ArticleJournal of the American Medical Informatics Association : JAMIA2022
Inclusion of social determinants of health improves sepsis readmission prediction models.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities.International journal for equity in health · 2024Pooled it
- Article
- Exploring the role of place visitation big data on small area health measure estimation.Computational urban science · 2026Article
- Predicting Metabolic Dysfunction-Associated Fatty Liver Disease Phenotypes Among Adults: 2-Stage Contrastive Learning Method.JMIR medical informatics · 2025Article
- AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias.The journal of prevention of Alzheimer's disease · 2025Article
- Risk factors associated with return sepsis admission following emergency department discharge with infection.The American journal of emergency medicine · 2025Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Racial differences in outcomes among patients with septic shock: A national cohort study.World journal of critical care medicine · 2025Article
- Real-time prediction of intensive care unit patient acuity and therapy requirements using state-space modelling.Nature communications · 2025Article
- Development and prospective implementation of a large language model based system for early sepsis prediction.NPJ digital medicine · 2025Article
- Development and Prospective Implementation of a Large Language Model based System for Early Sepsis Prediction.medRxiv : the preprint server for health sciences · 2025Article
- Association of Racial Residential Segregation and Survival After Out-of-Hospital Cardiac Arrest in the United States.Journal of the American Heart Association · 2025Observational
- A Prospective Comparison of Large Language Models for Early Prediction of Sepsis.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Social Determinants of Sepsis Mortality in the United States: A Retrospective, Epidemiologic Analysis.medRxiv : the preprint server for health sciences · 2024Article
- Model-based estimation of individual-level social determinants of health and its applications in All of Us.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Development and validation of a machine learning-based model for post-sepsis frailty.ERJ open research · 2024Article
- APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model.Research square · 2024Article
- Prediction of Readmission Following Sepsis Using Social Determinants of Health.Critical care explorations · 2024Article
- Predicting Provider Workload Using Predicted Patient Risk Score and Social Determinants of Health in Primary Care Setting.Applied clinical informatics · 2024Article
- The role of place-based factors and other social determinants of health on adverse post-sepsis outcomes: a review of the literature.Frontiers in disaster and emergency medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveSepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable. MATERIALS AND
methodsData from patients enrolled in the AllofUs Research Program cohort from 35 hospitals were used to develop a multicenter validated sepsis-related unplanned readmission model that incorporates clinical and social determinants of health (SDH) to predict 30-day unplanned readmissions. Sepsis cases were identified using concepts represented in the Observational Medical Outcomes Partnership. The dataset included over 60 clinical/laboratory features and over 100 SDH features.
resultsIncorporation of SDH factors into our model of clinical and demographic features improves model area under the receiver operating characteristic curve (AUC) significantly (from 0.75 to 0.80; P < .001). Model-agnostic interpretability techniques revealed demographics, economic stability, and delay in getting medical care as important SDH predictive features of unplanned hospital readmissions. DISCUSSION: This work represents one of the largest studies of sepsis readmissions using objective clinical data to date (8935 septic index encounters). SDH are important to determine which sepsis patients are more likely to have an unplanned 30-day readmission. The AllofUS dataset provides granular data from a diverse set of individuals, making this model potentially more generalizable than prior models.
conclusionUse of SDH improves predictive performance of a model to identify which sepsis patients are at high risk of an unplanned 30-day readmission.
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