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

Acute graft-vs-host disease (GVHD) grading systems that use only clinical symptoms at treatment initiation such as 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 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=0.009) 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 three MAGIC composite scores that significantly improved prediction of NRM compared to Manhattan risk (AUC, 0.76 vs. 0.70, P=0.010). Each increase in MAGIC composite score also corresponded to a significant decrease in day 28 treatment response (80% vs. 63% vs. 30%, P<0.001). 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.

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.

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

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-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.

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 are 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 co-mutations. 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 European LeukemiaNet (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 duration (14 vs. 21 vs. 28 days) in combination with hypomethylating agent in newly diagnosed acute myeloid leukemia: Comparative analysis of response, toxicity, and survival.

Overall survival and response rates of 270 patients with newly diagnosed acute myeloid leukemia receiving venetoclax (Ven) plus hypomethylating agent, stratified by Ven dosing schedule (Cycle 1 Ven 14 vs. 21 vs. 28 days).

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.

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.

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.

Adjusting diffusing capacity for anemia in patients undergoing allogeneic HCT: a comparison of two methodologies.

BACKGROUND: Diffusing capacity (DLCO) measurements are affected by hemoglobin. Two adjustment equations are used: Cotes (recommended by ATS/ERS) and Dinakara (used in the hematopoietic stem cell transplantation comorbidity index [HCT-CI]). It is unknown how these methods compare, and which is better from a prognostication standpoint. STUDY DESIGN: This is a retrospective cohort of 1273 adult patients who underwent allogeneic HCT, completed a pre-transplant DLCO and had a concurrent hemoglobin measurement. Non-relapse mortality was measured using competing risk analysis. RESULTS: Patients had normal spirometry (FEV1 99.7% [IQR: 89.4-109.8%; FVC 100.1% [IQR: 91.0-109.6%] predicted), left ventricular ejection fraction (57.2[6.7]%) and right ventricular systolic pressure (30.1[7.0] mmHg). Cotes-DLCO was 85.6% (IQR: 76.5-95.7%) and Dinakara-DLCO was 103.6% (IQR: 90.7-117.2%) predicted. For anemic patients (Hb<10g/dL), Cotes-DLCO was 84.2% (IQR: 73.9-94.1%) while Dinakara-DLCO 111.0% (97.3-124.7%) predicted. Cotes-DLCO increased HCT-CI score for 323 (25.4%) and decreased for 4 (0.3%) patients. Cotes-DLCO was superior for predicting non-relapse mortality: for both mild (66-80% predicted, HR 1.55 [95%CI: 1.26-1.92, p < 0.001]) and moderate (<65% predicted, HR 2.11 [95%CI: 1.55-2.87, p<0.001]) impairment. In contrast, for Dinakara-DLCO, only mild impairment (HR 1.69 [95%CI 1.26-2.27, p < 0.001]) was associated with lower survival while moderate impairment was not (HR 1.44 [95%CI: 0.64-3.21, p = 0.4]). In multivariable analyses, after adjusting for demographics, hematologic variables, cardiac function and FEV1, Cotes-DLCO was predictive of overall survival at 1-year (OR 0.98 [95%CI: 0.97-1.00], p = 0.01), but Dinakara-DLCO was not (OR 1.00 [95%CI: 0.98-1.00], p = 0.20). CONCLUSION: The ERS/ATS recommended Cotes method likely underestimates DLCO in patients with anemia, whereas the Dinakara (used in the HCT-CI score) overestimates DLCO. The Cotes method is superior to the Dinakara method score in predicting overall survival and relapse-free survival in patients undergoing allogeneic HCT.

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.

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.

Surrogates of endothelial injury predict survival after post-transplant cyclophosphamide.

BACKGROUND: 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. OBJECTIVE: We evaluate the prognostic ability of the 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. STUDY DESIGN: 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. RESULTS: A total of 199 patients were evaluated. Patients with a high log2-EASIX score had a significantly higher cumulative incidence of NRM at 1 years after alloHCT (34.5% vs. 12.3%, P = 0.003). Competing risk analysis showed that a high log2-EASIX score (HR 2.92, 95% CI 1.38 - 6.17, P = 0.005) and pre-alloHCT hypertension (HR 2.15, 95% CI 1.06 - 4.36, P = 0.034) 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 - 4.33, P = 0.005) and high risk (HR 5.77, 95% CI 2.31 - 14.39, P < 0.001) 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]. CONCLUSION: 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.

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.

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.

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.