DiNardo CD, Short NJ, Borthakur G, et al. De novo acute myeloid leukemia: A population-based study of outcome in the United States based on the Sur-veillance, Epidemiology, and End Results (SEER) database, 1980 to 2017. Cancer. 2021;127(12):2049\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.33458\u003c/span\u003e\u003cspan address=\"10.1002/cncr.33458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnett AK, Milligan D, Prentice AG, Goldstone AH, McMullin MF, Hills RK, et al. A comparison of low-dose cytarabine and hydroxyurea with or without all-trans retinoic acid for acute myeloid leukemia and high-risk myelodysplastic syndrome in patients not considered fit for intensive treatment. Cancer. 2007;109(6):1114\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.22496\u003c/span\u003e\u003cspan address=\"10.1002/cncr.22496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalati C, Dhulipala VC, Extermann MT, Ali NA, Kim J, Komrokji R, et al. Comparisons of commonly used front-line regimens on survival outcomes in patients aged 70 years and older with acute myeloid leukemia. Haematologica. 2020;105(2):398\u0026ndash;406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3324/haematol.2018.208637\u003c/span\u003e\u003cspan address=\"10.3324/haematol.2018.208637\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDombret H, Seymour JF, Butrym A, Wierzbowska A, Selleslag D, Jang JH, et al. International phase 3 study of azacitidine vs conventional care regimens in older patients with newly diagnosed AML with \u0026gt;\u0026thinsp;30% blasts. Blood. 2015;126(3):291\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2015-01-621664\u003c/span\u003e\u003cspan address=\"10.1182/blood-2015-01-621664\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantarjian HM, Thomas XG, Dmoszynska A, Wierzbowska A, Mazur G, Mayer J, et al. Multicenter, randomized, open-label, phase III trial of decitabine versus patient choice, with physician advice, of either supportive care or low-dose cytarabine for the treatment of older patients with newly diagnosed acute myeloid leukemia. J Clin Oncol. 2012;30(21):2670\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1200/JCO.2011.38.9429\u003c/span\u003e\u003cspan address=\"10.1200/JCO.2011.38.9429\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDargenio M, Tarantini G, Cascavilla N, Pavone E, Musto P, Mazza P, et al. Long-Term Follow-Up of Elderly Patients with Acute Myeloid Leukemia Treated with Decitabine: A Real World Study of the Apulian Hematological Network. Cancers (Basel). 2022;14(3):826. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14030826\u003c/span\u003e\u003cspan address=\"10.3390/cancers14030826\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGadaud N, Leroy H, B\u0026eacute;rard E, Tavitian S, Leguay T, Dimicoli-Salazar S, et al. Azacitidine, intensive chemotherapy or best supportive care in relapsed or refractory acute myeloid leukemia, a DATAML registry study. Leuk Lymphoma. 2022;63(6):1398\u0026ndash;406. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10428194.2021.2022140\u003c/span\u003e\u003cspan address=\"10.1080/10428194.2021.2022140\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts AW. Therapeutic development and current uses of BCL-2 inhibition. Hematol Am Soc Hematol Educ Program. 2020;2020(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/hematology.2020000154\u003c/span\u003e\u003cspan address=\"10.1182/hematology.2020000154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePfeffer CM, Singh ATK, Apoptosis. A Target for Anticancer Therapy. Int J Mol Sci. 2018;19:448. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms19020448\u003c/span\u003e\u003cspan address=\"10.3390/ijms19020448\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcBride A, Houtmann S, Wilde L, Vigil C, Eischen CM, Kasner M, et al. The Role of Inhibition of Apoptosis in Acute Leukemias and Myelodysplastic Syndrome. Front Oncol. 2019;9:192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2019.00192\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2019.00192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouers AJ, Leverson JD, Boghaert ER, Ackler SL, Catron ND, Chen J, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med. 2013;19(2):202\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nm.3048\u003c/span\u003e\u003cspan address=\"10.1038/nm.3048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKonopleva M, Pollyea DA, Potluri J, Chyla B, Hogdal L, Busman T, et al. Efficacy and Biological Correlates of Response in a Phase II Study of Venetoclax Monotherapy in Patients with Acute Myelogenous Leukemia. Cancer Discov. 2016;6(10):1106\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2159-8290\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo CD, Pratz K, Pullarkat V, Jonas BA, Arellano M, Becker PS, et al. Venetoclax combined with decitabine or azacitidine in treatment-naive, elderly patients with acute myeloid leukemia. Blood. 2019;133(1):7\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2018-08-868752\u003c/span\u003e\u003cspan address=\"10.1182/blood-2018-08-868752\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo CD, Jonas BA, Pullarkat V, Thirman MJ, Garcia JS, Wei AH, et al. Azacitidine and Venetoclax in Previously Untreated Acute Myeloid Leukemia. N Engl J Med. 2020;383(7):617\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2012971\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2012971\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiNardo CD, Tiong IS, Quaglieri A, MacRaild S, Loghavi S, Brown FC, et al. Molecular patterns of response and treatment failure after frontline venetoclax combinations in older patients with AML. Blood. 2020;135(11):791\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood.2019003988\u003c/span\u003e\u003cspan address=\"10.1182/blood.2019003988\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenugopal S, Maiti A, DiNardo CD, Qiao W, Ning J, Loghavi S, et al. Prognostic impact of conventional cytogenetics in acute myeloid leukemia treated with venetoclax and decitabine. Leuk Lymphoma. 2021;62(14):3501\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10428194.2021.1973675\u003c/span\u003e\u003cspan address=\"10.1080/10428194.2021.1973675\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAldoss I, Yang D, Aribi A, Ali H, Sandhu K, Al Malki MM, et al. Efficacy of the combination of venetoclax and hypomethylating agents in relapsed/refractory acute myeloid leukemia. Haematologica. 2018;103(9):e404\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3324/haematol.2018.188094\u003c/span\u003e\u003cspan address=\"10.3324/haematol.2018.188094\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAldoss I, Yang D, Pillai R, Sanchez JF, Mei M, Aribi A, et al. Association of leukemia genetics with response to venetoclax and hypomethylating agents in relapsed/refractory acute myeloid leukemia. Am J Hematol. 2019;94(10):E253\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.25567\u003c/span\u003e\u003cspan address=\"10.1002/ajh.25567\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLabrador J, Saiz-Rodr\u0026iacute;guez M, de Miguel D, de Laiglesia A, Rodr\u0026iacute;guez-Medina C, Vidriales MB, et al. Use of Venetoclax in Patients with Relapsed or Refractory Acute Myeloid Leukemia: The PETHEMA Registry Experience. Cancers (Basel). 2022;14(7):1734. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers14071734\u003c/span\u003e\u003cspan address=\"10.3390/cancers14071734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZong L, Yin M, Kong J, Zhang J, Song B, Zhu J, et al. Development of a scoring system for predicting primary resistance to venetoclax plus hypomethylating agents (HMAs) in acute myeloid leukemia patients. Mol Carcinog. 2023;62(10):1572\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/mc.23600\u003c/span\u003e\u003cspan address=\"10.1002/mc.23600\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArber DA, Orazi A, Hasserjian R, Thiele J, Borowitz MJ, Le Beau MM, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood. 2016;127(20):2391\u0026ndash;405. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2016-03-643544\u003c/span\u003e\u003cspan address=\"10.1182/blood-2016-03-643544\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026ouml;hner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood.2022016867\u003c/span\u003e\u003cspan address=\"10.1182/blood.2022016867\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzanza JR, Mensa J, Barber\u0026aacute;n J, V\u0026aacute;zquez L, P\u0026eacute;rez de Oteyza J, Kwon M, et al. Recommendations on the use of azole antifungals in hematology-oncology patients. Rev Esp Quimioter. 2023;36(3):236\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.37201/req/013.2023\u003c/span\u003e\u003cspan address=\"10.37201/req/013.2023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlsouqi A, Geramita E, Im A. Treatment of Acute Myeloid Leukemia in Older Adults. Cancers (Basel). 2023;15(22):5409. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cancers15225409\u003c/span\u003e\u003cspan address=\"10.3390/cancers15225409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirgh S, Sharma A, Shaikh MRMA, Kadian K, Agrawal N, Khushoo V, et al. Hypomethylating agents\u0026thinsp;+\u0026thinsp;venetoclax induction therapy in acute myeloid leukemia unfit for intensive chemotherapy novel avenues for lesser venetoclax duration and patients with baseline infections from a developing country. Am J Blood Res. 2021;11(3):290\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOtoukesh S, Zhang J, Nakamura R, Stein AS, Forman SJ, Marcucci G, et al. The efficacy of venetoclax and hypomethylating agents in acute myeloid leukemia with extramedullary involvement. Leuk Lymphoma. 2020;61(8):2020\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/10428194.2020.1742908\u003c/span\u003e\u003cspan address=\"10.1080/10428194.2020.1742908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByrne M, Danielson N, Sengsayadeth S, Rasche A, Culos K, Gatwood K, et al. The use of venetoclax-based salvage therapy for post-hematopoietic cell transplantation relapse of acute myeloid leukemia. Am J Hematol. 2020;95(9):1006\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.25859\u003c/span\u003e\u003cspan address=\"10.1002/ajh.25859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeld J, Tremblay D, Dougherty M, Czaplinska T, Sanchez G, Brady C, et al. Safety and Efficacy: Clinical Experience of Venetoclax in Combination With Hypomethylating Agents in Both Newly Diagnosed and Relapsed/Refractory Advanced Myeloid Malignancies. Hemasphere. 2021;5(4):e549. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/HS9.0000000000000549\u003c/span\u003e\u003cspan address=\"10.1097/HS9.0000000000000549\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShort NJ, Venugopal S, Qiao W, Kadia TM, Ravandi F, Macaron W, et al. Impact of frontline treatment approach on outcomes in patients with secondary AML with prior hypomethylating agent exposure. J Hematol Oncol. 2022;15(1):12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-022-01229-z\u003c/span\u003e\u003cspan address=\"10.1186/s13045-022-01229-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews AH, Perl AE, Luger SM, Loren AW, Gill SI, Porter DL, et al. Real-world effectiveness of CPX-351 vs venetoclax and azacitidine in acute myeloid leukemia. Blood Adv. 2022;6(13):3997\u0026ndash;4005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/bloodadvances.2022007265\u003c/span\u003e\u003cspan address=\"10.1182/bloodadvances.2022007265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGangat N, Johnson I, McCullough K, Farrukh F, Al-Kali A, Alkhateeb H, et al. Molecular predictors of response to venetoclax plus hypomethylating agent in treatment-na\u0026iuml;ve acute myeloid leukemia. Haematologica. 2022;107(10):2501\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3324/hae-matol.2022.281214\u003c/span\u003e\u003cspan address=\"10.3324/hae-matol.2022.281214\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChojecki AL, Arnall J, Boselli D, Patel R, Chiad Z, DiSogra KY, et al. Outcomes and hospitalization patterns of patients with acute myelogenous leukemia treated with frontline CPX-351 or HMA/venetoclax. Leuk Res. 2022;119:106904. 10.1016/j. leukres.2022.106904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinters AC, Gutman JA, Purev E, Nakic M, Tobin J, Chase S, et al. Real-world experience of venetoclax with azacitidine for untreated patients with acute myeloid leukemia. Blood Adv. 2019;3(20):2911\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/bloodadvances.2019000243\u003c/span\u003e\u003cspan address=\"10.1182/bloodadvances.2019000243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePei S, Pollyea DA, Gustafson A, Stevens BM, Minhajuddin M, Fu R, et al. Monocytic Subclones Confer Resistance to Venetoclax-Based Therapy in Patients with Acute Myeloid Leukemia. Cancer Discov. 2020;10(4):536\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2159-8290.CD-19-0710\u003c/span\u003e\u003cspan address=\"10.1158/2159-8290.CD-19-0710\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao L, Yang J, Chen M, Xiang X, Ma H, Niu T, et al. Myelomonocytic and monocytic acute myeloid leukemia demonstrate comparable poor outcomes with venetoclax-based treatment: a monocentric real-world study. Ann Hematol. 2024;103(4):1197\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00277-024-05646-7\u003c/span\u003e\u003cspan address=\"10.1007/s00277-024-05646-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorsia E, McCullough K, Joshi M, Cook J, Alkhateeb HB, Al-Kali A, et al. Venetoclax and hypomethylating agents in acute myeloid leukemia: Mayo Clinic series on 86 patients. Am J Hematol. 2020;95(12):1511\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ajh.25978\u003c/span\u003e\u003cspan address=\"10.1002/ajh.25978\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson IM, Ilyas R, McCullough K, Al-Kali A, Alkhateeb HB, Begna K, et al. Molecular Predictors of Response and Survival in Patients with Relapsed/Refractory Acute Myeloid Leukemia Following Venetoclax Plus Hypomethylating Agent Therapy. Blood. 2022;140(Suppl 1):3233\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2022-167967\u003c/span\u003e\u003cspan address=\"10.1182/blood-2022-167967\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ. 2024;386:e078276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj-2023-078276\u003c/span\u003e\u003cspan address=\"10.1136/bmj-2023-078276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute myeloid leukemia, Bcl-2 inhibitor, Hypomethylating agents, Nomogram, Sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-5949741/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5949741/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to develop a clinical prediction model for sensitivity to Bcl-2 inhibitors combined with hypomethylating agents (HMAs) in elderly/unfit patients with acute myeloid leukemia (AML).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical data, including French-American-British (FAB) classification, chromosomal karyotype, and second-generation sequencing results, were retrospectively collected from consecutive elderly/unfit patients with AML treated with Bcl-2 inhibitors in combination with HMAs between September 2019 and March 2024. Treatment efficacy was assessed in all patients. Logistic regression and Akaike information criterion were used to identify risk variables affecting efficacy. A nomogram was developed based on these variables to assess patient sensitivity to the treatment regimen. The performance of the nomogram was evaluated using a receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study included 209 patients with AML. The FAB classification, AML type, AML status, prior HMAs exposure, chromosomal karyotype, and mutations in \u003cem\u003eASXL1\u003c/em\u003e, \u003cem\u003eFLT3\u003c/em\u003e, \u003cem\u003eIDH\u003c/em\u003e, \u003cem\u003eNPM1\u003c/em\u003e, and \u003cem\u003eCEBPA\u003c/em\u003e were screened to develop the nomogram. The area under the ROC curve indicated a discriminatory power of 0.900 (95% CI, 0.860\u0026ndash;0.941). The calibration curve suggested favorable concordance between the predicted and actual occurrence probabilities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.849). DCA revealed a net clinical benefit when the threshold probability ranged from 0 to 0.98. Internal validation, performed 500 times using the bootstrap method, demonstrated a satisfactory model performance in the validation set.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA prediction model was developed and validated to serve as a decision-making tool for physicians treating elderly/unfit patients with AML prior to initiating therapy with Bcl-2 inhibitors combined with HMAs.\u003c/p\u003e","manuscriptTitle":"Development of a clinical prediction model for sensitivity to combination therapy of Bcl-2 inhibitors and hypomethylating agents in elderly/unfit patients with acute myeloid leukemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 12:04:40","doi":"10.21203/rs.3.rs-5949741/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-18T09:56:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-16T14:16:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236474258875558344997974971901929712596","date":"2025-03-10T21:26:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159321724596356517969799974322802637013","date":"2025-03-10T15:24:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-20T14:38:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172069131004453256536601795560954261258","date":"2025-02-18T07:45:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-17T22:23:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-14T08:33:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-12T11:41:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-02-03T09:00:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cbfd93c9-ea88-409f-93e5-05a660e488b0","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T13:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 12:04:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5949741","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5949741","identity":"rs-5949741","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}