Predicting Future Organ Support Needs Using Longitudinal Emergency Department Data: A Proof-of-Concept Study

Background Prediction of organ support needs, rather than mortality or critical care transfer alone, may improve the utility of early warning scores (EWS). Existing EWS may have limited sensitivity in predicting organ support due to reliance on cross-sectional snapshots of patient physiology, limiting their ability to account for changes in patient status. We aimed to develop and compare novel models capable of using longitudinal clinical data to predict organ support or death (OSD) within 48 hours of hospital admission. Methods We leveraged a retrospective cohort of adult ED encounters at a U.S. quaternary academic medical center from March 1, 2022, to February 5, 2024. Encounters were included if patients were ≥ 18 years and admitted to a medical service; those receiving organ support in the ED were excluded. The primary outcome was a composite of vasopressor initiation, invasive mechanical ventilation, continuous renal replacement therapy, or death within 48 hours of admission. Performance metrics included AUROC, AUPRC, sensitivity, and specificity. Results 1.7% (549/32,329) experienced organ support or death within 48 hours of admission. The transformer-based neural net demonstrated the strongest overall performance, with an AUROC of 0.84 and AUPRC of 0.20, outperforming the baseline to National Early Warning Score 2 (NEWS2) with higher sensitivity for the primary outcome (0.78 vs. 0.61) while maintaining sufficient specificity (0.71 vs. 0.83). XGBoost and elastic-net regression showed similar improvements in sensitivity (both 0.83) with modest reductions in specificity relative to NEWS2 calculated at time of admission. Conclusions Organ support represents a potentially modifiable and temporally proximal marker of critical illness. Models trained to interpret longitudinal trends in clinical variables—rather than cross-sectional snapshots—may better mirror clinician reasoning.