Differences in Acute Graft-Versus-Host Disease (GVHD) Severity and Its Outcomes Between Black and White Patients.

Acute graft-versus-host disease (GVHD) is a significant complication following hematopoietic stem cell transplantation (HCT). Although recent advancements in GVHD prophylaxis have resulted in successful HCT across HLA barriers and expanded access to HCT for racial minorities, less is known about how race affects the severity and outcomes of acute GVHD. This study examines differences in the clinical course of acute GVHD and the prognostic value of GVHD biomarkers for Black and White recipients. We conducted a retrospective analysis of patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) database who underwent HCT between 2014 and 2021 to describe the difference in clinical course of acute GVHD and significance of GVHD biomarkers between Black and White recipients. We used propensity score matching to generate a 1:3 matched cohort of 234 Black patients and 702 White patients with similar baseline characteristics. In the first year after HCT Black patients experienced a higher cumulative incidence of grade III-IV acute GVHD (17% versus 12%, P = 0.050), higher nonrelapse mortality (NRM; 18% versus 12%, P = .009), and lower overall survival that trended toward statistical significance (73% versus 79%, P = .071) compared to White patients. The difference in NRM in the first year was even greater among Black patients who developed GVHD than White patients (24% versus 14%, P = .041). The distribution of low, intermediate, and high MAGIC biomarker scores at the time of treatment was similar across racial groups (P = .847), however, Black patients with high biomarker scores experienced significantly worse NRM than White patients (71% versus 32%, P = .010). Our data indicate that Black patients are at a higher risk of NRM following HCT, primarily from a higher incidence of severe GVHD. Serum biomarkers at treatment initiation can stratify patients for risk of NRM across races, however Black patients with high biomarker scores had a significantly greater NRM risk. These results suggest a need for strategies that mitigate the higher risk for poor GVHD outcomes among Black patients.

Concretizing plan specifications as realizables within the OBO foundry.

BACKGROUND: Within the Open Biological and Biomedical Ontology (OBO) Foundry, many ontologies represent the execution of a plan specification as a process in which a realizable entity that concretizes the plan specification, a "realizable concretization" (RC), is realized. This representation, which we call the "RC-account", provides a straightforward way to relate a plan specification to the entity that bears the realizable concretization and the process that realizes the realizable concretization. However, the adequacy of the RC-account has not been evaluated in the scientific literature. In this manuscript, we provide this evaluation and, thereby, give ontology developers sound reasons to use or not use the RC-account pattern. RESULTS: Analysis of the RC-account reveals that it is not adequate for representing failed plans. If the realizable concretization is flawed in some way, it is unclear what (if any) relation holds between the realizable entity and the plan specification. If the execution (i.e., realization) of the realizable concretization fails to carry out the actions given in the plan specification, it is unclear under the RC-account how to directly relate the failed execution to the entity carrying out the instructions given in the plan specification. These issues are exacerbated in the presence of changing plans. CONCLUSIONS: We propose two solutions for representing failed plans. The first uses the Common Core Ontologies 'prescribed by' relation to connect a plan specification to the entity or process that utilizes the plan specification as a guide. The second, more complex, solution incorporates the process of creating a plan (in the sense of an intention to execute a plan specification) into the representation of executing plan specifications. We hypothesize that the first solution (i.e., use of 'prescribed by') is adequate for most situations. However, more research is needed to test this hypothesis as well as explore the other solutions presented in this manuscript.

Surrogates of Endothelial Injury Predict Survival After Post-transplant Cyclophosphamide.

Post-transplant cyclophosphamide (PT-Cy) is becoming the standard of care for preventing graft-versus-host disease (GVHD) following allogeneic hematopoietic stem cell transplant (alloHCT). Cyclophosphamide is associated with endothelial injury. We hypothesized that the endothelial activation and stress index (EASIX) score, being a marker of endothelial dysfunction, will predict non-relapse mortality (NRM) in alloHCT patients receiving PT-Cy for GVHD prophylaxis. We evaluate the prognostic ability of the hematopoietic cell transplantation-specific comorbidity index (HCT-CI) and EASIX scores, and report other factors influencing survival, in patients with hematologic malignancies undergoing alloHCT and receiving PT-Cy-based GVHD prophylaxis. Adult patients with hematologic malignancies who underwent alloHCT and received PT-Cy for GVHD prophylaxis at the three Mayo Clinic locations were included in this study. We retrospectively reviewed the Mayo Clinic database and the available electronic medical records to determine the patient, disease, and transplant characteristics. An HCT-CI score of ≥3 was considered high. The EASIX score was calculated from labs available between day -28 (of alloHCT) to the day of starting conditioning and analyzed on log2 transformed values. A log2-EASIX score ≥2.32 was considered high. The cumulative incidence of NRM was determined using competing risk analysis, with relapse considered as competing risk. Overall survival (OS) from transplant was determined using Kaplan-Meier and log-rank methods. Cox-proportional hazard method was used to evaluate factors impacting survival. A total of 199 patients were evaluated. Patients with a high log2-EASIX score had a significantly higher cumulative incidence of NRM at 1 year after alloHCT (34.5% versus 12.3%, P = .003). Competing risk analysis showed that a high log2-EASIX score (HR 2.92, 95% CI 1.38 to 6.17, P = .005) and pre-alloHCT hypertension (HR 2.15, 95% CI 1.06 to 4.36, P = .034) were independently predictive of 1 year-NRM. Accordingly, we combined the two factors to develop a composite risk model stratifying patients in low, intermediate, and high-risk groups: 111 (55.8%) patients were considered low-risk, 76 (38.2%) were intermediate and 12 (6%) were high-risk. Compared to patients in the low-risk group, the intermediate (HR 2.38, 95% CI 1.31 to 4.33, P = .005) and high-risk (HR 5.77, 95% CI 2.31 to 14.39, P < .001) groups were associated with a significantly inferior 1-year OS. Multiorgan failure (MOF) was among the common causes of NRM (14/32, 43.8%) particularly among patients with prior pulmonary comorbidities [7 (50%) patients]. Our study shows that EASIX score is predictive of survival after PT-Cy. The novel EASIX-HTN composite risk model may stratify patients prior to transplant. MOF is a common cause of NRM in patients receiving PT-Cy, particularly among patients with pulmonary comorbidities.

Community-engaged artificial intelligence research: A scoping review.

The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Novel MAGIC composite scores using both clinical symptoms and biomarkers best predict treatment outcomes of acute GVHD.

ABSTRACT: Acute graft-versus-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as the Minnesota risk identify standard and high-risk categories but lack a low-risk category suitable to minimize immunosuppressive strategies. We developed a new grading system that includes a low-risk stratum based on clinical symptoms alone and determined whether the incorporation of biomarkers would improve the model's prognostic accuracy. We randomly divided 1863 patients in the Mount Sinai Acute GVHD International Consortium (MAGIC) who were treated for GVHD into training and validation cohorts. Patients in the training cohort were divided into 14 groups based on similarity of clinical symptoms and similar nonrelapse mortality (NRM); we used a classification and regression tree (CART) algorithm to create three Manhattan risk groups that produced a significantly higher area under the receiver operating characteristic curve (AUC) for 6-month NRM than the Minnesota risk classification (0.69 vs 0.64, P = .009) in the validation cohort. We integrated serum GVHD biomarker scores with Manhattan risk using patients with available serum samples and again used a CART algorithm to establish 3 MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs 0.70, P = .010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs 63% vs 30%, P < .001). We conclude that the MAGIC composite score more accurately predicts response to therapy and long-term outcomes than systems based on clinical symptoms alone and may help guide clinical decisions and trial design.

Association of Sociodemographic Factors With Overtriage, Undertriage, and Value of Care After Major Surgery.

Objective: To determine whether certain patients are vulnerable to errant triage decisions immediately after major surgery and whether there are unique sociodemographic phenotypes within overtriaged and undertriaged cohorts. Background: In a fair system, overtriage of low-acuity patients to intensive care units (ICUs) and undertriage of high-acuity patients to general wards would affect all sociodemographic subgroups equally. Methods: This multicenter, longitudinal cohort study of hospital admissions immediately after major surgery compared hospital mortality and value of care (risk-adjusted mortality/total costs) across 4 cohorts: overtriage (N = 660), risk-matched overtriage controls admitted to general wards (N = 3077), undertriage (N = 2335), and risk-matched undertriage controls admitted to ICUs (N = 4774). K-means clustering identified sociodemographic phenotypes within overtriage and undertriage cohorts. Results: Compared with controls, overtriaged admissions had a predominance of male patients (56.2% vs 43.1%, P < 0.001) and commercial insurance (6.4% vs 2.5%, P < 0.001); undertriaged admissions had a predominance of Black patients (28.4% vs 24.4%, P < 0.001) and greater socioeconomic deprivation. Overtriage was associated with increased total direct costs [$16.2K ($11.4K-$23.5K) vs $14.1K ($9.1K-$20.7K), P < 0.001] and low value of care; undertriage was associated with increased hospital mortality (1.5% vs 0.7%, P = 0.002) and hospice care (2.2% vs 0.6%, P < 0.001) and low value of care. Unique sociodemographic phenotypes within both overtriage and undertriage cohorts had similar outcomes and value of care, suggesting that triage decisions, rather than patient characteristics, drive outcomes and value of care. Conclusions: Postoperative triage decisions should ensure equality across sociodemographic groups by anchoring triage decisions to objective patient acuity assessments, circumventing cognitive shortcuts and mitigating bias.

FLT3 inhibitors potentially improve response rates in acute myeloid leukemia harboring t(6;9)(DEK::NUP214): The Mayo Clinic experience.

Not available.

Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines.

OBJECTIVE: To use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. METHOD: We implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. RESULTS: Precision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. CONCLUSION: Algorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

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

ABSTRACT: The significance of biomarkers in second-line treatment for acute graft-versus-host disease (GVHD) has not been well characterized. We analyzed clinical data and serum samples at the 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, whereas 102 received other systemic agents. In agreement with prospective trials, ruxolitinib resulted in a higher day 28 (D28) overall response Frate than nonruxolitinib therapies (55% vs 31%, P = .003) and patients who received ruxolitinib had significantly lower nonrelapse mortality (NRM) than those who received nonruxolitinib therapies (point estimates at 2-year: 35% vs 61%, P = .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 nonruxolitinib therapies (point estimates at 2-year: 12% vs 41%, P = .016). However, patients with high MAP experienced high NRM regardless of treatment with ruxolitinib or nonruxolitinib therapies (point estimates at 2-year: 67% vs 80%, P = .65). A landmark analysis demonstrated that the relationship between the D28 response and NRM largely depends on the MAP level at the initiation of second-line therapy. In conclusion, MAP measured at second-line systemic treatment for acute GVHD predicts treatment response and NRM. The outcomes of patients with high MAP are poor regardless of treatment choice, and ruxolitinib appears to primarily benefit patients with low MAP.

Amphiregulin, ST2, and REG3α biomarker risk algorithms as predictors of nonrelapse mortality in patients with acute GVHD.

ABSTRACT: Graft-versus-host disease (GVHD) is a major cause of nonrelapse mortality (NRM) after allogeneic hematopoietic cell transplantation. Algorithms containing either the gastrointestinal (GI) GVHD biomarker amphiregulin (AREG) or a combination of 2 GI GVHD biomarkers (suppressor of tumorigenicity-2 [ST2] + regenerating family member 3 alpha [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 the serum concentrations of ST2, REG3α, and AREG by enzyme-linked immunosorbent assay at the time of GVHD diagnosis in 715 patients divided by the date of transplantation into training (2004-2015) and validation (2015-2017) cohorts. The training cohort (n = 341) was used to develop algorithms for predicting the probability of 12-month NRM that contained all possible combinations of 1 to 3 biomarkers and a threshold corresponding to the concordance probability was used to stratify patients for the risk of NRM. Algorithms were compared with each other based on several metrics, including the area under the receiver operating characteristics curve, 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 area under the receiver operating characteristics curve (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 posttransplant 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.

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.

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.

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.

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-RSMLD (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 (hazard ratio=1.8, 95% confidence interval: 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 (P=0.02) and IDH1 (P=0.01) mutations, but not by MLD or SF3B1 mutation. Exclusion of patients not meeting ICC-criteria for MDS-SF3B1 did not change the observations on overall survival. MLD-based, as opposed to SF3B1 mutation-based, disease classification for MDS-RS might be prognostically more relevant.

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.