ArticleChest2024
Development and Validation of the Hospital Medicine Safety Sepsis Initiative Mortality Model.
Article in Chest, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Presenting Symptoms and Antibiotic Timing Among Hospitalized Adults With Presumed Sepsis.JAMA network open · 2026Article
- Association of Timely Antibiotics and Fluid Resuscitation With Increased Discharge to Home After Sepsis.Chest · 2026Observational
- Comorbidities, Weight-Based Initial Fluid Resuscitation, and Mortality in Patients With Sepsis.JAMA network open · 2026Article
- Epidemiologic Characteristics and Management of Sepsis Among Previously Healthy Patients.CHEST critical care · 2025Article
- Hybrid machine learning for real-time prediction of edema trajectory in large middle cerebral artery stroke.NPJ digital medicine · 2025Article
- Development and validation of a nutrition-integrated nomogram for predicting 28-day mortality in sepsis patients.Frontiers in nutrition · 2025Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundWhen comparing outcomes after sepsis, it is essential to account for patient case mix to make fair comparisons. We developed a model to assess risk-adjusted 30-day mortality in the Michigan Hospital Medicine Safety sepsis initiative (HMS-Sepsis). RESEARCH QUESTION: Can HMS-Sepsis registry data adequately predict risk of 30-day mortality? Do performance assessments using adjusted vs unadjusted data differ? STUDY DESIGN AND
methodsRetrospective cohort of community-onset sepsis hospitalizations in the HMS-Sepsis registry (April 2022-September 2023), with split derivation (70%) and validation (30%) cohorts. We fit a risk-adjustment model (HMS-Sepsis mortality model) incorporating acute physiologic, demographic, and baseline health data and assessed model performance using concordance (C) statistics, Brier scores, and comparisons of predicted vs observed mortality by deciles of risk. We compared hospital performance (first quintile, middle quintiles, fifth quintile) using observed vs adjusted mortality to understand the extent to which risk adjustment impacted hospital performance assessment.
resultsAmong 17,514 hospitalizations from 66 hospitals during the study period, 12,260 hospitalizations (70%) were used for model derivation and 5,254 hospitalizations (30%) were used for model validation. Thirty-day mortality for the total cohort was 19.4%. The final model included 13 physiologic variables, two physiologic interactions, and 16 demographic and chronic health variables. The most significant variables were age, metastatic solid tumor, temperature, altered mental status, and platelet count. The model C statistic was 0.82 for the derivation cohort, 0.81 for the validation cohort, and ≥ 0.78 for all subgroups assessed. Overall calibration error was 0.0%, and mean calibration error across deciles of risk was 1.5%. Standardized mortality ratios yielded different assessments than observed mortality for 33.9% of hospitals.
interpretationThe HMS-Sepsis mortality model showed strong discrimination and adequate calibration and reclassified one-third of hospitals to a different performance category from unadjusted mortality. Based on its strong performance, the HMS-Sepsis mortality model may aid in fair hospital benchmarking, assessment of temporal changes, and observational causal inference analysis.
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