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Learn more: PMC Disclaimer | PMC Copyright Notice Ann Hematol . 2026 Apr 9;105(5):228. doi: 10.1007/s00277-026-06989-z Search in PMC Search in PubMed View in NLM Catalog Add to search A novel prognostic scoring system HATS for acute myeloid leukemia patients undergoing allogeneic hematopoietic stem cell transplantation Garret M K Leung Garret M K Leung 1 Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China Find articles by Garret M K Leung 1, # , Yishan Ye Yishan Ye 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Find articles by Yishan Ye 2, # , Yi Luo Yi Luo 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Find articles by Yi Luo 2 , Jimin Shi Jimin Shi 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Find articles by Jimin Shi 2 , Yanmin Zhao Yanmin Zhao 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Find articles by Yanmin Zhao 2 , Joycelyn PY Sim Joycelyn PY Sim 1 Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China Find articles by Joycelyn PY Sim 1 , Yok-Lam Kwong Yok-Lam Kwong 1 Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China Find articles by Yok-Lam Kwong 1, ✉ , Huang He Huang He 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China Find articles by Huang He 2, ✉ , Harinder Gill Harinder Gill 1 Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China Find articles by Harinder Gill 1, ✉ Author information Article notes Copyright and License information 1 Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, China 2 Bone Marrow Transplantation Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China ✉ Corresponding author. # Contributed equally. Received 2025 Dec 29; Accepted 2026 Mar 30; Issue date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . PMC Copyright notice PMCID: PMC13065535 PMID: 41954668 Abstract The optimal prognostication model for outcome following allogeneic hematopoietic stem cell transplantation (allo-HSCT) for acute myeloid leukemia (AML) remains undefined. To establish a prognostic scoring system, 466 consecutive AML patients undergoing allo-HSCT from 2014 to 2023 were analyzed as a training cohort. Six donor, leukemia and recipient related factors were identified as prognostically significant and assigned weighted scores, respectively 1 point each for donor cytomegalovirus seropositivity, Hemopoietic Cell Transplantation-specific Comorbidity Index ≥ 2, secondary AML, not in first complete remission, and low-intensity induction therapy; 3 points for inv(3)/t(3;3)(q21;q26) or KMT2A rearrangement; 4 points for − 5/del(5q) or TP53 mutation; 0 point for CEBPA bZIP in-frame mutation or core-binding factor AML; and 1 point for all other genetic risk groups. Scores were grouped into four risk categories: favorable (0–1), intermediate (2–3), poor (4–5), and very poor (≥ 6); constituting the HATS prognostication model. The corresponding 2-year overall survivals of favorable, intermediate, poor and very poor risk groups were 92%, 78%, 50%, and 7.4% (log-rank P < 0.001). An external validation cohort comprising 395 patients was similarly analyzed. With concordance statistics, HATS outperformed all six existing prognostication models. In conclusion, HATS represents a novel prognostication model devised for AML patients undergoing allo-HSCT. Clinicaltrials.gov identifier: NCT06702111 . Supplementary Information The online version contains supplementary material available at 10.1007/s00277-026-06989-z. Keywords: acute myeloid leukemia, allogeneic hematopoietic stem cell transplantation, prognostic model Introduction Acute myeloid leukemia (AML) is a heterogenous disease with diverse outcomes [ 1 , 2 ]. Although allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a potentially curative therapy, it is associated with significant morbidities and mortality [ 3 , 4 ]. Accurate prognostication is therefore critical for guiding treatment decisions, patient counselling, resource allocation, improving transplantation outcomes, and identifying clinical needs for future research. Numerous prognostication models based on a wide array of clinical variables have been developed in the past two decades to predict outcome following allo-HSCT [ 5 – 11 ]. However, the optimal prognostic scoring system remains undefined, due to the complicated interplay between patient, leukemia, and transplantation factors [ 12 – 14 ]. The European LeukemiaNet (ELN) 2022 classification is limited in its ability to predict outcome after allo-HSCT, as it relies solely on genomic risks and is primarily geared towards adult patients younger than 60 years treated with intensive induction therapy [ 15 ]. Efforts to improve ELN 2022 include re-classifying its subgroups and incorporating measurable residual disease (MRD) into the risk assessment [ 16 – 18 ]. The hematopoietic cell transplantation comorbidity index (HCT-CI) was developed nearly two decades ago based on the Charlson Comorbidity Index, which was used to predict one-year mortality in patients admitted to general medical centers [ 5 ]. Although the ability of HCT-CI to predict post allo-HSCT survival was validated subsequently, these studies all grouped scores of 1 and 2 together, limiting its ability to further subclassified patients with fewer comorbidities [ 19 – 21 , 5 , 22 ]. Similarly, an HCT-CI cut-off of ≥ 3 is also used in existing prognostication models [ 10 , 11 ]. While HCT-CI only relies on comorbidities, the disease-risk index (DRI) solely considers the cytogenetic risk and remission status at transplantation, and is intended for studying outcomes across broad disease categories [ 7 ]. Notably, the DRI cytogenetic risk is mainly based on the CIBMTR grouping published more than a decade ago in 2012. This grouping defines favorable-risk as core-binding factor (CBF)-AML only, adverse-risk as complex cytogenetics with ≥ 4 abnormalities, and intermediate-risk as others; [ 23 , 7 ] which obviously is very different from current cytogenetic risk categorization [ 15 ]. The disease risk comorbidity index (DRCI) divides patients into two subgroups based on HCT-CI (< 3 versus ≥ 3), resulting in a total of six risk categories [ 10 ]. The AML-specific disease risk group (AML-DRG) and AML hematopoietic cell transplant-composite risk (AML-HCT-CR) integrate MRD (flow cytometric or molecular) in the risk stratification, with ELN 2017 used for genetic risk assessment, and HCT ≥ 3 and age ≥ 60 years as additional risk factors [ 11 ]. Importantly, unknown MRD is treated as positive in these two models, with over 40% of patients in the original study having unknown MRD [ 11 ]. Such limitations affect the inclusion of MRD status in prognostication, especially in cohorts with incomplete MRD information. To address the lack of a model dedicated to predicting outcome of AML patients undergoing allo-HSCT, we studied a retrospective series of patients with currently available models to ascertain their prognostic powers, and then with a panel of donor, recipient and leukemia factors to determine relevant prognostic markers. The objective was to use this patient series as a training cohort to establish a novel prognostication model, which would then be validated with an external cohort. Materials and methods Training cohort Consecutive adult patients with AML undergoing allo-HSCT at Queen Mary Hospital (QMH), Hong Kong, China, between January 1, 2014 and December 31, 2023 were retrospectively analyzed and constituted the training cohort. Decisions on allo-HSCT were based on leukemia factors (risk stratification, remission status, MRD) and patient factors (performance score, HCT-CI, and donor availability). Details of indications for allo-HSCT and patient selection were given in Supplementary Table 1. This study was approved by the Institutional Review Board of the University of Hong Kong (UW 24–621) and registered at clinicaltrials.gov (identifier: NCT06702111 ) and conducted in accordance to the Declaration of Helsinki. Allo-HSCT Conditioning regimens were categorized as myeloablative or reduced intensity according to American Society of Transplantation and Cellular Therapy (ASTCT) criteria [ 24 ]. Graft versus host disease (GVHD) prophylaxes were post-transplantation cyclophosphamide (PTCy) in haploidentical and selected mismatched unrelated (MMUD) transplantations, and conventional calcineurin inhibitor-based in matched sibling, matched unrelated, and other MMUD transplantations. All patients received standard antimicrobial prophylaxis and supportive care according to institutional protocols. MRD testing MRD assessment was performed in specific subsets of patients. NPM1 and CBF::MYH11 MRD was detected by in-house droplet digital polymerase chain reaction (ddPCR) with a sensitivity of 10 − 5 to 10 − 6 . RUNX1::RUNX1T1 MRD was detected with an in-house reverse transcription PCR, with a sensitivity of 10 − 5 to 10 − 6 . A minority of patient had case-specific MRD assessment by ddPCR based on other gene mutations or fusions with a sensitivity of 10 − 4.5 to 10 − 5 [ 25 ]. Validation cohort Consecutive adult patients with AML undergoing allo-HSCT at the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China, between January 1, 2018 and December 31, 2021 were retrospectively analyzed and constituted the validation cohort. The indications for allo-HSCT aligned with those of QMH. This study was approved by the Ethics Committee of Clinical Trial of the First Affiliated Hospital of Zhejiang University (IIT20200086C-R1) and conducted in accordance to the Declaration of Helsinki. Statistical methods The prognostic power of six existing prognostication models, including ELN 2022 [ 15 ], HCT-CI [ 5 ], DRI [ 7 ], DRCI [ 10 ], AML-DRG and AML-HCT-CR [ 11 ], was evaluated with concordance (C) statistics. A C-statistic of 1.0 indicated perfect predictive accuracy, while a value of 0.5 indicated random chance. The bootstrap method employing 1000 re-samples was used to obtain the optimism-adjusted C-statistic and 95% confidence interval (CI). The C-statistics of different models were compared with a non-parametric approach [ 26 ]. Area under time-dependent receiver operating characteristic curve (AUC) was estimated with the inverse probability of censored weighting approach [ 27 ]. Time-dependent prediction error was evaluated using integrated Brier scores (IBS), applying inverse probability of censoring weighting. All analyses were performed with the complete-case method without data imputation. Overall survival (OS), defined as the time from allo-HSCT to death (event) or last follow-up (censor), was estimated with the Kaplan-Meier method, and potential impacting factors were compared with the log-rank test. Hazards ratios (HRs) were calculated with the Cox proportional hazards regression model. Two-tailed P-values of < 0.05 were considered statistically significant. Statistical analyses were conducted with the rms, compareC, timeROC, and pec packages from the R-4.4.1 statistical software ( http://cran.r-project.org/ ). Prognostic model development In the development of a novel prognostic model, cox proportional hazards regression model was used to identify clinicopathologic, cytogenetic and molecular features predictive of survivals after allo-HSCT in the training cohort. Significant variables on univariate and multivariate analyses were retained to obtain the β coefficients. Disease risk scores were calculated from weighted points according to the β coefficients (1 point for β at ≤ 1.0; 2 points for β at 1.0–1.5; 3 points for β at 1.6–2.0; and 4 points for β at > 2). The total score was the sum of points from individual risk factors. The overall scores were grouped into 4 risk categories (favorable, intermediate, poor, very poor) based on their association with OS. In turn, the prognostic significance of each significant variable and risk categories of the novel prognostic model was tested on the validation cohort with the cox proportional hazard model and C statistic. Results Training cohort Four hundred and sixty-six consecutive patients (men, N = 211; women, N = 255) with a median age of 51 (18–68) years were analyzed (Table 1 ). The majority (87%) of patients received intensive chemotherapy for remission induction. Patients who received low-intensity induction, based on risk/benefit considerations, were significantly older, and had poorer (intermediate/adverse) ELN 2022 risk diseases that were relapsed or refractory (Supplementary Table 2). Most patients ( N = 368, 79%) had HCT-CI = 0 and hence few pre-transplantation comorbidities. Pre-HSCT, most patients were in complete remission (CR) (CR1, N = 307, 66%; CR2, N = 140, 30%; ≥CR3, N = 13, 3%), with only six patients (1%) in non-remission (NR). Table 1. Clinicopathologic features of the training cohort of 466 patients with acute myeloid leukemia (AML) undergoing allogeneic hematopoietic stem cell transplantation (HSCT) Clinicopathologic parameters Numbers Age Recipient Median – years (range) 51 (18-68) ≥60 years – number (%) 68 (15%) Donor Median – years (range) 38 (15-67) ≥35 years – number (%) 271 (58%) Male sex – number (%) Recipient 211 (45%) Donor 265 (57%) CMV seropositivity – number (%) Recipient 418 (90%) Donor 328 (70%) HCT-CI – number (%) 0 368 (79%) 1 64 (14%) ≥2 34 (7%) White cell count at diagnosis Median – per microliter (range) 12,000 (100-450,300) ≥20,000 per microliter – number (%) 194 (42%) Secondary AML – number (%) 43 (9%) Disease status at transplantation – number (%) CR/CRi 460 (99%) CR1 307 (66%) CR2 140 (30%) CR3+ 13 (3%) NR 6 (1%) ELN 2022 risk classification – number (%) Favorable 97 (21%) Intermediate 218 (47%) Adverse 151 (32%) Remission induction intensity – number (%) Intensive 405 (87%) Low-intensity 61 (13%) MRD status before transplantation – number (%) Not available 382 (82%) Available 84 (18%) Positive 65 (77%) Negative 19 (23%) Prior allogeneic HSCT – number (%) 22 (5%) Donor type – number (%) Matched sibling 177 (38%) Matched unrelated 108 (23%) Mismatched unrelated 67 (14%) Haploidentical 114 (24%) HLA matching – number (%) Matched 285 (61%) ≥1 HLA mismatched 181 (39%) Hematopoietic stem cell source – number (%) Peripheral blood 349 (75%) Bone marrow 117 (25%) Conditioning intensity – number (%) Myeloablative 273 (59%) Reduced intensity 193 (41%) GVHD prophylaxis – number (%) Conventional 327 (70%) PTCy-based 139 (30%) Open in a new tab CMV cytomegalovirus, HCT-CI hematopoietic cell transplantation comorbidity index, CR complete remission, CRi complete remission with incomplete count recovery, NR non-remission, HLA human leukocyte antigen, MRD measurable residual disease, GVHD graft-versus-host disease, PTCy posttransplantation cyclophosphamide Results of existing prognostication models in the training cohort The results of stratification according to existing prognostication models were shown in Supplementary Table 3. Only ELN 2022 had an even risk distribution (favorable: 21%; intermediate: 47%; adverse: 32%). Other models showed significant skewing with predominant clustering of cases to one/two low-risk categories (HCT-CI, 0: 79%; DRI, intermediate: 83%; DRCI, intermediate-1: 80%; AML-DRG, low/intermediate: 99%; AML-HCT-CR, low/intermediate: 88%); suggesting that prognostic discrimination was limited particularly in the low/intermediate risk groups. Kaplan-Meier analyses of OS showed similar findings, with largely overlapping survival curves and hence poor discrimination in the low/intermediate risk categories (Fig. 1 A). Accordingly, the adjusted C statistics of these models were also of low values, reflecting low discriminative power (Fig. 1 B). Fig. 1. Open in a new tab Performance of six current prognostication models in predicting post-allogeneic hematopoietic stem cell transplantation overall survival in 466 patients with acute myeloid leukemia (AML). A. Overall survival analysed by Kaplan-Meier method in different prognostication models. All six models showed overlapping survival curves particularly in the low and low/intermediate risk groups. B . Comparison of C-statistics of different prognostic models, with AML HCT-CR achieving the highest value. ELN: European LeukemiaNet; HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index; DRI: Disease Risk Index; DRCI: Disease Risk Comorbidity Index; AML-DRG: Acute Myeloid Leukemia-specific Disease Risk Group; AML-HCR-CR: AML Hematopoietic Cell Transplant-composite Risk; LR: low risk; IR-1: intermediate risk-1; IR-2: intermediate risk-2; HR: high risk; C: concordance; Adj. C-stat: adjusted C-statistic; CI: confidence interval Genetic/cytogenetic prognostic groups in the training cohort Because ELN 2022 showed better distribution of prognostic groups, selected genetic/cytogenetic changes from ELN 2022 commonly found in the training cohort were used for analysis of OS post-HSCT. Four clusters were observed (Supplementary Fig. 1). Patients with CBF AML and bZIP in-frame mutations in CEBPA ( CEBPA bZIP) showed the most favorable outcome, forming the first cluster. Patients with inv(3)(q21;q26)/t(3;3)(q21;q26) and KMT2A rearrangements had poorer outcomes, placing them in the third cluster. Patients with TP53 mutations ( TP53 mut ) and − 5/del(5q) had the worst survival, forming the fourth cluster. Patients with other genetic/cytogenetic aberrations showed intermediate outcomes, forming the second cluster. Development of a novel prognostic model The impacts on OS of the genetic/cytogenetic grouping and eighteen patient-, disease- and treatment-related clinicopathologic parameters, selected based on their reported prognostic relevance in AML after allo-HSCT, were evaluated by Cox regression analyses (Table 2 ). On multivariate analysis, factors associated with inferior OS included donor cytomegalovirus (CMV) seropositivity, HCT-CI score ≥ 2, secondary AML, non-CR1 (≥ CR2 and NR) at HSCT, genetic risk groups other than CEBPA bZIP or CBF AML, and prior low-intensity induction therapy. Weighted points were assigned to each significant variable in proportion to the β coefficients obtained in the final multivariate model (Table 3 ). The aggregate score was referred to as the H ong Kong a llogeneic HSC T risk s core (HATS), with possible values of 0–9. The HATS scores of the training cohort ranged from 1 to 8, with 2 as the most common score ( N = 207, 44%), followed by 3 ( N = 106, 23%) and 4 ( N = 61, 13%) (Fig. 2 A). Kaplan-Meier analysis showed progressively decreasing OS with increasing HATS scores, with 1 portending the best and ≥ 6 portending the worst survivals (Supplementary Fig. 2). Scores with HRs less than 2 versus the reference group were classified as favorable, scores with HRs from 2 to less than 5 as intermediate; scores with HRs from 5 to less than 15 as poor; and scores with HRs of 15 or higher as very poor. Hence, four HATS risk groups could be defined: favorable (scores 0–1); intermediate (scores 2–3); poor (scores 4–5); and very poor (scores ≥ 6). Table 2. Prognostic indicators for overall survival in the training cohort after allogeneic hematopoietic stem cell transplantation Univariate Multivariate HR 95% CI P -value HR 95% CI P -value Patient Factors Recipient ≥ 60 years 1.40 0.91, 2.17 0.127 — — — Donor ≥ 35 years 0.73 0.54, 0.99 0.045 0.86 0.62, 1.19 0.365 Male recipient 1.50 1.10, 2.04 0.010 1.26 0.92, 1.72 0.150 Male donor 1.44 1.05, 1.97 0.025 1.31 0.95, 1.80 0.104 CMV positive recipient 0.94 0.67, 1.32 0.72 — — — CMV positive donor 2.32 1.19, 4.55 0.014 2.02 1.02, 4.00 0.044 HCT-CI ≥ 2 2.44 1.53, 3.91 < 0.001 2.04 1.22, 3.40 0.006 Leukemia factors WCC ≥ 20,000 per microliter 1.06 0.78, 1.44 0.708 — — — Secondary AML 2.33 1.52, 3.58 < 0.001 1.65 1.03, 2.65 0.036 Disease status at transplantatio 0.011 0.006 CR1 Ref Ref — Ref Ref — ≥CR2 1.56 1.14, 2.13 0.005 1.71 1.22, 2.38 0.002 NR 2.36 0.75, 7.47 0.140 1.59 0.49, 5.14 0.441 Genetic risk group < 0.001 < 0.001 CEBPA bZIP or CBF AML Ref Ref — Ref Ref — Others 2.18 0.96, 4.95 0.063 2.93 1.19, 7.26 0.020 inv(3)/t(3;3) or KMT2A-r 4.84 1.96, 12.0 < 0.001 7.23 2.53, 20.7 < 0.001 -5/del(5q) or TP53mut 11.50 4.60, 28.9 < 0.001 11.70 3.96, 34.8 < 0.001 Low-intensity remission induction 2.44 1.68, 3.53 < 0.001 2.10 1.37, 3.21 < 0.001 HSCT factors Prior allogeneic HSCT 1.61 0.87, 2.96 0.129 — — — Donor type 0.304 Matched sibling Ref Ref — Matched unrelated 0.98 0.65, 1.47 0.916 — — — Mismatched unrelated 1.45 0.94, 2.26 0.094 Haploidentical 1.20 0.79, 1.82 0.403 Mismatched HSCT 1.30 0.95, 1.78 0.103 — — — PBSC as graft source 1.08 0.76, 1.52 0.679 — — — Reduced intensity conditioning 1.25 0.91, 1.70 0.164 — — — PTCy-based GVHD prophylaxis 1.24 0.87, 1.75 0.232 — — — Open in a new tab HR hazards ratio for death, CI confidence interval, Ref reference group, CMV cytomegalovirus, HCT-CI hematopoietic cell transplantation-specific comorbidity index, AML acute myeloid leukemia, ELN EuropeanLeukemiaNet, CR1 first complete remission, NR non-remission, CEBPA bZIP bZIP in-frame mutations in CEBPA, CBF core-binding factor, KMT2A-r KMT2A-rearranged, mut mutated, HSCT hematopoietic stem cell transplantation, PBSC peripheral blood stem cells, PTCy post-transplantation cyclophosphamide, GVHD graft-versus-host disease Table 3. Prognostic score assignment based on overall survival in the training cohort after allogeneic hematopoietic stem cell transplantation Risk factors β coefficient Score points Donor CMV seropositivity 0.707 1 HCT-CI ≥ 2 0.694 1 Secondary AML 0.491 1 Non-CR1 at HSCT 0.568 1 Genetic risk group CEBPA bZIP or CBF AML — 0 Others 0.790 1 inv(3)/t(3;3) or KMT2A-r 1.643 3 -5/del(5q) or TP53 mut 2.221 4 Low-intensity remission induction 0.750 1 Open in a new tab CMV cytomegalovirus, HCT-CI hematopoietic cell transplantation-specific comorbidity index, AML acute myeloid leukemia, ELN European LeukemiaNet; non-CR1: second complete remission or beyond, and non-remission; CEBPA bZIP: bZIP in-frame mutations in CEBPA; CBF core-binding factor, KMT2A-r KMT2A-rearranged; mut mutated Fig. 2. Open in a new tab Development and performance of the novel Hong Kong Allogeneic HSCT Risk Score (HATS). A . Risk score distribution, with score of 2 occurring in the largest number of cases. B . Overall survival by HATS risk groups, showing discrete separation of survival curves between the four risk groups. C . Pairwise log-rank tests with adjusted p-values, showing significant differences between all four risk groups. D . HATS outperformed the Acute Myeloid Leukemia Hematopoietic Cell Transplant-composite Risk (AML-HCT-CR) in adjusted C-statistic. E . Time-dependent receiver operating characteristic curve (left) and area under time-dependent receiver operating characteristic curves (AUC) (right) of AML-HCT-CR and HATS, showing a significant difference. F . Re-stratification of risk groups from European LeukemiaNet (ELN) 2022 to HATS. G . Re-stratification of risk groups from AML-HCT-CR to HATS. HATS in the training cohort For the training cohort, there was a well-demarcated stepwise decrease in survivals when patients were divided into favorable ( N = 50); intermediate ( N = 313); poor ( N = 81) and very poor ( N = 22) risk groups (Fig. 2 B). Pairwise log-rank tests with adjusted P-values showed significant survival differences between each pair of risk groups (Fig. 2 C). For favorable, intermediate, poor and very poor risk groups, the respective median OS were not reached, not reached, 23 months and 7.3 months, and the respective 2-year OS were 92%, 78%, 50%, and 7.4% (Table 4 ); the respective 100-day cumulative incidences of grade 3–4 acute GVHD were 4.0%, 11%, 11%, and 9.1% ( P = 0.295), and the respective 2-year cumulative incidences of moderate-to-severe chronic GVHD were 25%, 35%, 22%, and 30% ( P = 0.166); the respective 2-year relapse-free survival rates were 92%, 72%, 38%, and 5.3% ( P < 0.001), and the respective 2-year GVHD-free, relapse-free survival rates were 65%, 37%, 25%, and 0% ( P < 0.001). Table 4. Overall survivals (OS) according to HATS Risk Group in the training cohort HATS risk group HR 95% CI P -value* Median OS 95% CI 24-month OS P -value† Favorable Ref Ref Ref NR -, - 92% Ref Intermediate 3.23 1.31, 7.94 0.011 NR 96, - 78% 0.007 Poor 7.90 3.14, 19.9 < 0.001 23 months 14, 87 50% < 0.001 Very Poor 26.3 9.75, 70.9 < 0.001 7.3 months 5.1, 15 7.4% < 0.001 Open in a new tab *Cox proportional hazards regression †Log-rank test. Overall P-value < 0.001; P < 0.05 is indicative significant differences in survival compared with reference (Ref) group HATS Hong Kong Allogeneic HSCT Risk Score, HR hazards ratio for death, CI confidence interval Comparison of HATS with other prognostication models Amongst the six existing models, AML-HCT-CR had the highest C statistic value (Fig. 1 B) and was therefore chosen as the comparator. Compared with AML-HCT-CR, HATS provided a significantly better prediction of the risk of death following allo-HSCT (adjusted C-statistic: 0.662 versus 0.599, P = 0.001; AUC for risk of death within 2 years: 0.696 versus 0.628, P = 0.008) (Fig. 2 D and E). Risk group re-distribution conceivably contributed to the improvement in survival prediction, with 78% of AML-HCT-CR low-risk cases re-classified as HATS intermediate-risk cases, and 32% of AML-HCT-CR intermediate-risk cases re-classified as HATS poor- or very poor-risk cases (Fig. 2 F). Because HATS assigns a high score to genetic/cytogenetic changes that are largely based on ELN 2022, risk group re-distribution between HATS and ELN 2022 was also examined. The results showed that 71% of ELN 2022 favorable-risk cases were re-classified as HATS intermediate-risk cases; and 21% of ELN 2022 intermediate-risk cases were re-classified as HATS favorable-risk (6%), poor-risk (14%), or very poor-risk (1.4%) cases. Notably, 55% of ELN adverse-risk cases were re-classified as HATS favorable-risk (7%) and intermediate-risk (48%) cases; with only 46% of cases remaining as HATS poor- or very poor-risk cases (Fig. 2 G). Therefore, the assessment of other relevant risk factors in HATS has significantly altered risk distribution based on genetics/cytogenetics as in ELN 2022, likely thereby improving the prognostic power. Validation cohort Three hundred and ninety-five consecutive AML patients (men, N = 203; women, N = 192) with a median age of 41 (interquartile range, 31–52) years were analyzed (Supplementary Table 4). The validation cohort compared with the training cohort had significantly younger recipients (median age: 41 versus 51 years, P < 0.001) and donors (median age: 32 versus 38 years, P < 0.001); more male donors (66% versus 57%, P = 0.006); higher donor CMV seropositivity rate (86% versus 70%, P < 0.001); better ELN 2022 genetic profiles (favorable: 30% versus 21%, P < 0.001; adverse: 17% versus 32%, P < 0.001); more low-risk and fewer high-risk groups ( CEBPA bZIP/CBF: 25% versus 13%, P < 0.001; -5/del(5q)/ TP53 mut : 3% versus 6%; P < 0.001); more haploidentical donors (77% versus 24%, P < 0.001); more peripheral blood HSCT (100% versus 75%, P < 0.001); fewer reduced-intensity conditioning regimens (14% versus 41%, P < 0.001); and no PTCy-based GVHD prophylaxis (0% versus 30%, P < 0.001) (Supplementary Table 5). Accordingly, the validation cohort mainly comprised low/intermediate-risk patients as assessed by existing prognostication models (Supplementary Table 6). HATS score ranged from 0 to 6, with 2 being the most common score ( N = 167, 42%) followed by 3 ( N = 101, 26%) and 1 ( N = 67, 17%) (Supplementary Fig. 3). Increasing HATS scores were associated with progressively inferior OS by Kaplan-Meier analysis (Supplementary Fig. 3). Correspondingly, the validation cohort was stratified into HATS favorable: 18% ( N = 73), intermediate: 68% ( N = 268), poor: 12% ( N = 47), and very poor: 2% ( N = 7) groups (Supplementary Table 6). Similar to the training cohort although differing from it in many baseline characteristics, the validation cohort was best prognosticated by HATS, which achieved the highest C-statistic of 0.626 in comparison with the other six existing prognostic models, with ELN 2022 achieving the nearest at 0.605 (Fig. 3 A). Risk group re-distribution was also evident, with 53% of ELN 2022 favorable-risk cases re-classified as HATS intermediate-risk cases, and 59% of ELN 2022 adverse-risk cases re-classified as HATS favorable-risk (7%) or intermediate-risk (52%) cases (Fig. 3 B). Compared with AML-HCT-CR, HATS provided a significantly better prediction of the risk of death following allo-HSCT: the AUC for risk of death within 2 years was 0.619 versus 0.541 ( P = 0.034) (Supplementary Fig. 4). Overall prediction error, measured by the IBS, was lower for HATS (0.150) compared with AML-HCT-CR (0.167). Over 0–24 months, the IBS was 0.092 for HATS and 0.110 for AML-HCT-CR, indicating better 2-year prediction accuracy for HATS, in addition to its superior discrimination. Furthermore, HATS outperformed ELN 2022 with superior discrimination of the favorable and intermediate groups. The 5-year OS of ELN 2022 favorable- and intermediate-risk groups were close at 84% (95% CI: 77–91%) and 74% (95% CI: 68–81%) (Fig. 3 C); whereas the 5-year OS of the HATS favorable-risk and intermediate-risk groups were better separated at 93% (95% CI: 87–99%) and 73% (95% CI: 67–79%) (Fig. 3 D). The 5-year OS of ELN 2022 adverse-risk group at 54% (95% CI: 42–69%) was comparable to those of HATS poor-risk and very poor-risk groups at 52% (95% CI: 39–69%) and 57% (95% CI: 30–100%) respectively (Fig. 3 C and D). Notably, the poor-risk and very poor-risk groups in the validation cohort, as compared with the training cohort, were not as well separated, probably because of fewer number of patients in these risk groups. Fig. 3. Open in a new tab HATS in the validation cohort. A . Performance of different prognostication models by C-statistic in the external validation cohort. HATS achieved the highest C-statistic value. B . Re-stratification of risk groups from ELN 2022 to HATS in the validation cohort. C . Overall survival of the validation cohort by ELN 2022 classification. D . Overall survival of the validation cohort by HATS risk groups. ELN: European LeukemiaNet; HCT-CI: Hematopoietic Cell Transplantation-Specific Comorbidity Index; DRI: Disease Risk Index; DRCI: Disease Risk Comorbidity Index; AML-DRG: Acute Myeloid Leukemia-specific Disease Risk Group; AML-HCR-CR: AML Hematopoietic Cell Transplant-composite Risk; HATS: Hong Kong Allogeneic HSCT Risk Score; C: concordance; Adj. C-stat: adjusted C-statistic; CI: confidence interval. Discussion In this study, we evaluated six existing prognostic models in a series of AML patients undergoing allo-HSCT. These models were selected because they are widely used, have been externally validated in allo‑HSCT, and could be accurately reconstructed from our available variables. With the same series as a training cohort, we performed multivariate analysis to define a more comprehensive panel of prognostic markers covering recipient, leukemia and donor related factors, thereby building a novel model HATS. The performance of HATS was compared with these models and shown to be superior to all six of them. HATS assigns scores to six different clinicopathologic parameters. The highest scores are assigned to genetic/cytogenetic changes. Of the aberrations examined, inv(3)(q21;q26)/t(3;3)(q21;q26), KMT2A rearrangements, -5/del(5q) and TP53 mut were associated with the worst survivals and hence given the highest scores. The dismal outcome of AML with TP53 mut even with allo-HSCT had previously been reported [ 28 , 29 ]. In fact, the negative prognostic impact of high-risk aberrations including − 5/del(5q), -7, -17/del(17p), and complex/monosomy karyotypes (CK/MK) could partly be mitigated by the absence of TP53 mutations [ 30 ]. Similarly, we also found that patients with − 7, -17/del(17p), or CK/MK had intermediate risks in the absence of TP53 mutations. However, -5/del(5q) in our cohort had outcome as poor as TP53 mut . Secondly, donor CMV seropositivity was identified as an independent risk factor. This aligned with recent studies demonstrating that CMV-seropositive donors conferred higher mortality risk, primarily through increased non-relapse mortality (NRM) and relapse risk [ 31 , 32 ]. However, donor CMV seropositivity should only be considered one independent adverse factor within HATS, whose impact needs to be balanced against other clinical and logistical considerations in donor-selection. The third risk factor is HCT-CI ≥ 2. Although HCT-CI has been extensively validated [ 5 ], its grouping of scores 1 and 2 together limits the discrimination in lower-risk patients. In fact, there was a substantial difference in NRM between patients with HCT-CI score of 0 and those with scores of 1 and 2 [ 5 ]. Hence, the lower threshold of ≥ 2 used in our study better discriminated patients with fewer comorbidities. The fourth risk factor is secondary AML, which has consistently been shown to have inferior post-HSCT survivals compared with de novo AML; [ 33 , 34 ] with myeloablative conditioning reported to partially mitigate the difference [ 34 ]. In our study, secondary AML remained a significant risk independent of other factors including conditioning regimens in multivariate analysis. The fifth risk factor is non-CR1 at the time of allo-HSCT. A recent matched-pair analysis of allo-HSCT for AML showed that 5-year OS was significantly higher in CR1 than in CR2 (76.7% versus 59.3%, P = 0.026) [ 35 ]. Furthermore, MRD-negative patients in CR2 had outcomes similar to MRD-positive patients in CR1 after allo-HSCT for AML, underscoring the adverse impact of later remission status [ 36 ]. The sixth risk factor is low-intensity induction therapy. In adult AML patients unfit for intensive chemotherapy, venetoclax in combination with hypomethylating agents or low-dose cytarabine had acceptable outcomes [ 37 , 38 ]. Although recent reports also showed similar survivals after allo-HCT for AML patients receiving low-intensity or intensive chemotherapy induction, these studies were limited by their retrospective nature and small study population [ 39 – 41 ]. Our finding that low-intensity induction was an independent risk requires further validation, especially in conjunction with MRD assessment to determine if suboptimal leukemia clearance by less intensive treatment might negatively impact on outcome after allo-HSCT. The validation cohort in this study differed significantly from the training cohort in multiple clinicopathologic features, including a younger median age for donors and recipients, fewer ELN 2022 adverse-risk diseases, more favorable genetic/cytogenetic profiles, and more haploidentical HSCT. Despite these differences, HATS performed best in prognostication as compared with existing models, demonstrating its robustness and applicability in patients with different clinicopathologic and genetic landscapes. With both the training and validation cohorts, a notable feature of HATS is its ability to identify a favorable group with excellent 2-year OS (training cohort: 92%; validation cohort:93%). It also identified a very high-risk group with a dismal 2-year OS of 7.4% in the training cohort. In the validation cohort, interpretation of a 2-year OS of 57% in the very high-risk group is limited by the small number of patients ( N = 7) in this category. Combining the “poor” and “very poor” risk groups may therefore be appropriate in future external cohorts that include only a small number of patients with high‑risk features. Another limitation is that pre‑transplant MRD was available in only a small subset of patients and was assessed using heterogeneous methods, precluding robust statistical analysis and inclusion in the final model. We acknowledge this and consider systematic, standardized MRD collection a key priority for future refinements and external validations of HATS. Nonetheless, adoption of the HATS helps to better inform allo-HSCT outcome based on patient, and donor, disease, and treatment factors. Furthermore, the very poor outcome in the high-risk group suggests that therapeutic strategies other than allo-HSCT should be actively pursued in these patients. Whether the HATS model is generalizable requires validation in more patient cohorts, particularly for factors not fully addressed in this study owing to sample limitations, including the independent negative impact of -5/del(5q) and low-intensity induction treatment on survival, the impact of active disease and high HCT-CI, and the significance of pre-HSCT MRD [ 42 , 36 , 17 , 18 ]. We conclude that HATS represents a novel prognostication system that has outperformed existing models. Its specific advantages, including discrete discrimination between favorable- and intermediate-risk groups, and the identification of a very-high risk group, are highly relevant in management decisions and patient counselling. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (2.2MB, docx) Acknowledgements Nil. Authors’ contributions GMKL and HG conceived the study. GMKL, YY, YL, JS, YZ, JPYS, YLK, HH and HG treated the patients. GMKL, YY, YL, JS, YZ, HH and HG collected and assembled the data. GMKL, YY, HH and HG analyzed and interpreted the data. All authors wrote and approved the final manuscript. Funding This study was not supported by any funding. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of the University of Hong Kong (UW 24–621) and Ethics Committee of Clinical Trial of the First Affiliated Hospital of Zhejiang University (IIT20200086C-R1) and was conducted in accordance to the Declaration of Helsinki. All patients gave informed consent to participate. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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