Amphiregulin, ST2,and REG3α Biomarker Risk Algorithms as Predictors of Non-Relapse Mortality in Patients with Acute GVHD.

Graft-vs-host disease (GVHD) is a major cause of non-relapse mortality (NRM) following allogeneic hematopoietic cell transplant (HCT). Algorithms containing either the GI GVHD biomarker amphiregulin (AREG) or a combination of two GI GVHD biomarkers, (ST2+REG3α) when measured at GVHD diagnosis are validated predictors of NRM risk, but have never been assessed in the same patients using identical statistical methods. We measured serum concentrations of ST2, REG3, and AREG by ELISA at the time of GVHD diagnosis in 715 patients divided by date of transplant into training (2004-2015) and validation (2015-2017) cohorts. The training cohort (n=341) was used to develop algorithms for predicting probability of 12 month NRM that contained all possible combinations of 1-3 biomarkers and a threshold corresponding to the concordance probability was used to stratify patients for risk of NRM. Algorithms were compared to each other based on several metrics including the area under the receiver operating characteristics curve (AUC), proportion of patients correctly classified, sensitivity, and specificity using only the validation cohort (n=374). All algorithms were strong discriminators of 12 month NRM, whether or not patients were systemically treated (n=321). An algorithm containing only ST2+REG3α had the highest AUC (0.757), correctly classified the most patients (75%), and more accurately risk stratified those who developed Minnesota standard risk GVHD and for patients who received post-transplant cyclophosphamide-based prophylaxis. An algorithm containing only AREG more accurately risk stratified patients with Minnesota high risk GVHD. Combining ST2, REG3α, and AREG into a single algorithm did not improve performance.

The MAGIC algorithm probability predicts treatment response and long-term outcomes to second-line therapy for acute GVHD.

The significance of biomarkers at second-line treatment for acute graft-versus-host disease (GVHD) is not well characterized. We analyzed clinical data and serum samples at initiation of second-line systemic treatment of acute GVHD from 167 patients from 17 centers of the Mount Sinai Acute GVHD International Consortium (MAGIC) between 2016 and 2021. Sixty-two patients received ruxolitinib-based therapy while 102 received other systemic agents. In agreement with prospective trials, ruxolitinib resulted in higher day 28 (D28) ORR compared to non-ruxolitinib therapies (55% vs 31%, P=0.003) and patients who received ruxolitinib had significantly lower non-relapse mortality (NRM) than those who received non-ruxolitinib therapies (point estimates at 2-year: 35% vs 61%, p=0.002). Biomarker analyses demonstrated that the benefit from ruxolitinib was observed only in patients with low MAGIC algorithm probabilities (MAPs) at the start of second-line treatment. Among patients with a low MAP, those who received ruxolitinib experienced significantly lower NRM than those who received non-ruxolitinib therapies (point estimates at 2-year: 12% vs 41%, p=0.016). However, patients with a high MAP experienced high NRM regardless of treatment with ruxolitinib or non-ruxolitinib therapies (point estimates at 2-year: 67% vs 80%, p=0.65). A landmark analysis demonstrated that the relationship between D28 response and NRM largely depends on the MAP level at initiation of second-line therapy. In conclusion, the MAP measured at second-line systemic treatment for acute GVHD predicts treatment response and NRM. Outcomes of patients with high MAP are poor, regardless of treatment choice, and ruxolitinib appears to primarily benefit patients with low MAP.

Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias.

OBJECTIVE: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio. METHODS: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups. RESULTS: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups. CONCLUSIONS: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA_Docker.

Development and validation of a race-agnostic computable phenotype for kidney health in adult hospitalized patients.

Standard race adjustments for estimating glomerular filtration rate (GFR) and reference creatinine can yield a lower acute kidney injury (AKI) and chronic kidney disease (CKD) prevalence among African American patients than non-race adjusted estimates. We developed two race-agnostic computable phenotypes that assess kidney health among 139,152 subjects admitted to the University of Florida Health between 1/2012-8/2019 by removing the race modifier from the estimated GFR and estimated creatinine formula used by the race-adjusted algorithm (race-agnostic algorithm 1) and by utilizing 2021 CKD-EPI refit without race formula (race-agnostic algorithm 2) for calculations of the estimated GFR and estimated creatinine. We compared results using these algorithms to the race-adjusted algorithm in African American patients. Using clinical adjudication, we validated race-agnostic computable phenotypes developed for preadmission CKD and AKI presence on 300 cases. Race adjustment reclassified 2,113 (8%) to no CKD and 7,901 (29%) to a less severe CKD stage compared to race-agnostic algorithm 1 and reclassified 1,208 (5%) to no CKD and 4,606 (18%) to a less severe CKD stage compared to race-agnostic algorithm 2. Of 12,451 AKI encounters based on race-agnostic algorithm 1, race adjustment reclassified 591 to No AKI and 305 to a less severe AKI stage. Of 12,251 AKI encounters based on race-agnostic algorithm 2, race adjustment reclassified 382 to No AKI and 196 (1.6%) to a less severe AKI stage. The phenotyping algorithm based on refit without race formula performed well in identifying patients with CKD and AKI with a sensitivity of 100% (95% confidence interval [CI] 97%-100%) and 99% (95% CI 97%-100%) and a specificity of 88% (95% CI 82%-93%) and 98% (95% CI 93%-100%), respectively. Race-agnostic algorithms identified substantial proportions of additional patients with CKD and AKI compared to race-adjusted algorithm in African American patients. The phenotyping algorithm is promising in identifying patients with kidney disease and improving clinical decision-making.

Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classification of infant feeding status from clinical notes using Medical Subject Headings (MeSH) terms. Annotation of notes was completed using TeamTat to uniquely classify clinical notes according to infant feeding status. We trained 6 machine learning models to classify infant feeding status: logistic regression, random forest, XGBoost gradient descent, k-nearest neighbors, and support-vector classifier. Model comparison was evaluated based on overall accuracy, precision, recall, and F1 score. Our modeling corpus included an even number of clinical notes that was a balanced sample across each class. We manually reviewed 999 notes that represented 746 mother-infant dyads with a mean gestational age of 38.9 weeks and a mean maternal age of 26.6 years. The most frequent feeding status classification present for this study was exclusive breastfeeding [n = 183 (18.3%)], followed by exclusive formula bottle feeding [n = 146 (14.6%)], and exclusive feeding of expressed mother's milk [n = 102 (10.2%)], with mixed feeding being the least frequent [n = 23 (2.3%)]. Our final analysis evaluated the classification of clinical notes as breast, formula/bottle, and missing. The machine learning models were trained on these three classes after performing balancing and down sampling. The XGBoost model outperformed all others by achieving an accuracy of 90.1%, a macro-averaged precision of 90.3%, a macro-averaged recall of 90.1%, and a macro-averaged F1 score of 90.1%. Our results demonstrate that natural language processing can be applied to clinical notes stored in the electronic health records to classify infant feeding status. Early identification of breastfeeding status using NLP on unstructured electronic health records data can be used to inform precision public health interventions focused on improving lactation support for postpartum patients.

Pulmonary function as a continuum of risk: critical care utilization and survival after allogeneic hematopoietic stem cell transplantation - a multicenter cohort study.

Abnormal pre-transplant pulmonary function tests (PFTs) are associated with reduced survival after allogeneic HCT. Existing scoring systems consider risk dichotomously, attributing risk only to those with abnormal lung function. In a multicenter cohort of 1717 allo-HCT recipients, we examined the association between pre-transplant PFT measures and need for ICU admission (120d), frequency of mechanical ventilation (120d) and overall survival (5 y). Predictive models were developed and validated using Cox proportional hazards, incorporating age, FEV1 (forced expiratory volume in 1-second) and diffusing capacity (DLCO). In univariate analysis, hazard ratios for each outcome (95% CI) were: mechanical ventilation (FEV1: 0.60 [0.52-0.69], DLCO: 0.69 [0.61-0.77], p < 0.001), ICU admission (FEV1: 0.74 [0.67-0.82], DLCO: 0.79 [0.72-0.86], p < 0.001) and overall survival (FEV1: HR 0.87 [0.81-0.94], DLCO: 0.83 [0.77-0.89], p < 0.001). A multivariable Cox model was developed and compared to the HCT-CI Pulmonary score in a validation cohort. This model was better at predicting need for ICU admission and mechanical ventilation, while both models predicted overall survival (p < 0.001). In conclusion, the risk conferred by pre-transplant pulmonary function should be considered in a continuous rather than dichotomous manner. A more granular prognostication system can better inform risk of critical care utilization in the early post-HCT period.

A Validated Risk Stratification That Incorporates MAGIC Biomarkers Predicts Long-Term Outcomes in Pediatric Patients with Acute GVHD.

Acute graft versus host disease (GVHD) is a common and serious complication of allogeneic hematopoietic cell transplantation (HCT) in children but overall clinical grade at onset only modestly predicts response to treatment and survival outcomes. Two tools to assess risk at initiation of treatment were recently developed. The Minnesota risk system stratifies children for risk of nonrelapse mortality (NRM) according to the pattern of GVHD target organ severity. The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm of 2 serum biomarkers (ST2 and REG3α) predicts NRM in adult patients but has not been validated in a pediatric population. We aimed to develop and validate a system that stratifies children at the onset of GVHD for risk of 6-month NRM. We determined the MAGIC algorithm probabilities (MAPs) and Minnesota risk for a multicenter cohort of 315 pediatric patients who developed GVHD requiring treatment with systemic corticosteroids. MAPs created 3 risk groups with distinct outcomes at the start of treatment and were more accurate than Minnesota risk stratification for prediction of NRM (area under the receiver operating curve (AUC), .79 versus .62, P = .001). A novel model that combined Minnesota risk and biomarker scores created from a training cohort was more accurate than either biomarkers or clinical systems in a validation cohort (AUC .87) and stratified patients into 2 groups with highly different 6-month NRM (5% versus 38%, P < .001). In summary, we validated the MAP as a prognostic biomarker in pediatric patients with GVHD, and a novel risk stratification that combines Minnesota risk and biomarker risk performed best. Biomarker-based risk stratification can be used in clinical trials to develop more tailored approaches for children who require treatment for GVHD.

Cladribine plus cytarabine plus venetoclax in acute myeloid leukemia relapsed or refractory to venetoclax plus hypomethylating agent.

Not available.

Prognostic impact of SF3B1 mutation and multilineage dysplasia in myelodysplastic syndromes with ring sideroblasts: a Mayo Clinic study of 170 informative cases.

The revised 4th edition of the World Health Organization (WHO4R) classification lists myelodysplastic syndromes with ring sideroblasts (MDS-RS) as a separate entity with single lineage (MDS-RS-SLD) or multilineage (MDS-RS-MLD) dysplasia. The more recent International Consensus Classification (ICC) distinguishes between MDS with SF3B1 mutation (MDS-SF3B1) and MDS-RS without SF3B1 mutation; the latter is instead included under the category of MDS not otherwise specified. The current study includes 170 Mayo Clinic patients with WHO4R-defined MDS-RS, including MDS-RS-SLD (N=83) and MDS-RS-MLD (N=87); a subset of 145 patients were also evaluable for the presence of SF3B1 and other mutations, including 126 with (87%) and 19 (13%) without SF3B1 mutation. Median overall survival for all 170 patients was 6.6 years with 5- and 10-year survival rates of 59% and 25%, respectively. A significant difference in overall survival was apparent between MDS-RS-MLD and MDS-RS-SLD (p<0.01) but not between MDS-RS with and without SF3B1 mutation (p=0.36). Multivariable analysis confirmed the independent prognostic contribution of MLD (HR 1.8, 95% CI 1.1-2.8; p=0.01) and also identified age (p<0.01), transfusion need at diagnosis (p<0.01), and abnormal karyotype (p<0.01), as additional risk factors; the impact from SF3B1 or other mutations was not significant. Leukemia-free survival was independently affected by abnormal karyotype (p<0.01), RUNX1 (0.02) and IDH1 (p=0.01) mutations, but not by MLD or SF3B1 mutation. Exclusion of patients not meeting ICC-criteria for MDSSF3B1 did not change the observations on overall survival. MLD-based, as opposed to SF3B1 mutationbased, disease classification for MDS-RS might be prognostically more relevant.

Cardiac events in newly diagnosed acute myeloid leukaemia during treatment with venetoclax + hypomethylating agents.

Among 301 newly diagnosed patients with acute myeloid leukaemia receiving venetoclax and a hypomethylating agent, 23 (7.6%) experienced major cardiac complications: 15 cardiomyopathy, 5 non-ST elevation myocardial infarction and/or 7 pericarditis/effusions. Four patients had more than one cardiac complication. Baseline characteristics included median age ± interquartile range; 73 ± 5 years; 87% males; 96% with cardiovascular risk factors; and 90% with preserved baseline ejection fraction. In multivariate analysis, males were more likely (p = 0.02) and DNMT3A-mutated cases less likely (p < 0.01) to be affected. Treatment-emergent cardiac events were associated with a trend towards lower composite remission rates (43% vs. 62%; p = 0.09) and shorter survival (median 7.7 vs. 13.2 months; p < 0.01). These observations were retrospectively retrieved and warrant further prospective examination.

A Day 14 Endpoint for Acute GVHD Clinical Trials.

The overall response rate (ORR) 28 days after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). However, physicians often need to modify immunosuppression earlier than day (D) 28, and non-relapse mortality (NRM) does not always correlate with ORR at D28. We studied 1144 patients that received systemic treatment for GVHD in the Mount Sinai Acute GVHD International Consortium (MAGIC) and divided them into a training set (n=764) and a validation set (n=380). We used a recursive partitioning algorithm to create a Mount Sinai model that classifies patients into favorable or unfavorable groups that predicted 12 month NRM according to overall GVHD grade at both onset and D14. In the Mount Sinai model grade II GVHD at D14 was unfavorable for grade III/IV GVHD at onset and predicted NRM as well as the D28 standard response model. The MAGIC algorithm probability (MAP) is a validated score that combines the serum concentrations of suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha (REG3α) to predict NRM. Inclusion of the D14 MAP biomarker score with the D14 Mount Sinai model created three distinct groups (good, intermediate, poor) with strikingly different NRM (8%, 35%, 76% respectively). This D14 MAGIC model displayed better AUC, sensitivity, positive and negative predictive value, and net benefit in decision curve analysis compared to the D28 standard response model. We conclude that this D14 MAGIC model could be useful in therapeutic decisions and may offer an improved endpoint for clinical trials of acute GVHD treatment.

Flares of acute graft-versus-host disease: a Mount Sinai Acute GVHD International Consortium analysis.

ABSTRACT: The absence of a standardized definition for graft-versus-host disease (GVHD) flares and data on its clinical course are significant concerns. We retrospectively evaluated 968 patients across 23 Mount Sinai Acute GVHD International Consortium (MAGIC) transplant centers who achieved complete response (CR) or very good partial response (VGPR) within 4 weeks of treatment. The cumulative incidence of flares within 6 months was 22%, and flares were associated with a higher risk of nonrelapse mortality (NRM; adjusted hazard ratio [aHR], 4.84; 95% confidence interval [CI], 3.19-7.36; P < .001). Flares were more severe (grades 3/4, 41% vs 16%; P < .001) and had more frequent lower gastrointestinal (LGI) involvement (55% vs 32%; P < .001) than the initial GVHD. At CR/VGPR, elevated MAGIC biomarkers predicted the future occurrence of a flare, along with its severity and LGI involvement. In multivariate analyses, higher Ann Arbor (AA) biomarker scores at CR/VGPR were significant risk factors for flares (AA2 vs AA1: aHR, 1.81 [95% CI, 1.32-2.48; P = .001]; AA3 vs AA1: aHR, 3.14 [95% CI, 1.98-4.98; P < .001]), as were early response to initial treatment (aHR, 1.84; 95% CI, 1.21-2.80; P = .004) and HLA-mismatched unrelated donor (aHR, 1.74; 95% CI, 1.00-3.02; P = .049). MAGIC biomarkers also stratified the risk of NRM both at CR/VGPR and at the time of flare. We conclude that GVHD flares are common and carry a significant mortality risk. The occurrence of future flares can be predicted by serum biomarkers that may serve to guide adjustment and discontinuation of immunosuppression.

Genetic landscape and clinical outcomes of patients with BCOR mutated myeloid neoplasms.

The BCL6-corepressor (BCOR) is a tumor-suppressor gene located on the short arm of chromosome X. Data is limited regarding factors predicting survival in BCOR-mutated (mBCOR) acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). We evaluated 138 patients with mBCOR myeloid disorders, of which 36 (26.1%) had AML and 63 (45.6%) had MDS. Sixty-six (47.8%) patients had a normal karyotype while 18 (13%) patients had complex karyotype. BCOR-mutated MDS/AML were highly associated with RUNX1 and U2AF1 comutations. In contrast, TP53 mutation was infrequently seen with mBCOR MDS. Patients with an isolated BCOR mutation had similar survival compared to those with high-risk co-mutations by ELN 2022 criteria (median OS 1.16 vs. 1.27 years, P = 0.46). Complex karyotype adversely impacted survival among mBCOR AML/MDS (HR 4.12, P < 0.001), while allogeneic stem cell transplant (alloSCT) improved survival (HR 0.38, P = 0.04). However, RUNX1 co-mutation was associated with an increased risk of post-alloSCT relapse (HR 88.0, P = 0.02), whereas melphalan-based conditioning was associated with a decreased relapse-risk (HR 0.02, P = 0.01). We conclude that mBCOR is a high-risk feature across MDS/AML and that alloSCT improves survival in this population.

Temporal trends in critical care utilization and outcomes in allogeneic hematopoietic stem cell transplant recipients.

Historically, the prognosis of allogeneic hematopoietic stem cell transplant (allo-HCT) recipients who require intensive care unit (ICU) admission has been poor. We aimed to describe the epidemiological trends of ICU utilization and outcomes in allo-HCT patients. We conducted a retrospective cohort study including adults (≥ 18) undergoing allo-HCT between 01/01/2005 and 31/12/2020 at Mayo Clinic, Rochester. Temporal trends in outcomes were assessed by robust linear regression modelling. Risk factors for hospital mortality were chosen a priori and assessed with multivariable logistic regression modelling. Of 1,249 subjects, there were 486 ICU admissions among 287 individuals. Although older patients underwent allo-HCT (1.64 [95% CI: 1.11 to 2.45] years per year; P = 0.025), there was no change in ICU utilization over time (P = 0.91). The ICU and hospital mortality rates were 19.2% (55/287) and 28.2% (81/287), respectively. There was a decline in ICU mortality (-0.38% [95% CI: -0.70 to -0.06%] per year; P = 0.035). The 1-year post-HCT mortality for those requiring ICU admission was 56.1% (161/287), with no significant difference over time, versus 15.8% (141/891, 71 missing) among those who did not. The frequency and duration of invasive mechanical ventilation (IMV) declined. In multivariable analyses, higher serum lactate, higher sequential organ failure assessment (SOFA) scores, acute respiratory distress (ARDS), and need for IMV were associated with greater odds of hospital mortality. Over time, rates of ICU utilization have remained stable, despite increasing patient age. Several trends suggest improvement in outcomes, notably lower ICU mortality and frequency of IMV. However, long-term survival remains unchanged. Further work is needed to improve long-term outcomes in this population.

Venetoclax plus hypomethylating agents in DDX41-mutated acute myeloid leukaemia and myelodysplastic syndrome: Mayo Clinic series on 12 patients.

Venetoclax (VEN) is an FDA-approved selective inhibitor of B-cell leukaemia/lymphoma-2 (BCL-2), used for treating elderly or unfit acute myeloid leukaemia (AML) patients unable to undergo intensive chemotherapy. Combining VEN with hypomethylating agents (HMAs) has shown impressive response rates in high-risk myelodysplastic syndromes (MDS) and relapsed/refractory AML. However, the efficacy of VEN and HMAs in treating DDX41-mutated (mDDX41) MDS/AML patients remains uncertain. Despite the favourable prognostic nature of mDDX41 MDS/AML patients, there is a lack of clinical experience regarding their response to different treatment regimens, leading to an unknown optimal therapeutic approach.

Granularity in disease classification impacts survival prediction in advanced systemic mastocytosis: A single institution study of 329 informative cases.

The World Health Organization (WHO) classification system categorizes advanced systemic mastocytosis (SM-Adv) into aggressive SM (ASM), mast cell leukemia (MCL), and SM with associated hematological neoplasm (SM-AHN). By contrast, the International Consensus Classification (ICC) requires "immature" MC cytomorphology for the diagnosis of MCL and limits SM-AHN to myeloid neoplasms (SM-AMN). The current study includes 329 patients with SM-Adv (median age 65 years, range 18-88; males 58%): WHO subcategories SM-AHN (N = 212; 64%), ASM (N = 99; 30%), and MCL (N = 18; 6%); ICC subcategories SM-AMN (N = 190; 64%), ASM (N = 99; 33%), and MCL (N = 9; 3%); WHO-defined MCL with "mature" MC cytomorphology and SM-AHN associated with lymphoid neoplasms were operationally labeled as "MCL-mature" (N = 9) and SM-ALN (N = 22), respectively, and distinguished from ICC-defined MCL and SM-AMN. Multivariable analysis that included the Mayo alliance risk factors for survival in SM (age >60 years, anemia, thrombocytopenia, increased alkaline phosphatase) revealed more accurate survival prediction with the ICC versus WHO classification order: (i) survival was significantly worse with MCL-immature versus MCL-mature (hazard ratio [HR] 15; p < .01), (ii) prognostic distinction between MCL and SM-AHN/AMN was confirmed in the context of ICC (HR 9.3; p < .01) but not WHO classification order (p = .99), (iii) survival was similar between MCL-mature and SM-AMN (p = .18), and (iv) SM-AMN (HR 1.7; p < .01) but not SM-ALN (p = .37) was prognostically distinct from ASM. The current study provides evidence for the independent prognostic contribution of both the ICC system for SM-Adv and the Mayo alliance risk factors for survival in SM.

Lung Injury Prediction Model in Bone Marrow Transplantation: A Multicenter Cohort Study.

Rationale: Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives: To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods: Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results: Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (<24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870-0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828-0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782-0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831-0.914). Conclusions: A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.