ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2023
Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome: An International Multicenter Retrospective Study.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 12 citations in OpenAlex.
- A Real-Time Dynamic Warning Method for MODS in Trauma Sepsis Patients Based on a Pre-Trained Transfer Learning Algorithm.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction model for risk of multiple organ dysfunction syndrome in wasp sting patients: development and validation.Frontiers in medicine · 2026Article
- Development and validation of a MODS risk prediction model for organophosphorus poisoning patients.Frontiers in medicine · 2026Article
- GRACE-ICU: A multimodal nomogram-based approach for illness severity assessment of older adults in the ICU.NPJ digital medicine · 2025Article
- Predicting multiple organ dysfunction syndrome in trauma-induced sepsis: Nomogram and machine learning approaches.Journal of intensive medicine · 2025Article
- Explainable mortality prediction models incorporating social health determinants and physical frailty for heart failure patients.PloS one · 2025Article
- The evaluation of cytokines in predicting the organ injury of critically pediatric patients: a retrospective study.Translational pediatrics · 2024Article
- Illness severity assessment of older adults in critical illness using machine learning (ELDER-ICU): an international multicentre study with subgroup bias evaluation.The Lancet. Digital health · 2023Article
- Data heterogeneity in federated learning with Electronic Health Records: Case studies of risk prediction for acute kidney injury and sepsis diseases in critical care.PLOS digital health · 2023Article
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Authors and funding
16 authors at 9 institutions in 5 countries.
Funding
Abstract
backgroundMultiple organ dysfunction syndrome (MODS) is associated with a high risk of mortality among older patients. Current severity scores are limited in their ability to assist clinicians with triage and management decisions. We aim to develop mortality prediction models for older patients with MODS admitted to the ICU.
methodsThe study analyzed older patients from 197 hospitals in the United States and 1 hospital in the Netherlands. The cohort was divided into the young-old (65-80 years) and old-old (≥80 years), which were separately used to develop and evaluate models including internal, external, and temporal validation. Demographic characteristics, comorbidities, vital signs, laboratory measurements, and treatments were used as predictors. We used the XGBoost algorithm to train models, and the SHapley Additive exPlanations (SHAP) method to interpret predictions.
resultsThirty-four thousand four hundred and ninety-seven young-old (11.3% mortality) and 21 330 old-old (15.7% mortality) patients were analyzed. Discrimination AUROC of internal validation models in 9 046 U.S. patients was as follows: 0.87 and 0.82, respectively; discrimination of external validation models in 1 905 EUR patients was as follows: 0.86 and 0.85, respectively; and discrimination of temporal validation models in 8 690 U.S. patients: 0.85 and 0.78, respectively. These models outperformed standard clinical scores like Sequential Organ Failure Assessment and Acute Physiology Score III. The Glasgow Coma Scale, Charlson Comorbidity Index, and Code Status emerged as top predictors of mortality.
conclusionsOur models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.
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