Establishment and validation of a non-relapse mortality risk prediction model for high-risk and refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell

Establishment and validation of a non-relapse mortality risk prediction model for high-risk and refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell | 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 Establishment and validation of a non-relapse mortality risk prediction model for high-risk and refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell Minglu Li, Weijie Zhang, Xinhong Fei, Shuqin Zhang, Jie Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6212386/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to identify risk factors for non-relapse mortality following allogeneic hematopoietic stem cell transplantation (HSCT) in patients with high-risk refractory acute leukemia and to develop a predictive model. Clinical data and laboratory findings of 966 patients who underwent HSCT at the Aerospace Central Hospital from January 2015 to December 2021 were collected retrospectively, with 595 patients selected as study subjects. Univariate analysis using competing risk models identified potential risk factors, and cumulative incidence curves were plotted. The data were divided into a training set (n = 417) and a validation set (n = 178) at a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (Lasso) regression was used to identify predictors of non-relapse mortality, and a predictive model was constructed using competing risk analysis. The model was internally validated in the validation set. A nomogram was developed for visualization, and its performance was assessed. Predictors included age (< 18, 18–45, ≥ 45 years), pre-transplant status (CR/Non CR), extramedullary infiltration (No/Yes), HCT score (0, 1–2, ≥ 3), neutrophil engraftment time (< 16, ≥ 16 days), platelet engraftment time (< 16, ≥ 16 days), and aGVHD (0-I degree/II-IV degree). The nomogram accurately predicted 1-, 2-, and 3-year non-relapse mortality with AUCs of 0.839 (95% CI: 0.793–0.884), 0.802 (95% CI: 0.75–0.854), and 0.753 (95% CI: 0.691–0.815), respectively. Time-dependent ROC, time-dependent AUC, and calibration curves confirmed the model's discriminative power and precision. This nomogram may assist clinicians in assessing patient prognosis and devising personalized treatment plans. Non-relapse mortality High-risk refractory acute leukemia Least absolute shrinkage and selection operator Competing risks model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Since the first successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) in the world, the development of allo-HSCT in the world has spanned over 50 years. Studies have shown that the survival rate of leukemia patients has significantly improved by adopting new standards and treatment strategies, such as high-dose chemotherapy, demethylating agents, small molecule targeted drugs, and immune targeted drugs, while allo-HSCT remains a key method for achieving long-term survival. With the improvement of pre-treatment regimens, advancements in acute graft-versus-host disease (GVHD) treatment, and the expansion of donor selection criteria, an increasing number of leukemia patients are experiencing the benefits of allo-HSCT. However, complications associated with allo-HSCT, such as infection, graft-versus-host disease, and relapse, have had a significant impact on the efficacy of allo-HSCT and the quality of life of patients. Studies indicate that 20–32% of patients die from non-relapse mortality (NRM), mainly due to severe infections, organ failure, and graft dysfunction( 1 – 4 ). Through comprehensive literature review and clinical investigation, age( 5 ), hematopoietic cell transplantation-specific comorbidity index (HCT-CI)( 6 , 7 ), GVHD( 8 ), viral infections, and complications such as hemorrhagic cystitis( 9 ) and Cytomegaovirus (CMV)( 10 ) infection have been identified as important risk factors for non-relapse mortality in acute leukemia patients after allo-HSCT. Although these factors have been recognized, further research is needed to enhance the understanding of the specific correlations of these factors and their impact on patient prognosis. Future studies should focus on developing risk prediction models for non-relapse mortality in refractory acute leukemia patients undergoing allo-HSCT, providing clinicians with more precise assessment tools to improve patient outcomes. 2. Methods 2.1 Patients Clinical data from 966 patients with hematologic diseases who underwent HSCT at the Hematology Department of our hospital between January 1, 2015, and December 31, 2021, were gathered. After screening, 595 patients were chosen for inclusion in the study. The flowchart depicting the screening process is outlined in Fig. 1 . The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Aerospace central Hospital. 2.2 Conditioning Regimen The Conditioning regimen included busulfan (BU, 3.2mg/kg/d, -8d to -6d), total body irradiation (TBI, 400cGy x 3d), cytarabine (AraC, 4g/m², -10d to -9d), cyclophosphamide (Cy, 1.8g/m², -5d to -4d), MECCNU (250mg/m², -3d), and anti-human T-cell immunoglobulin (ATLG, 5mg/kg/day, -5d to -2d). Additional medications were added based on patient condition, including CLAG (cladribine, cytarabine, and G-CSF) or FLAG (fludarabine, cytarabine, and G-CSF) regimens, teniposide, idarubicin, decitabine, and their combinations. 2.3 Definitions and Assessments Diagnosis of leukemia followed the 2016 World Health Organization (WHO) classification criteria( 11 ).Diagnosis and grading of acute and chronic graft-versus-host disease (GVHD) followed the Glucksberg and MAGIC criteria, and the NIH scoring system, respectively( 12 , 13 ).Engraftment criteria included neutrophil count > 0.5×10⁹/L for three consecutive days and platelet count > 20×10⁹/L for seven consecutive days without transfusion( 14 ). Relapse was defined as the presence of leukemia cells or bone marrow blasts ≥ 5% in peripheral blood or bone marrow in patients who achieved complete remission post-transplant( 15 ).Non-relapse mortality referred to deaths unrelated to disease relapse, such as transplant-related complications. Transplant conditioning intensity (TCI) score was determined according to previous literature( 16 ). The primary endpoint was non-relapse mortality with relapse as a competing risk event. Survival time was calculated from the date of HSCT to death or last follow-up. Participants with unknown cause of death, unknown survival time, or zero survival time were excluded. Participants alive at the last follow-up were censored. 2.4 Statistical Analysis Descriptive statistics were used for variable comparison. Non-normally distributed data were presented as median and interquartile range. Univariate analysis was performed using chi-square or Fisher's exact test for categorical variables and t-test or rank-sum test for continuous variables. Significance level was set at P < 0.05. Optimal cutoff points for continuous variables were determined using KM curves. In the training set, competing risk models were used for univariate analysis, and significant variables were selected for further analysis with cumulative incidence function (CIF) curves and Fine-Gray tests. Lasso regression combined with cross-validation was used for predictor selection. Based on Lasso regression, univariate competing risk analysis, variables were screened, and a competing risk model was constructed. Nomograms were generated in the training sets. Time-dependent Receiver operating characteristic (ROC) curves and the areas under the curves (AUCs), as well as calibration curves, were used to evaluate model performance. Statistical analyses were conducted using R software version 4.32. 3. Results 3.1 Baseline Characteristics The baseline characteristic table (Table 1 ) provides important information on the demographic and clinical features of the study population. Table 1 Patient demographics and baseline characteristics. Characteristic Status p-value 2 Censored, N = 300 1 NRM, N = 171 1 Replase, N = 124 1 Age 29 (17, 41) 34 (27, 44) 28 (18, 39) < 0.001 Sex 0.721 Male 182 (61%) 101 (59%) 70 (56%) Female 118 (39%) 70 (41%) 54 (44%) Underlying.disease 0.745 AML 223 (74%) 131 (77%) 90 (73%) T-ALL 23 (8%) 14 (8%) 11 (9%) B-ALL 45 (15%) 20 (12%) 19 (15%) MAPL 9 (3%) 6 (4%) 4 (3%) Pre.transplantation.status < 0.001 CR 200 (67%) 69 (40%) 28 (23%) Non CR 100 (33%) 102 (60%) 96 (77%) CNSL 0.182 No 280 (93%) 156 (91%) 109 (88%) Yes 20 (7%) 15 (9%) 15 (12%) Extramedullary.leukemia < 0.001 No 268 (89%) 108 (63%) 86 (69%) Yes 32 (11%) 63 (37%) 38 (31%) HCT.CI.scores < 0.001 Low-risk 67 (22%) 21 (12%) 6 (5%) Intermediate-risk 222 (74%) 124 (73%) 100 (81%) High-risk 11 (4%) 26 (15%) 18 (15%) Donor.age 36 (27, 45) 36 (28, 46) 39 (26, 46) 0.853 Donor.recipient.sex.matched 0.498 Male-male 144 (48%) 78 (46%) 56 (45%) Male-female 92 (31%) 52 (30%) 34 (27%) Female-male 38 (13%) 23 (13%) 14 (11%) Female-female 26 (9%) 18 (11%) 20 (16%) Doner.type 0.005 MSD 52 (17%) 31 (18%) 16 (13%) HFD 224 (75%) 126 (74%) 101 (81%) URD 20 (7%) 3 (2%) 6 (5%) UCB 4 (1%) 11 (6%) 1 (1%) Donor.recipient.blood.type.matched 0.102 Matched 158 (53%) 91 (53%) 74 (60%) Major dismatched 73 (24%) 39 (23%) 14 (11%) Minor dismatched 51 (17%) 27 (16%) 27 (22%) Bidirectional 18 (6%) 14 (8%) 9 (7%) Conditioning.regimen.with.ATG 0.032 No 55 (18%) 47 (27%) 21 (17%) Yes 245 (82%) 124 (73%) 103 (83%) TCI 2.00 (2.00, 2.50) 2.00 (2.00, 2.00) 2.00 (1.50, 2.00) 0.119 Stem.cell.source 0.879 BM + PB 187 (62%) 119 (70%) 94 (76%) PB 109 (36%) 41 (24%) 29 (23%) UCB 4 (1%) 11 (6%) 1 (1%) With.UCB 0.456 No 97 (32%) 46 (27%) 39 (31%) Yes 203 (68%) 125 (73%) 85 (69%) MNC.cell.dose.count 9.11 (8.59, 9.70) 9.10 (8.66, 9.60) 9.08 (8.80, 9.50) 0.975 CD34..cell.dose.count 4.18 (2.75, 5.94) 3.38 (2.10, 5.20) 3.56 (2.61, 5.11) 0.001 WBC.engraftment.days 13.0 (11.0, 15.0) 15.0 (12.0, 18.0) 13.5 (12.0, 17.0) < 0.001 PLT.engraftment.days 13 ( 12 , 17 ) 19 ( 13 , 30 ) 16 ( 12 , 23 ) < 0.001 CMV.infections 0.018 No 41 (14%) 9 (5%) 14 (11%) Yes 259 (86%) 162 (95%) 110 (89%) EBV.infections 0.427 No 202 (67%) 105 (61%) 80 (65%) Yes 98 (33%) 66 (39%) 44 (35%) Hemorrhagic.cystitis 0.042 No 69 (23%) 26 (15%) 18 (15%) Yes 231 (77%) 145 (85%) 106 (85%) aGVHD < 0.001 0_1 257 (86%) 77 (45%) 89 (72%) 2_4 43 (14%) 94 (55%) 35 (28%) cGVHD 0.341 No 20 (7%) 7 (4%) 10 (8%) Yes 280 (93%) 164 (96%) 114 (92%) 1 Median (IQR); n (%) 2 Kruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's exact test AML: Acute Myeloid Leukemia, ALL: Acute Lymphoblastic Leukemia, MAPL: Mixed Phenotype Acute Leukemia, CNSL: Central Nervous System Leukemia, HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, MSD: Matched Sibling Donor, HFD: Haploidentical Family Donor, URD: Unrelated Donor, UCB: Umbilical Cord Blood, ATG: Anti-Thymocyte Globulin, TCI: Transplant Conditioning Intensity, BM: Bone Marrow, PB: Peripheral Blood, MNC: Mononuclear Cells, WBC: White Blood Cell, PLT: Platelet, CMV: Cytomegalovirus, EBV: Epstein-Barr Virus, GVHD: Graft-versus-Host Disease The 595 patients were randomly divided into a training cohort (70%, n = 417) and a validation cohort (30%, n = 178). By comparison, we found that there were no statistically significant differences (p > 0.05) between the two groups for all variables included. This indicates that our data allocation was both random and reasonable. The specific grouping details are presented in the supplement documents (Table S1 ). 3.2 Optimal Cutoff Value Determination Using the KM Curve To ascertain the prognostic significance of specific continuous variables more accurately, we utilized the Kaplan-Meier curve to stratify these variables, thereby identifying the optimal cutoff points that would enhance the discriminatory power of prognostic outcomes. Notably, the cutoff values derived from the sample data may diverge from those commonly employed in clinical practice. The graphical representation of the optimal cutoff values is presented in the supplement documents (Figure S1 ). 3.3 Univariate Competing Risk Analysis To explore the determinants of non-relapse mortality (NRM), we conducted univariate competitive risk analysis using a competing risks model, aiming to identify variables significantly associated with NRM (Table 2 ).In this context, relapse was considered a competing event, and a significant correlation between NRM and the following variables was observed: age (18–45 years vs. ≥45 years), pre-transplant condition, extramedullary infiltration, HCT score ≥ 3, umbilical cord blood donor type, pre-treatment regimen including ATG, stem cell source (umbilical cord blood), days to WBC engraftment ≥ 16, days to PLT engraftment ≥ 16, and GVHD. To further clarify the impact of these significant variables on relapse and NRM, CIF curves were plotted and Fine-Gray tests were conducted.The details is presented in the supplement documents (Figure S2,S3;Table S2,S3). Table 2 The Results of Univariate Competing Risk Analysis. Characteristic N Event N HR 1 95% CI 1 p-value Sex Male 257 76 — — Female 160 43 1.17 0.82, 1.68 0.39 Age,group < 18 94 15 — — 18–45 239 77 3.04 1.65,5.61 < 0.001 ≥ 45 84 27 2.33 1.36,3.98 0.002 Underlying.disease AML 311 92 — — T-ALL 35 10 0.89 0.43,1.84 0.75 B-ALL 57 14 0.65 0.35,1.21 0.17 MAPL 14 3 1.28 0.51,3.23 0.6 Pre.transplantation.status CR 214 53 — — Non CR 203 66 1.7 1.17,2.47 0.005 CNSL No 379 107 — — Yes 38 12 1.01 0.55,1.87 0.97 Extramedullary.leukemia No 324 74 — — Yes 93 45 2.16 1.48,3.15 < 0.001 HCT.CI.scores Low-risk 70 17 — — Intermediate-risk 310 83 1.25 0.72,2.19 0.42 High-risk 37 19 2.32 1.13,4.73 0.021 Donor.recipient.sex.matched Male-male 203 59 — — Male-female 116 35 1.16 0.76,1.77 0.48 Female-male 54 17 0.99 0.55,1.77 0.97 Female-female 44 8 1.19 0.66,2.13 0.56 Donor.age,group < 18 51 16 — — 18–45 265 78 1.47 0.77,2.8 0.24 ≥ 45 101 25 1.65 0.83,3.3 0.15 Doner.type MSD 71 21 — — HFD 309 85 0.93 0.59,1.46 0.75 UCB 15 10 2.53 1.07,5.96 0.035 URD 22 3 0.52 0.15,1.78 0.29 Donor.recipient.blood.type.matched Matched 230 62 — — Major dismatched 89 29 1.14 0.72,1.79 0.57 Minor dismatched 69 19 0.83 0.5,1.4 0.49 Bidirectional 29 9 1.17 0.61,2.24 0.65 TCI 417 119 1.05 0.74,1.48 0.79 Conditioning.regimen.with.ATG No 90 35 — — Yes 327 84 0.67 0.45,1 0.047 Stem.cell.source BM + PB 271 78 — — PB 131 31 0.83 0.53,1.28 0.39 UCB 15 10 2.6 1.18,5.75 0.018 With.UCB No 136 33 — — Yes 281 86 1.07 0.72,1.58 0.75 MNC.cell.dose.count,group < 10 356 101 — — ≥ 10 61 18 1.1 0.6,2.01 0.75 CD34..cell.dose.count,group < 2 64 28 — — ≥ 2 353 91 0.82 0.57,1.17 0.27 WBC.engraftment.days,group < 16 277 71 — — ≥ 16 140 48 2.12 1.45,3.11 < 0.001 PLT.engraftment.days,group < 16 229 48 — — ≥ 16 188 71 2.34 1.62,3.38 < 0.001 CMV.infections No 49 7 — — Yes 368 112 1.78 0.82,3.85 0.14 EBV.infections No 281 74 — — Yes 136 45 0.91 0.62,1.32 0.62 Hemorrhagic.cystitis No 85 20 — — Yes 332 99 1.03 0.81, 2.13 0.92 aGVHD 0,1 297 54 — — 2,4 120 65 3.92 2.72,5.65 < 0.001 cGVHD No 27 4 — — Yes 390 115 1.37 0.51,3.62 0.53 1 HR = Hazard Ratio, CI = Confidence Interval AML: Acute Myeloid Leukemia, ALL: Acute Lymphoblastic Leukemia, MAPL: Mixed Phenotype Acute Leukemia, CNSL: Central Nervous System Leukemia, HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, MSD: Matched Sibling Donor, HFD: Haploidentical Family Donor, URD: Unrelated Donor, UCB: Umbilical Cord Blood, ATG: Anti-Thymocyte Globulin, TCI: Transplant Conditioning Intensity, BM: Bone Marrow, PB: Peripheral Blood, MNC: Mononuclear Cells, WBC: White Blood Cell, PLT: Platelet, CMV: Cytomegalovirus, EBV: Epstein-Barr Virus, GVHD: Graft-versus-Host Disease 3.4 Predictor Selection Using Lasso Regression To identify predictors of NRM, we employed Lasso regression analysis, incorporating all variables into the modeling cohort. In pursuit of a more inclusive variable set and enhanced predictive performance, we selected the lambda.min value, achieving the optimal predictive model at lambda.min = 0.008482706 (Fig. 2 ). Incorporating the findings from Lasso regression and univariate competing risk analysis, a multivariate competing risk model was developed, including age, pre-transplantation condition, extramedullary infiltration, HCT score, WBC engraftment days, PLT engraftment days, and acute GVHD as the seven pivotal variables. 3.5 Development of a Competing Risk Regression Model A multivariate competing risk model was constructed using age, pre-transplantation condition, extramedullary infiltration, HCT score, WBC engraftment days, PLT engraftment days, and acute GVHD. The detailed outcomes are delineated in the Table 3 . Table 3 Results of the Multivariate Competitive Risk Model in the Training Set. Cause Variable coef HR 95% CI NRM Age > = 45 1.2466 3.4786 1.80,6.73 Age 18–45 0.8759 2.4010 1.37,4.22 Pre.transplantation.status Non CR 0.2873 1.3329 0.88,2.02 Extramedullary.leukemia Yes 0.2581 1.2945 0.83,2.02 HCT.CI.scores Intermediate-risk -0.7215 0.4860 0.28,0.86 HCT.CI.scores Low-risk -0.8591 0.4235 0.20,0.90 WBC.engraftment.days > = 16 0.4941 1.6391 1.05,2.56 PLT.engraftment.days > = 16 0.3958 1.4856 0.95,2.32 aGVHD 2_4 1.3498 3.8565 2.62,5.67 Relapse Age > = 45 0.00062 1.0006 0.53,1.89 Age 18–45 -0.2304 0.7942 0.48,1.31 Pre.transplantation.status Non CR 1.6607 5.2631 3.00,9.24 Extramedullary.leukemia Yes 0.1124 1.1190 0.68,1.84 HCT.CI.scores Intermediate-risk -1.0764 0.3408 0.19,0.63 HCT.CI.scores Low-risk -1.8206 0.1619 0.06,0.47 WBC.engraftment.days > = 16 -0.3029 0.7387 0.43,1.27 PLT.engraftment.days > = 16 0.3939 1.4827 0.93,2.37 aGVHD 2_4 0.2247 1.2519 0.79,1.99 1 HR = Hazard Ratio, CI = Confidence Interv HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, WBC: White Blood Cell, PLT: Platelet, GVHD: Graft-versus-Host Disease Figures 3.6 Construction of a Nomogram Drawing from the competing risk model outcomes, regression coefficients were translated into scores, which were then summated to predict the probability of NRM at 1, 2, and 3 years, culminating in the creation of a Nomogram (Fig. 3 ). Utilization of the Nomogram involves drawing vertical lines from each variable to its corresponding tick mark to obtain scores, summing these scores, and subsequently drawing a vertical line from the total score to the NRM axis at 1, 2, and 3 years to ascertain the probability of NRM occurrence. 3.7 Model Validation The overall performance of the Nomogram was assessed by validating the model's performance on both the training and validation datasets. We obtained AUC values of 0.839 (95% CI: 0.793–0.884), 0.802 (95% CI: 0.75–0.854), and 0.753 (95% CI: 0.691–0.815) for 1, 2, and 3 years in the training set, respectively, along with C-index values of 0.744, 0.737, and 0.744 for the same time intervals. The discriminative ability of the predictive model was evaluated using time-dependent ROC curves, with the associated time-dependent AUC curves plotted for visual representation. The model demonstrated robust and stable discriminative performance, as evidenced by the time-dependent ROC and AUC curves for both the training and validation datasets (Fig. 4 ). The calibration performance was appraised by the proximity of the calibration curve to the ideal diagonal, indicating the model's accuracy in predicting outcomes (Fig. 5 ). 4. Discussion The retrospective study found that age is an independent risk factor for non-relapse mortality (NRM) after allo-HSCT in high-risk refractory acute leukemia patients, with a highly significant correlation. Eolia Brissot's study showed that patient age (≥ 50 years) is associated with higher NRM( 17 ). This study also confirmed that high-risk refractory acute leukemia patients aged ≥ 45 have a 3.48 times higher risk of NRM compared to patients < 18 years old (95%CI: 1.80–6.73). While some studies have shown an association between increasing age and higher incidence and mortality rates in allo-HSCT( 18 ), current research trends suggest that the correlation with actual age is low, and biological age (indicated by health status and comorbidities) may be a more accurate predictor of NRM( 6 , 7 ). With the aging population and no clear age limit for undergoing allo-HSCT, the number of elderly patients receiving allo-HSCT is expected to rise. Predicting NRM should not be solely based on patient age, but should consider other patient characteristics to develop better follow-up strategies. The study found that grade II-IV aGVHD (HR = 3.86, 95%CI: 2.62–5.67) is an independent predictor of NRM with high significance; however, cGVHD showed no significant correlation. Sheng-Hsuan Chien's study indicated that moderate to severe cGVHD is associated with higher OS or PFS rates. Interestingly, factors significantly associated with NRM are not cGVHD but III-IV grade aGVHD( 19 ). This discrepancy with clinical reality may be because patients with longer survival have more opportunities to develop cGVHD, and some patients may experience NRM before cGVHD occurs. Therefore, preventing and treating GVHD is crucial for allo-HSCT patients to improve survival rates and reduce the risk of related complications. Wang CY et al. analyzed the prognostic significance of HCT-CI score, age, gender, conditioning regimen, pre-transplant disease status, graft source, and HLA matching degree on NRM and overall survival (OS) using a Cox regression model. The results showed that HCT-CI score is an independent risk factor for patients undergoing allogeneic HSCT( 20 ). In large cohort studies, complications evaluated by the HCT-CI score significantly impact NRM( 4 ). Consistent with these findings, our study revealed that an HCT-CI score ≥ 3 is an independent risk factor for NRM in high-risk refractory acute leukemia patients, with HCT-CI 0 points (HR = 0.42, 95%CI: 0.20–0.90) and 1–2 points (HR = 0.49, 95%CI: 0.28–0.86) being protective factors for NRM. The HCT-CI score can serve as a useful tool for predicting the risk of NRM after allogeneic hematopoietic stem cell transplantation, measuring the impact of pre-existing comorbidities on transplant patients. Eolia Brissot's study indicated that CMV-positive status is associated with higher NRM ( 17 ). Junko Takeshita et al. further emphasized the impact of CMV infection on prognosis by evaluating the correlation between CMV viral load area under the curve (CMV-AUC) and the NRM rate in allo-HSCT patients( 10 , 21 – 23 ). Both direct and indirect cytomegalovirus infections increase overall and NRM rates. In the inter-group comparison, we found a significant correlation between CMV infection and the prognosis of high-risk refractory acute leukemia (P = 0.018). However, the univariate competing risk analysis showed no significant correlation between CMV infection and NRM (P = 0.14), suggesting that while CMV may somewhat influence a patient's prognosis, its effect on NRM when considering relapse as a competing risk is limited. Due to the limitations of single-center data, larger sample sizes are needed to further clarify the impact of CMV infection on patient outcomes. Nevertheless, with the use of less toxic non-myeloablative conditioning regimens and intensified post-transplant immunosuppressive therapy, combined with an increasing number of elderly patients undergoing transplants, the risk of opportunistic infections and viral reactivation in the future is expected to rise. Late CMV disease mortality and the indirect effects of CMV remain major barriers to achieving successful long-term outcomes. Although Epstein-Barr virus (EBV) is a triggering factor for various diseases, clinical studies evaluating the impact of EBV serostatus on clinical outcomes in the allo-HCT population are lacking. Kołodziejczak M et al. conducted a meta-analysis to systematically summarize and analyze the impact of donor and recipient EBV serostatus on transplant outcomes in allo-HCT recipients and found that EBV seropositivity in donors and recipients has no significant impact on NRM( 24 ). Our study reached the same conclusion. Wang CY et al. found that pre-transplant disease status is an independent risk factor for patients undergoing allogeneic HSCT( 20 ). In our study, univariate analysis showed that not achieving complete remission before transplantation (HR = 1.7, 95%CI: 1.17–2.47, P = 0.005) is significantly associated with NRM in high-risk refractory acute leukemia patients, but in the multivariate analysis, it was not significant for NRM. In the multivariate competing risk model, not reaching complete remission was a highly significant independent risk factor for relapse (HR = 5.26, 95%CI: 3.00-9.24). Patients with complete remission usually have restored hematopoietic function after transplantation, which helps in rebuilding a healthy immune system post-transplant. This, in turn, reduces the risk of infections and bleeding, improving patient survival. However, its main impact is on relapse and relapse-related death, with a limited effect on NRM. Patients undergoing hematopoietic stem cell transplantation receive a series of radiotherapy and chemotherapy pre-treatments, which may result in damage and inhibition of normal bone marrow cells, making it easier for leukemia cells to spread to extramedullary tissues. After hematopoietic stem cell transplantation, patients receive immunosuppressive therapy to prevent graft-versus-host disease (GVHD) occurrence, potentially weakening the immune system's monitoring and clearance capacity against leukemia cells, leading to extramedullary infiltration. The occurrence of extramedullary infiltration is significant for patient treatment and prognosis. In our study, univariate analysis found that extramedullary infiltration (HR = 2.16, 95%CI: 1.48–3.15, P<0.001) was a significant associated factor for non-relapse mortality in high-risk refractory acute leukemia patients. Delayed engraftment is a significant adverse prognostic factor for patients with high-risk refractory acute leukemia. During white blood cell engraftment delay, the risk of infection increases, temporary loss of hematopoietic function occurs, affecting patient recovery and prolonging the time spent in a state of low immunity. Platelet engraftment delay poses a risk of bleeding for patients, including skin bruising, nosebleeds, and gum bleeding. Moreover, platelet engraftment delay may also increase the risk of infections, attributed to impairment in megakaryocyte maturation in the bone marrow and increased destruction of mature platelets in peripheral blood. Platelet engraftment is influenced by various factors, including transplant type, CD34 + cell infusion quantity, HLA allele compatibility level, severity grading of aGVHD, cytomegalovirus infection, adjustments in immunosuppressive therapy, etc. In our study, white blood cell engraftment time ≥ 16 days (HR = 2.12, 95%CI: 1.45–3.11, P<0.001) and platelet engraftment time ≥ 16 days (HR = 2.34, 95%CI: 1.62–3.38, P<0.001) were associated factors for non-relapse mortality in high-risk refractory acute leukemia patients, with platelet engraftment time ≥ 16 days being an independent risk factor (HR = 1.64, 95%CI: 1.05–2.56). Hence, meticulous management and successful white blood cell and platelet engraftment are essential to reduce the risk of bleeding, enhance quality of life, improve transplant success rates, and conserve medical resources. To achieve these goals, various measures such as increasing platelet engraftment, adjusting immunosuppressive therapy regimens, preventing infections and bleeding, optimizing donor stem cell selection for earlier white blood cell engraftment, and adjusting pre-treatment schemes for high-risk patients could be implemented. Yasuyuki Arai's study demonstrated that the CD4 immune reconstitution after allo-HSCT is associated with a lower NRM rate. They found that reaching CD4 > 50 cells/µL and/or B cells > 25 cells/uL before day 100 post-HSCT predicted reduced NRM( 25 ). David Kliman et al. used a Cox regression model to identify predictive factors associated with increased NRM, including age, male gender, CMV seropositivity, HLA mismatch, and transplantation beyond 6 months after diagnosis( 26 ). The impact of ABO blood type mismatch on outcomes after allogeneic HSCT remains controversial. Ataca Atilla P's study aimed to determine the effect of ABO blood type mismatch on transplant outcomes and complications. The results indicated that ABO blood type mismatch is unrelated to the risks of NRM and GVHD( 27 ). Ciftciler R et al. analyzed allogeneic HSCT patient performance in terms of neutrophil and platelet recovery rates, GVHD, relapse rate, mortality rate, NRM, and survival rates. The study pointed out that ABO mismatch does not seem to significantly affect major outcomes post-allo-HSCT, including GVHD, relapse rate, mortality rate, disease-free survival (DFS), and overall survival (OS). ABO blood type mismatch does not delay platelet and neutrophil engraftment after allo-HSCT( 28 ). Hideki Nakasone et al. found that mixed-sex stem cell transplantation is associated with increased rates of GVHD occurrence and NRM( 29 ). Future research will aim to incorporate more indicators into the model to optimize statistical analysis. In traditional survival analysis, the Cox proportional hazards regression primarily focuses on a single major event, such as death or survival.This analysis assumes that censoring time and failure time are independent, meaning there is no competing risk between outcomes. Competing risk refers to the probability that a known event in the observation cohort may affect the occurrence of another event or completely hinder it. However, clinical survival data often involve multiple outcomes. Competing risk models involve multiple types of events that may compete with each other. For example, when studying non-recurrent death, the occurrence of a relapse in patients can affect the outcome of non-recurrent death. Traditional survival analysis typically indicates the probability of event occurrence and risk factors, while competing risk models show the risk comparison and interaction between different types of events. Applying traditional survival analysis in such cases may lead to an overestimation of the cumulative incidence of events. Therefore, we chose to apply a competing risk model to clinical right-censored data, treating relapse as a competing risk for non-recurrent death in the analysis. In descriptive analysis, the Kaplan-Meier method is commonly used to describe the survival time and survival status of patients in different groups. When studying non-recurrent death, it is important to focus on relapse as a competing event that impacts the outcome. Therefore, cumulative incidence functions (CIF) are used to plot survival analysis curves, and Fine-Gray tests are conducted. This helps provide a clearer and more intuitive understanding of the relationship between variables and relapse and non-recurrent death. Regression analysis is used to reveal quantitative relationships between multiple variables and can be utilized for variable selection and model building. In the variable selection process, significant or strong correlations between variables often arise, resulting in multicollinearity. Multicollinearity can increase the variance of estimated regression coefficients, widen confidence interval ranges, and significantly reduce the accuracy of estimates, affecting the interpretability of the regression equation. To effectively address this issue, using an improved linear regression is a better choice. The Lasso method considers all predictor variables collectively, effectively addressing issues of correlation between newly included variables and previously included variables. In contrast to traditional stepwise regression methods, Lasso can handle all variables simultaneously. By combining Lasso regression with competing risk models, a predictive model with improved performance compared to traditional models can be built, leading to significantly enhanced predictive accuracy. Therefore, based on Lasso regression and competing risk models, we developed and validated a risk prediction model for non-recurrent death in high-risk refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation. In recent years, the Lasso method has gradually been applied in various clinical domains. For example, predicting the recurrence rate of early hepatocellular carcinoma using Lasso-Cox regression and constructing a nomogram evaluation model has proven to accurately determine the recurrence rates of patients at 1, 3, and 5 years, providing substantial clinical utility( 30 ). Overall, Lasso regression, with its unique advantages, can construct clinical prediction models and has played a role in various clinical fields, providing clinicians with reliable tools for disease assessment. Identifying the optimal treatment plan, counseling, and follow-up for leukemia patients, recognizing risk factors, and developing accurate prediction models are crucial for leukemia patients. While many models have been developed using traditional statistical methods and machine learning techniques to predict survival and death risk in leukemia patients, these models have limitations in terms of their applicability and convenience, with varying predictive abilities. We developed and validated a risk prediction model for non-recurrent death in refractory high-risk acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation based on a competing risk model using Lasso regression. Through nomograms, we successfully predicted the one-year, two-year, and three-year non-recurrent death rates of patients with satisfactory predictive performance. In internal validation, we verified the best consistency between the predicted results and observed results. Overall, the study results indicate that the model has high effectiveness. There are several limitations to this study. Firstly, the model is based on single-center retrospective analysis, lacking random assignment, patient selection, and data integrity issues, which may lead to bias. Secondly, the study lacks external validation, limiting the generalizability of the conclusions and the predictive performance of the model. It should be noted that the patients included in our center were severely ill and belonged to the high-risk refractory acute leukemia group. Therefore, when applying the model, attention should be paid to the target population and stratification of characteristics of different population groups. The model is suitable for high-risk refractory acute leukemia patients and is not applicable to standard-risk, chronic leukemia, lymphoma, or myelodysplastic syndrome patients, thus having limitations in extrapolation. It is worth mentioning that most current studies focus on standard-risk acute leukemia patients, and the high-risk factors for non-recurrent death in refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation are not well understood. Moreover, most leukemia patient prediction models worldwide mainly focus on death endpoints, with relatively few studies on predicting non-recurrent death. Therefore, our study provides further insights in this aspect, offering new methods and guidance for prognosis assessment in high-risk refractory acute leukemia patients. This helps clinicians quantify the risk of non-recurrent death, optimize treatment plans, and provide personalized follow-up for high-risk patients effectively and conveniently. Declarations Acknowledgment This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflict of interest relevant to this manuscript are reported for all authors. Funding No funding was received for this study. Conflict of interest The authors have fully disclosed their conflicts of interest in the manuscript and during the submission process. This manuscript has not been published and is not under consideration for publication elsewhere. Author contributions This research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflicts of interest relevant to this manuscript are reported for any author. Minglu Li, Jingbo Wang conceived of the presented idea. Minglu Li and Rongmu Luo developed the theory and performed the computations. Minglu Li, Weijie Zhang drafted the manuscript. Minglu Li, Xinhong Fe , Shuqin Zhang and Jie Zhao verified the analytical methods. All authors discussed the results and contributed to the final manuscript. 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Expert Rev Hematol. 10.1586/ehm.10.32 . -8-1 Carré M, Porcher R, Finke J, Ehninger G, Koster L, Beelen D, Ganser A, Volin L, Lozano S, Friis L, Michallet M, Tischer J, Olavarria E, Cascon MJP, Iacobelli S, Koc Y, Jindra P, Arat M, Witte Td RM (2020) Role of Age and Hematopoietic Cell Transplantation-Specific Comorbidity Index in Myelodysplastic Patients Undergoing an Allotransplant: A Retrospective Study from the Chronic Malignancies Working Party of the European Group for Blood and Marrow Transplantation. Biol Blood Marrow Transplant 3/03/01;26 G M, A B. Allogeneic Hematopoietic Stem Cell Transplantation for Acute Myeloid Leukemia of the Elderly: Review of Literature and New Perspectives - PubMed. Mediterranean J Hematol Infect Dis 11/01/2020;12(1). 10.4084/MJHID.2020.081 BD F, C LBBRLSC, MAD BGSAB, GMT P, MM GHHGCH, HM K, SJ LTNRN, B R, BN WMWMP, EA S (2023) Adapting the HCT-CI Definitions for Children, Adolescents, and Young Adults with Hematologic Malignancies Undergoing Allogeneic Hematopoietic Cell Transplantation - PubMed. Transplantation Cell therapy 29(2). 10.1016/j.jtct.2022.11.019 ML S, MB M, BM RSFB, DG S M, B S. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT - PubMed. Blood 10/15/2005;106(8 ). doi:10.1182/blood-2005-05-2004 JA NM-CRZ (2021) Graft-versus-host disease prophylaxis: Pathophysiology-based review on current approaches and future directions - PubMed. Blood Rev 48. 10.1016/j.blre.2020.100792 RQ K, dJ JJBTP (2013) Hemorrhagic cystitis in a cohort of pediatric transplantations: incidence, treatment, outcome, and risk factors - PubMed. Biology blood marrow transplantation: J Am Soc Blood Marrow Transplantation 19(8). 10.1016/j.bbmt.2013.05.014 L H, M S, E D, A G, B H. Increased risk of complicated CMV infection with the use of mycophenolate mofetil in allogeneic stem cell transplantation - PubMed. Bone Marrow Transplant. (2002) ;29(11). 10.1038/sj.bmt.1703583 DA A, A O RH, MJ JT, MM B LB, CD B, M C, JW V. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia - PubMed. Blood 05/19/2016;127(20). 10.1182/blood-2016-03-643544 MH J, HT G, EW MAKMWDW, NS CJPDW, GS T, H C, RF D, GB KPS, T MYIAVSAMBDDJ, CL H, SA K, PJ M, RS MHS, CS W, GB C, SJ V, SZ L (2015) National Institutes of Health Consensus Development Project on Criteria for Clinical Trials in Chronic Graft-versus-Host Disease: I. The 2014 Diagnosis and Staging Working Group report - PubMed. 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Cancer Lett. 12/01/2018;438 . doi:10.1016/j.canlet.2018.08.030 C ASMLBNSRNDB, U CGS, D B P, JJ NM, Z CAGAHLGSGMAEBFMJE, C PFBAR, NC SMG (2020) Redefining and measuring transplant conditioning intensity in current era: a study in acute myeloid leukemia patients - PubMed. Bone Marrow Transplant 55(6). 10.1038/s41409-020-0803-y HJ EBMLMSGERSJF, K S-E, AR Z KAG, D B SM, IW WABNMPK B, N K, A V, M G, E H, C S, J E, M M, A N. Comparison of matched sibling donors versus unrelated donors in allogeneic stem cell transplantation for primary refractory acute myeloid leukemia: a study on behalf of the Acute Leukemia Working Party of the EBMT - PubMed. J Hematol Oncol. 06/24/2017;10(1). 10.1186/s13045-017-0498-8 M Y, T K, S SY, T F MNUDO, M T, K I YOTE, T MSYK (2023) Age and allogeneic hematopoietic cell transplantation outcomes in acute myeloid leukemia - PubMed. Int J Hematol 117(3). 10.1007/s12185-022-03486-7 SH C, YC L, CJ L, PS K, HY W, LT H, JS L, TJ C (2020) European Group for Blood and Marrow Transplantation score correlates with outcomes of older patients undergoing allogeneic hematopoietic stem cell transplantation - PubMed. J Chin Med Association: JCMA 83(3). 10.1097/JCMA.0000000000000255 CY W, HY R, ZX Q, LH YWXNC, MJ W, WL W, WS X, JP WYLYJD (2013) [Prognostic implications of hematopoietic cell transplantation-specific comorbidity index on non-relapse mortality and overall survival after allogeneic hematopoietic stem cell transplantation] - PubMed. Zhonghua xue ye xue za zhi = Zhonghua xueyexue zazhi. 34(8). 10.3760/cma.j.issn.0253-2727.2013.08.004 C JB (2002) Current management strategies for the prevention and treatment of cytomegalovirus infection in pediatric transplant recipients - PubMed. Paediatr Drugs 4(5). 10.2165/00128072-200204050-00001 M B, G B. Quantitation of cytomegalovirus: methodologic aspects and clinical applications - PubMed. Clin Microbiol Rev. (1998) ;11(3). 10.1128/CMR.11.3.533 P L. Beta-herpesvirus challenges in the transplant recipient - PubMed. J Infect Dis 10/15/2002;186 Suppl 1. 10.1086/342962 dlC MKLGR (2021) Impact of donor and recipient Epstein-Barr Virus serostatus on outcomes of allogeneic hematopoietic cell transplantation: a systematic review and meta-analysis - PubMed. Ann Hematol 100(3). 10.1007/s00277-021-04428-9 AG TL, SV CAL, SE BRD, C dK PSNDK (2023) Early immune reconstitution as predictor for outcomes after allogeneic hematopoietic cell transplant; a tri-institutional analysis - PubMed. Cytotherapy 25(9). 10.1016/j.jcyt.2023.05.012 D K, S T, C GK, D CAMDGJK, L C R (2022) The improvement in overall survival from unrelated donor transplantation in Australia and New Zealand is driven by a reduction in non-relapse mortality: A study from the ABMTRR - PubMed. Bone Marrow Transplant 57(6). 10.1038/s41409-022-01683-w P AA, M KY EAEANGSCBSKT, O I MOTD, O MBHA (2020) Effects of ABO incompatibility in allogeneic hematopoietic stem cell transplantation - PubMed. Transfus clinique et biologique: J de la Societe francaise de Transfus sanguine 27(3). 10.1016/j.tracli.2020.06.008 F RCHGYBTKSA (2020) Impact of ABO blood group incompatibility on the outcomes of allogeneic hematopoietic stem cell transplantation - PubMed. Transfus apheresis science: official J World Apheresis Association : official J Eur Soc Haemapheresis 59(1). 10.1016/j.transci.2019.06.024 L HNMR, R TPBBSFWJMRL, DB N (2015) Risks and benefits of sex-mismatched hematopoietic cell transplantation differ according to conditioning strategy - PubMed. Haematologica 100(11). 10.3324/haematol.2015.125294 C QWWQHZBLJL Z, T M, J Z, Y Z. Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma - PubMed. Front Immunol. 11/23/2022;13. 10.3389/fimmu.2022.1019638 Additional Declarations No competing interests reported. Supplementary Files Supplementdocuments.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6212386","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":433278929,"identity":"4282de67-5966-49dc-81a1-e9aaf095a172","order_by":0,"name":"Minglu Li","email":"","orcid":"","institution":"Peking University Aerospace School of Clinical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Minglu","middleName":"","lastName":"Li","suffix":""},{"id":433278930,"identity":"2b29d009-2ce6-4e97-be71-274e320f7c6e","order_by":1,"name":"Weijie Zhang","email":"","orcid":"","institution":"Peking University Aerospace School of 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Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3RMQrCMBSA4VcK7RL3SMEzPBCqIuhVlIJTFQ/gIAhOFa8iOKhbJFCXimsFQaUgDg4WFwsORtHRqJtg/iEkkI+EBECl+tUoAiDoTEzZV8SoPIj+IUMg+BnB2Zzv881VaWR6cXxurSBn9hkkYwkJGrU8xZ0z8eZDi/g7KHgctF7wmtjMtZEidzCsDyytzQFDB3StKyGLw5O4UZLcyDp6Q0I3uxGkJIhBU/dTdDkphwdbPDKvFDzfLhKfEwwcnPYkJN13syd64eWc2YmW5xbP4Gy63SQSIjIsClBtP1bkNjApED8XH8UF32xSqVSqf+4KUgtXWvqlkrYAAAAASUVORK5CYII=","orcid":"","institution":"Peking University Aerospace School of Clinical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jingbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-12 13:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6212386/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6212386/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79327110,"identity":"dca24936-c31c-49ed-b311-ff7d1d8b92fa","added_by":"auto","created_at":"2025-03-27 05:43:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient enrollment and exclusion flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAllo-HSCT: Allogeneic Hematopoietic Stem Cell Transplantation, MDS: Myelodysplastic Syndromes, CML: Chronic Myeloid Leukemia, BUCY regimen: Busulfan and Cyclophosphamide regimen, TBI-CY regimen: Total Body Irradiation-Cyclophosphamide regimen, PTCY regimen: Post-transplantation Cyclophosphamide regimen\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/f98c72b9f4b67705086df5a0.png"},{"id":79327112,"identity":"b2b9e0a8-7bbf-4b88-85a6-fa2f4932b845","added_by":"auto","created_at":"2025-03-27 05:43:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLasso Regression Cross-Validation Plot and Lasso Regression Coefficient Path Plot \u003c/strong\u003eA, Lasso Regression Cross-Validation Plot;\u003c/p\u003e\n\u003cp\u003eB, Lasso Regression Coefficient Path Plot ;\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/15a4c24785f785766eb65297.jpg"},{"id":79328941,"identity":"15d20359-b1db-4aa4-b80e-8a10d24a7bb1","added_by":"auto","created_at":"2025-03-27 06:07:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":92731,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the risk probability of NRM at 1, 2, and 3 years.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, WBC: White Blood Cell, PLT: Platelet, GVHD: Graft-versus-Host Disease\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/b6a2f5da2770857e8f8dc4d6.jpg"},{"id":79327117,"identity":"3a575553-cbfc-481a-ae73-a036a68d35c3","added_by":"auto","created_at":"2025-03-27 05:43:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTraining set and validation set ROC curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, Training set ROC curves; We obtained AUC values of 0.839 (95% CI: 0.793–0.884), 0.802 (95% CI: 0.75–0.854), and 0.753 (95% CI: 0.691–0.815) for 1, 2, and 3 years in the training set.\u003c/p\u003e\n\u003cp\u003eB, Validation set ROC curves; We obtained AUC values of 0.783 (95% CI: 0.703–0.864), 0.723 (95% CI: 0.638–0.809), and 0.732 (95% CI: 0.644–0.821 for 1, 2, and 3 years in the validation set.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/541fc724983cdca70f4b617f.jpg"},{"id":79327118,"identity":"ddd67621-f90d-42d8-9fb4-66822fdc483e","added_by":"auto","created_at":"2025-03-27 05:43:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":41055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTime-dependent AUC curve and calibration curve\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, Training set time-dependent AUC;\u003c/p\u003e\n\u003cp\u003eB, Validation set time-dependent AUC;\u003c/p\u003e\n\u003cp\u003eC, Training set calibration curve;\u003c/p\u003e\n\u003cp\u003eD, Validation set calibration curve;\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/7b3dfc5276feb3cc84a3d597.jpg"},{"id":82461767,"identity":"030655dc-9269-450c-9eb0-61991f9c3122","added_by":"auto","created_at":"2025-05-11 14:46:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2476313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/1eb2499b-3eb1-40cd-bee8-f47190b63de7.pdf"},{"id":79327114,"identity":"4a22b3a7-a127-4db2-926a-829b3508aeae","added_by":"auto","created_at":"2025-03-27 05:43:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":467180,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementdocuments.docx","url":"https://assets-eu.researchsquare.com/files/rs-6212386/v1/87a66fe4a71a7d71d4d2b37b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment and validation of a non-relapse mortality risk prediction model for high-risk and refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSince the first successful allogeneic hematopoietic stem cell transplantation (allo-HSCT) in the world, the development of allo-HSCT in the world has spanned over 50 years. Studies have shown that the survival rate of leukemia patients has significantly improved by adopting new standards and treatment strategies, such as high-dose chemotherapy, demethylating agents, small molecule targeted drugs, and immune targeted drugs, while allo-HSCT remains a key method for achieving long-term survival. With the improvement of pre-treatment regimens, advancements in acute graft-versus-host disease (GVHD) treatment, and the expansion of donor selection criteria, an increasing number of leukemia patients are experiencing the benefits of allo-HSCT. However, complications associated with allo-HSCT, such as infection, graft-versus-host disease, and relapse, have had a significant impact on the efficacy of allo-HSCT and the quality of life of patients. Studies indicate that 20\u0026ndash;32% of patients die from non-relapse mortality (NRM), mainly due to severe infections, organ failure, and graft dysfunction(\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough comprehensive literature review and clinical investigation, age(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), hematopoietic cell transplantation-specific comorbidity index (HCT-CI)(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), GVHD(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), viral infections, and complications such as hemorrhagic cystitis(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and Cytomegaovirus (CMV)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) infection have been identified as important risk factors for non-relapse mortality in acute leukemia patients after allo-HSCT. Although these factors have been recognized, further research is needed to enhance the understanding of the specific correlations of these factors and their impact on patient prognosis. Future studies should focus on developing risk prediction models for non-relapse mortality in refractory acute leukemia patients undergoing allo-HSCT, providing clinicians with more precise assessment tools to improve patient outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eClinical data from 966 patients with hematologic diseases who underwent HSCT at the Hematology Department of our hospital between January 1, 2015, and December 31, 2021, were gathered. After screening, 595 patients were chosen for inclusion in the study. The flowchart depicting the screening process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Aerospace central Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Conditioning Regimen\u003c/h2\u003e \u003cp\u003eThe Conditioning regimen included busulfan (BU, 3.2mg/kg/d, -8d to -6d), total body irradiation (TBI, 400cGy x 3d), cytarabine (AraC, 4g/m\u0026sup2;, -10d to -9d), cyclophosphamide (Cy, 1.8g/m\u0026sup2;, -5d to -4d), MECCNU (250mg/m\u0026sup2;, -3d), and anti-human T-cell immunoglobulin (ATLG, 5mg/kg/day, -5d to -2d). Additional medications were added based on patient condition, including CLAG (cladribine, cytarabine, and G-CSF) or FLAG (fludarabine, cytarabine, and G-CSF) regimens, teniposide, idarubicin, decitabine, and their combinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definitions and Assessments\u003c/h2\u003e \u003cp\u003eDiagnosis of leukemia followed the 2016 World Health Organization (WHO) classification criteria(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).Diagnosis and grading of acute and chronic graft-versus-host disease (GVHD) followed the Glucksberg and MAGIC criteria, and the NIH scoring system, respectively(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).Engraftment criteria included neutrophil count\u0026thinsp;\u0026gt;\u0026thinsp;0.5\u0026times;10⁹/L for three consecutive days and platelet count\u0026thinsp;\u0026gt;\u0026thinsp;20\u0026times;10⁹/L for seven consecutive days without transfusion(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelapse was defined as the presence of leukemia cells or bone marrow blasts\u0026thinsp;\u0026ge;\u0026thinsp;5% in peripheral blood or bone marrow in patients who achieved complete remission post-transplant(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).Non-relapse mortality referred to deaths unrelated to disease relapse, such as transplant-related complications. Transplant conditioning intensity (TCI) score was determined according to previous literature(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe primary endpoint was non-relapse mortality with relapse as a competing risk event. Survival time was calculated from the date of HSCT to death or last follow-up. Participants with unknown cause of death, unknown survival time, or zero survival time were excluded. Participants alive at the last follow-up were censored.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used for variable comparison. Non-normally distributed data were presented as median and interquartile range. Univariate analysis was performed using chi-square or Fisher's exact test for categorical variables and t-test or rank-sum test for continuous variables. Significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Optimal cutoff points for continuous variables were determined using KM curves. In the training set, competing risk models were used for univariate analysis, and significant variables were selected for further analysis with cumulative incidence function (CIF) curves and Fine-Gray tests. Lasso regression combined with cross-validation was used for predictor selection.\u003c/p\u003e \u003cp\u003eBased on Lasso regression, univariate competing risk analysis, variables were screened, and a competing risk model was constructed. Nomograms were generated in the training sets. Time-dependent Receiver operating characteristic (ROC) curves and the areas under the curves (AUCs), as well as calibration curves, were used to evaluate model performance. Statistical analyses were conducted using R software version 4.32.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThe baseline characteristic table (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) provides important information on the demographic and clinical features of the study population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics and baseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCensored, N\u0026thinsp;=\u0026thinsp;300\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNRM, N\u0026thinsp;=\u0026thinsp;171\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReplase, N\u0026thinsp;=\u0026thinsp;124\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (17, 41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (27, 44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (18, 39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying.disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.745\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e223 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre.transplantation.status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon CR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCNSL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtramedullary.leukemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCT.CI.scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (27, 45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (28, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (26, 46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.recipient.sex.matched\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144 (48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale-male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale-female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoner.type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eMSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224 (75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.recipient.blood.type.matched\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91 (53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor dismatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor dismatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBidirectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConditioning.regimen.with.ATG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 (2.00, 2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (2.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.50, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStem.cell.source\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM\u0026thinsp;+\u0026thinsp;PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWith.UCB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e125 (73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMNC.cell.dose.count\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.11 (8.59, 9.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.10 (8.66, 9.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.08 (8.80, 9.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD34..cell.dose.count\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.18 (2.75, 5.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.38 (2.10, 5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.56 (2.61, 5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC.engraftment.days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.0 (11.0, 15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 (12.0, 18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5 (12.0, 17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT.engraftment.days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMV.infections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e259 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110 (89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEBV.infections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemorrhagic.cystitis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e231 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eaGVHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257 (86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94 (55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecGVHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164 (96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMedian (IQR); n (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003eKruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's exact test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAML: Acute Myeloid Leukemia, ALL: Acute Lymphoblastic Leukemia, MAPL: Mixed Phenotype Acute Leukemia, CNSL: Central Nervous System Leukemia, HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, MSD: Matched Sibling Donor, HFD: Haploidentical Family Donor, URD: Unrelated Donor, UCB: Umbilical Cord Blood, ATG: Anti-Thymocyte Globulin, TCI: Transplant Conditioning Intensity, BM: Bone Marrow, PB: Peripheral Blood, MNC: Mononuclear Cells, WBC: White Blood Cell, PLT: Platelet, CMV: Cytomegalovirus, EBV: Epstein-Barr Virus, GVHD: Graft-versus-Host Disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe 595 patients were randomly divided into a training cohort (70%, n\u0026thinsp;=\u0026thinsp;417) and a validation cohort (30%, n\u0026thinsp;=\u0026thinsp;178). By comparison, we found that there were no statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between the two groups for all variables included. This indicates that our data allocation was both random and reasonable. The specific grouping details are presented in the supplement documents (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Optimal Cutoff Value Determination Using the KM Curve\u003c/h2\u003e \u003cp\u003eTo ascertain the prognostic significance of specific continuous variables more accurately, we utilized the Kaplan-Meier curve to stratify these variables, thereby identifying the optimal cutoff points that would enhance the discriminatory power of prognostic outcomes. Notably, the cutoff values derived from the sample data may diverge from those commonly employed in clinical practice. The graphical representation of the optimal cutoff values is presented in the supplement documents (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate Competing Risk Analysis\u003c/h2\u003e \u003cp\u003eTo explore the determinants of non-relapse mortality (NRM), we conducted univariate competitive risk analysis using a competing risks model, aiming to identify variables significantly associated with NRM (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).In this context, relapse was considered a competing event, and a significant correlation between NRM and the following variables was observed: age (18\u0026ndash;45 years vs. \u0026ge;45 years), pre-transplant condition, extramedullary infiltration, HCT score\u0026thinsp;\u0026ge;\u0026thinsp;3, umbilical cord blood donor type, pre-treatment regimen including ATG, stem cell source (umbilical cord blood), days to WBC engraftment\u0026thinsp;\u0026ge;\u0026thinsp;16, days to PLT engraftment\u0026thinsp;\u0026ge;\u0026thinsp;16, and GVHD. To further clarify the impact of these significant variables on relapse and NRM, CIF curves were plotted and Fine-Gray tests were conducted.The details is presented in the supplement documents (Figure S2,S3;Table S2,S3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Results of Univariate Competing Risk Analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvent N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82, 1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.65,5.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36,3.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderlying.disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43,1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35,1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAPL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51,3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePre.transplantation.status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon CR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17,2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCNSL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55,1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExtramedullary.leukemia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.48,3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCT.CI.scores\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72,2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.13,4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.recipient.sex.matched\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76,1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale-male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.55,1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale-female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.66,2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.age,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77,2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83,3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDoner.type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.59,1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07,5.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15,1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor.recipient.blood.type.matched\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor dismatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72,1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinor dismatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5,1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBidirectional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61,2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTCI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74,1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConditioning.regimen.with.ATG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStem.cell.source\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBM\u0026thinsp;+\u0026thinsp;PB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53,1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.18,5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWith.UCB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72,1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMNC.cell.dose.count,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6,2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCD34..cell.dose.count,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57,1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWBC.engraftment.days,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.45,3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePLT.engraftment.days,group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.62,3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCMV.infections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82,3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEBV.infections\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62,1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHemorrhagic.cystitis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81, 2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eaGVHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.72,5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ecGVHD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51,3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAML: Acute Myeloid Leukemia, ALL: Acute Lymphoblastic Leukemia, MAPL: Mixed Phenotype Acute Leukemia, CNSL: Central Nervous System Leukemia, HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, MSD: Matched Sibling Donor, HFD: Haploidentical Family Donor, URD: Unrelated Donor, UCB: Umbilical Cord Blood, ATG: Anti-Thymocyte Globulin, TCI: Transplant Conditioning Intensity, BM: Bone Marrow, PB: Peripheral Blood, MNC: Mononuclear Cells, WBC: White Blood Cell, PLT: Platelet, CMV: Cytomegalovirus, EBV: Epstein-Barr Virus, GVHD: Graft-versus-Host Disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictor Selection Using Lasso Regression\u003c/h2\u003e \u003cp\u003eTo identify predictors of NRM, we employed Lasso regression analysis, incorporating all variables into the modeling cohort. In pursuit of a more inclusive variable set and enhanced predictive performance, we selected the lambda.min value, achieving the optimal predictive model at lambda.min\u0026thinsp;=\u0026thinsp;0.008482706 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIncorporating the findings from Lasso regression and univariate competing risk analysis, a multivariate competing risk model was developed, including age, pre-transplantation condition, extramedullary infiltration, HCT score, WBC engraftment days, PLT engraftment days, and acute GVHD as the seven pivotal variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Development of a Competing Risk Regression Model\u003c/h2\u003e \u003cp\u003eA multivariate competing risk model was constructed using age, pre-transplantation condition, extramedullary infiltration, HCT score, WBC engraftment days, PLT engraftment days, and acute GVHD. The detailed outcomes are delineated in the Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Multivariate Competitive Risk Model in the Training Set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCause\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecoef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eNRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80,6.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge 18\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37,4.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre.transplantation.status Non CR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88,2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtramedullary.leukemia Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83,2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCT.CI.scores Intermediate-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28,0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCT.CI.scores Low-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.8591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20,0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBC.engraftment.days\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.05,2.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLT.engraftment.days\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95,2.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaGVHD 2_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.62,5.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eRelapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.53,1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge 18\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.2304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48,1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre.transplantation.status Non CR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.00,9.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtramedullary.leukemia Yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68,1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCT.CI.scores Intermediate-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.0764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19,0.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHCT.CI.scores Low-risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.8206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.06,0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBC.engraftment.days\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.3029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43,1.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLT.engraftment.days\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.4827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93,2.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eaGVHD 2_4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79,1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003eHR = Hazard Ratio, CI\u0026thinsp;=\u0026thinsp;Confidence Interv\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index, WBC: White Blood Cell, PLT: Platelet, GVHD: Graft-versus-Host Disease\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFigures\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Construction of a Nomogram\u003c/h2\u003e \u003cp\u003eDrawing from the competing risk model outcomes, regression coefficients were translated into scores, which were then summated to predict the probability of NRM at 1, 2, and 3 years, culminating in the creation of a Nomogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Utilization of the Nomogram involves drawing vertical lines from each variable to its corresponding tick mark to obtain scores, summing these scores, and subsequently drawing a vertical line from the total score to the NRM axis at 1, 2, and 3 years to ascertain the probability of NRM occurrence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Model Validation\u003c/h2\u003e \u003cp\u003eThe overall performance of the Nomogram was assessed by validating the model's performance on both the training and validation datasets. We obtained AUC values of 0.839 (95% CI: 0.793\u0026ndash;0.884), 0.802 (95% CI: 0.75\u0026ndash;0.854), and 0.753 (95% CI: 0.691\u0026ndash;0.815) for 1, 2, and 3 years in the training set, respectively, along with C-index values of 0.744, 0.737, and 0.744 for the same time intervals. The discriminative ability of the predictive model was evaluated using time-dependent ROC curves, with the associated time-dependent AUC curves plotted for visual representation. The model demonstrated robust and stable discriminative performance, as evidenced by the time-dependent ROC and AUC curves for both the training and validation datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calibration performance was appraised by the proximity of the calibration curve to the ideal diagonal, indicating the model's accuracy in predicting outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe retrospective study found that age is an independent risk factor for non-relapse mortality (NRM) after allo-HSCT in high-risk refractory acute leukemia patients, with a highly significant correlation. Eolia Brissot's study showed that patient age (\u0026ge;\u0026thinsp;50 years) is associated with higher NRM(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This study also confirmed that high-risk refractory acute leukemia patients aged\u0026thinsp;\u0026ge;\u0026thinsp;45 have a 3.48 times higher risk of NRM compared to patients\u0026thinsp;\u0026lt;\u0026thinsp;18 years old (95%CI: 1.80\u0026ndash;6.73). While some studies have shown an association between increasing age and higher incidence and mortality rates in allo-HSCT(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), current research trends suggest that the correlation with actual age is low, and biological age (indicated by health status and comorbidities) may be a more accurate predictor of NRM(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). With the aging population and no clear age limit for undergoing allo-HSCT, the number of elderly patients receiving allo-HSCT is expected to rise. Predicting NRM should not be solely based on patient age, but should consider other patient characteristics to develop better follow-up strategies.\u003c/p\u003e \u003cp\u003eThe study found that grade II-IV aGVHD (HR\u0026thinsp;=\u0026thinsp;3.86, 95%CI: 2.62\u0026ndash;5.67) is an independent predictor of NRM with high significance; however, cGVHD showed no significant correlation. Sheng-Hsuan Chien's study indicated that moderate to severe cGVHD is associated with higher OS or PFS rates. Interestingly, factors significantly associated with NRM are not cGVHD but III-IV grade aGVHD(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This discrepancy with clinical reality may be because patients with longer survival have more opportunities to develop cGVHD, and some patients may experience NRM before cGVHD occurs. Therefore, preventing and treating GVHD is crucial for allo-HSCT patients to improve survival rates and reduce the risk of related complications. Wang CY et al. analyzed the prognostic significance of HCT-CI score, age, gender, conditioning regimen, pre-transplant disease status, graft source, and HLA matching degree on NRM and overall survival (OS) using a Cox regression model. The results showed that HCT-CI score is an independent risk factor for patients undergoing allogeneic HSCT(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In large cohort studies, complications evaluated by the HCT-CI score significantly impact NRM(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Consistent with these findings, our study revealed that an HCT-CI score\u0026thinsp;\u0026ge;\u0026thinsp;3 is an independent risk factor for NRM in high-risk refractory acute leukemia patients, with HCT-CI 0 points (HR\u0026thinsp;=\u0026thinsp;0.42, 95%CI: 0.20\u0026ndash;0.90) and 1\u0026ndash;2 points (HR\u0026thinsp;=\u0026thinsp;0.49, 95%CI: 0.28\u0026ndash;0.86) being protective factors for NRM. The HCT-CI score can serve as a useful tool for predicting the risk of NRM after allogeneic hematopoietic stem cell transplantation, measuring the impact of pre-existing comorbidities on transplant patients.\u003c/p\u003e \u003cp\u003eEolia Brissot's study indicated that CMV-positive status is associated with higher NRM (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Junko Takeshita et al. further emphasized the impact of CMV infection on prognosis by evaluating the correlation between CMV viral load area under the curve (CMV-AUC) and the NRM rate in allo-HSCT patients(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Both direct and indirect cytomegalovirus infections increase overall and NRM rates. In the inter-group comparison, we found a significant correlation between CMV infection and the prognosis of high-risk refractory acute leukemia (P\u0026thinsp;=\u0026thinsp;0.018). However, the univariate competing risk analysis showed no significant correlation between CMV infection and NRM (P\u0026thinsp;=\u0026thinsp;0.14), suggesting that while CMV may somewhat influence a patient's prognosis, its effect on NRM when considering relapse as a competing risk is limited. Due to the limitations of single-center data, larger sample sizes are needed to further clarify the impact of CMV infection on patient outcomes. Nevertheless, with the use of less toxic non-myeloablative conditioning regimens and intensified post-transplant immunosuppressive therapy, combined with an increasing number of elderly patients undergoing transplants, the risk of opportunistic infections and viral reactivation in the future is expected to rise. Late CMV disease mortality and the indirect effects of CMV remain major barriers to achieving successful long-term outcomes.\u003c/p\u003e \u003cp\u003eAlthough Epstein-Barr virus (EBV) is a triggering factor for various diseases, clinical studies evaluating the impact of EBV serostatus on clinical outcomes in the allo-HCT population are lacking. Kołodziejczak M et al. conducted a meta-analysis to systematically summarize and analyze the impact of donor and recipient EBV serostatus on transplant outcomes in allo-HCT recipients and found that EBV seropositivity in donors and recipients has no significant impact on NRM(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Our study reached the same conclusion.\u003c/p\u003e \u003cp\u003eWang CY et al. found that pre-transplant disease status is an independent risk factor for patients undergoing allogeneic HSCT(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In our study, univariate analysis showed that not achieving complete remission before transplantation (HR\u0026thinsp;=\u0026thinsp;1.7, 95%CI: 1.17\u0026ndash;2.47, P\u0026thinsp;=\u0026thinsp;0.005) is significantly associated with NRM in high-risk refractory acute leukemia patients, but in the multivariate analysis, it was not significant for NRM. In the multivariate competing risk model, not reaching complete remission was a highly significant independent risk factor for relapse (HR\u0026thinsp;=\u0026thinsp;5.26, 95%CI: 3.00-9.24). Patients with complete remission usually have restored hematopoietic function after transplantation, which helps in rebuilding a healthy immune system post-transplant. This, in turn, reduces the risk of infections and bleeding, improving patient survival. However, its main impact is on relapse and relapse-related death, with a limited effect on NRM.\u003c/p\u003e \u003cp\u003ePatients undergoing hematopoietic stem cell transplantation receive a series of radiotherapy and chemotherapy pre-treatments, which may result in damage and inhibition of normal bone marrow cells, making it easier for leukemia cells to spread to extramedullary tissues. After hematopoietic stem cell transplantation, patients receive immunosuppressive therapy to prevent graft-versus-host disease (GVHD) occurrence, potentially weakening the immune system's monitoring and clearance capacity against leukemia cells, leading to extramedullary infiltration. The occurrence of extramedullary infiltration is significant for patient treatment and prognosis. In our study, univariate analysis found that extramedullary infiltration (HR\u0026thinsp;=\u0026thinsp;2.16, 95%CI: 1.48\u0026ndash;3.15, P\u0026lt;0.001) was a significant associated factor for non-relapse mortality in high-risk refractory acute leukemia patients.\u003c/p\u003e \u003cp\u003eDelayed engraftment is a significant adverse prognostic factor for patients with high-risk refractory acute leukemia. During white blood cell engraftment delay, the risk of infection increases, temporary loss of hematopoietic function occurs, affecting patient recovery and prolonging the time spent in a state of low immunity. Platelet engraftment delay poses a risk of bleeding for patients, including skin bruising, nosebleeds, and gum bleeding. Moreover, platelet engraftment delay may also increase the risk of infections, attributed to impairment in megakaryocyte maturation in the bone marrow and increased destruction of mature platelets in peripheral blood. Platelet engraftment is influenced by various factors, including transplant type, CD34\u0026thinsp;+\u0026thinsp;cell infusion quantity, HLA allele compatibility level, severity grading of aGVHD, cytomegalovirus infection, adjustments in immunosuppressive therapy, etc. In our study, white blood cell engraftment time\u0026thinsp;\u0026ge;\u0026thinsp;16 days (HR\u0026thinsp;=\u0026thinsp;2.12, 95%CI: 1.45\u0026ndash;3.11, P\u0026lt;0.001) and platelet engraftment time\u0026thinsp;\u0026ge;\u0026thinsp;16 days (HR\u0026thinsp;=\u0026thinsp;2.34, 95%CI: 1.62\u0026ndash;3.38, P\u0026lt;0.001) were associated factors for non-relapse mortality in high-risk refractory acute leukemia patients, with platelet engraftment time\u0026thinsp;\u0026ge;\u0026thinsp;16 days being an independent risk factor (HR\u0026thinsp;=\u0026thinsp;1.64, 95%CI: 1.05\u0026ndash;2.56). Hence, meticulous management and successful white blood cell and platelet engraftment are essential to reduce the risk of bleeding, enhance quality of life, improve transplant success rates, and conserve medical resources. To achieve these goals, various measures such as increasing platelet engraftment, adjusting immunosuppressive therapy regimens, preventing infections and bleeding, optimizing donor stem cell selection for earlier white blood cell engraftment, and adjusting pre-treatment schemes for high-risk patients could be implemented.\u003c/p\u003e \u003cp\u003eYasuyuki Arai's study demonstrated that the CD4 immune reconstitution after allo-HSCT is associated with a lower NRM rate. They found that reaching CD4\u0026thinsp;\u0026gt;\u0026thinsp;50 cells/\u0026micro;L and/or B cells\u0026thinsp;\u0026gt;\u0026thinsp;25 cells/uL before day 100 post-HSCT predicted reduced NRM(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). David Kliman et al. used a Cox regression model to identify predictive factors associated with increased NRM, including age, male gender, CMV seropositivity, HLA mismatch, and transplantation beyond 6 months after diagnosis(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The impact of ABO blood type mismatch on outcomes after allogeneic HSCT remains controversial. Ataca Atilla P's study aimed to determine the effect of ABO blood type mismatch on transplant outcomes and complications. The results indicated that ABO blood type mismatch is unrelated to the risks of NRM and GVHD(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Ciftciler R et al. analyzed allogeneic HSCT patient performance in terms of neutrophil and platelet recovery rates, GVHD, relapse rate, mortality rate, NRM, and survival rates. The study pointed out that ABO mismatch does not seem to significantly affect major outcomes post-allo-HSCT, including GVHD, relapse rate, mortality rate, disease-free survival (DFS), and overall survival (OS). ABO blood type mismatch does not delay platelet and neutrophil engraftment after allo-HSCT(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Hideki Nakasone et al. found that mixed-sex stem cell transplantation is associated with increased rates of GVHD occurrence and NRM(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Future research will aim to incorporate more indicators into the model to optimize statistical analysis.\u003c/p\u003e \u003cp\u003eIn traditional survival analysis, the Cox proportional hazards regression primarily focuses on a single major event, such as death or survival.This analysis assumes that censoring time and failure time are independent, meaning there is no competing risk between outcomes. Competing risk refers to the probability that a known event in the observation cohort may affect the occurrence of another event or completely hinder it. However, clinical survival data often involve multiple outcomes. Competing risk models involve multiple types of events that may compete with each other. For example, when studying non-recurrent death, the occurrence of a relapse in patients can affect the outcome of non-recurrent death. Traditional survival analysis typically indicates the probability of event occurrence and risk factors, while competing risk models show the risk comparison and interaction between different types of events. Applying traditional survival analysis in such cases may lead to an overestimation of the cumulative incidence of events. Therefore, we chose to apply a competing risk model to clinical right-censored data, treating relapse as a competing risk for non-recurrent death in the analysis.\u003c/p\u003e \u003cp\u003eIn descriptive analysis, the Kaplan-Meier method is commonly used to describe the survival time and survival status of patients in different groups. When studying non-recurrent death, it is important to focus on relapse as a competing event that impacts the outcome. Therefore, cumulative incidence functions (CIF) are used to plot survival analysis curves, and Fine-Gray tests are conducted. This helps provide a clearer and more intuitive understanding of the relationship between variables and relapse and non-recurrent death.\u003c/p\u003e \u003cp\u003eRegression analysis is used to reveal quantitative relationships between multiple variables and can be utilized for variable selection and model building. In the variable selection process, significant or strong correlations between variables often arise, resulting in multicollinearity. Multicollinearity can increase the variance of estimated regression coefficients, widen confidence interval ranges, and significantly reduce the accuracy of estimates, affecting the interpretability of the regression equation. To effectively address this issue, using an improved linear regression is a better choice. The Lasso method considers all predictor variables collectively, effectively addressing issues of correlation between newly included variables and previously included variables. In contrast to traditional stepwise regression methods, Lasso can handle all variables simultaneously. By combining Lasso regression with competing risk models, a predictive model with improved performance compared to traditional models can be built, leading to significantly enhanced predictive accuracy. Therefore, based on Lasso regression and competing risk models, we developed and validated a risk prediction model for non-recurrent death in high-risk refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation.\u003c/p\u003e \u003cp\u003eIn recent years, the Lasso method has gradually been applied in various clinical domains. For example, predicting the recurrence rate of early hepatocellular carcinoma using Lasso-Cox regression and constructing a nomogram evaluation model has proven to accurately determine the recurrence rates of patients at 1, 3, and 5 years, providing substantial clinical utility(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Overall, Lasso regression, with its unique advantages, can construct clinical prediction models and has played a role in various clinical fields, providing clinicians with reliable tools for disease assessment.\u003c/p\u003e \u003cp\u003eIdentifying the optimal treatment plan, counseling, and follow-up for leukemia patients, recognizing risk factors, and developing accurate prediction models are crucial for leukemia patients. While many models have been developed using traditional statistical methods and machine learning techniques to predict survival and death risk in leukemia patients, these models have limitations in terms of their applicability and convenience, with varying predictive abilities.\u003c/p\u003e \u003cp\u003eWe developed and validated a risk prediction model for non-recurrent death in refractory high-risk acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation based on a competing risk model using Lasso regression. Through nomograms, we successfully predicted the one-year, two-year, and three-year non-recurrent death rates of patients with satisfactory predictive performance. In internal validation, we verified the best consistency between the predicted results and observed results. Overall, the study results indicate that the model has high effectiveness.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. Firstly, the model is based on single-center retrospective analysis, lacking random assignment, patient selection, and data integrity issues, which may lead to bias. Secondly, the study lacks external validation, limiting the generalizability of the conclusions and the predictive performance of the model. It should be noted that the patients included in our center were severely ill and belonged to the high-risk refractory acute leukemia group. Therefore, when applying the model, attention should be paid to the target population and stratification of characteristics of different population groups. The model is suitable for high-risk refractory acute leukemia patients and is not applicable to standard-risk, chronic leukemia, lymphoma, or myelodysplastic syndrome patients, thus having limitations in extrapolation. It is worth mentioning that most current studies focus on standard-risk acute leukemia patients, and the high-risk factors for non-recurrent death in refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell transplantation are not well understood. Moreover, most leukemia patient prediction models worldwide mainly focus on death endpoints, with relatively few studies on predicting non-recurrent death. Therefore, our study provides further insights in this aspect, offering new methods and guidance for prognosis assessment in high-risk refractory acute leukemia patients. This helps clinicians quantify the risk of non-recurrent death, optimize treatment plans, and provide personalized follow-up for high-risk patients effectively and conveniently.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflict of interest relevant to this manuscript are reported for all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have fully disclosed their conflicts of interest in the manuscript and during the submission process. This manuscript has not been published and is not under consideration for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. No conflicts of interest relevant to this manuscript are reported for any author. Minglu Li, Jingbo Wang conceived of the presented idea. Minglu Li and Rongmu Luo developed the theory and performed the computations. Minglu Li, Weijie Zhang drafted the manuscript. Minglu Li, Xinhong Fe , Shuqin Zhang and Jie Zhao verified the analytical methods. All authors discussed the results and contributed to the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eY T, K SK, R I, Y I, K O TTT, SW YISF, R K (2016) Analysis of non-relapse mortality and causes of death over 15 years following allogeneic hematopoietic stem cell transplantation - PubMed. Bone Marrow Transplant 51(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/bmt.2015.330\u003c/span\u003e\u003cspan address=\"10.1038/bmt.2015.330\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGooley TA, Chien JW, Pergam SA, Hingorani S, Sorror ML, Boeckh M, Martin PJ, Sandmaier BM, Marr KA, Appelbaum FR, Storb R, McDonald GB (2010) Reduced Mortality after Allogeneic Hematopoietic-Cell Transplantation. 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Front Immunol. 11/23/2022;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.1019638\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.1019638\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Non-relapse mortality, High-risk refractory acute leukemia, Least absolute shrinkage and selection operator, Competing risks model","lastPublishedDoi":"10.21203/rs.3.rs-6212386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6212386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to identify risk factors for non-relapse mortality following allogeneic hematopoietic stem cell transplantation (HSCT) in patients with high-risk refractory acute leukemia and to develop a predictive model. Clinical data and laboratory findings of 966 patients who underwent HSCT at the Aerospace Central Hospital from January 2015 to December 2021 were collected retrospectively, with 595 patients selected as study subjects. Univariate analysis using competing risk models identified potential risk factors, and cumulative incidence curves were plotted. The data were divided into a training set (n\u0026thinsp;=\u0026thinsp;417) and a validation set (n\u0026thinsp;=\u0026thinsp;178) at a 7:3 ratio. In the training set, the least absolute shrinkage and selection operator (Lasso) regression was used to identify predictors of non-relapse mortality, and a predictive model was constructed using competing risk analysis. The model was internally validated in the validation set. A nomogram was developed for visualization, and its performance was assessed. Predictors included age (\u0026lt;\u0026thinsp;18, 18\u0026ndash;45, \u0026ge;\u0026thinsp;45 years), pre-transplant status (CR/Non CR), extramedullary infiltration (No/Yes), HCT score (0, 1\u0026ndash;2, \u0026ge;\u0026thinsp;3), neutrophil engraftment time (\u0026lt;\u0026thinsp;16, \u0026ge;\u0026thinsp;16 days), platelet engraftment time (\u0026lt;\u0026thinsp;16, \u0026ge;\u0026thinsp;16 days), and aGVHD (0-I degree/II-IV degree). The nomogram accurately predicted 1-, 2-, and 3-year non-relapse mortality with AUCs of 0.839 (95% CI: 0.793\u0026ndash;0.884), 0.802 (95% CI: 0.75\u0026ndash;0.854), and 0.753 (95% CI: 0.691\u0026ndash;0.815), respectively. Time-dependent ROC, time-dependent AUC, and calibration curves confirmed the model's discriminative power and precision. This nomogram may assist clinicians in assessing patient prognosis and devising personalized treatment plans.\u003c/p\u003e","manuscriptTitle":"Establishment and validation of a non-relapse mortality risk prediction model for high-risk and refractory acute leukemia patients undergoing allogeneic hematopoietic stem cell","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-27 05:43:37","doi":"10.21203/rs.3.rs-6212386/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"baf72ec9-9135-4e4a-9a15-b48e287410da","owner":[],"postedDate":"March 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-11T14:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-27 05:43:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6212386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6212386","identity":"rs-6212386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}