Enhancing the accuracy of a multivariable prediction model to identify medical patients suitable for same day emergency care services

OBJECTIVES: To assess the performance of the Glasgow admission prediction score (GAPS) and ambulatory score (AmbS) for identifying emergency department (ED) attendances suitable for medical same day emergency care (SDEC) services and to derive and validate a novel tool for this purpose, the SDEC Triage Tool (SDEC-T). METHODS: A retrospective diagnostic study using routine healthcare data from three hospitals in a diverse urban setting (Birmingham, UK). All unplanned ED attendances by adults requiring internal medicine assessment were included. The primary outcome was suitability for SDEC, defined as discharged alive with a length of stay under 12 hours (LOS<12). The SDEC-T was derived using multivariable analysis, informed by stakeholder workshops. RESULTS: 152 877 attendances were included (median age: 58 years; 54.3% female; 68.4% White ethnicity); LOS <12 was achieved in 45.0% (n=68 752). The GAPS and AmbS had moderate predictive accuracy, with areas under the receiver operating characteristic curve (AUROCs) of 0.741 (95% CI 0.738 to 0.744) and 0.733 (95% CI 0.730 to 0.736), respectively. The SDEC-T comprised elements of the GAPS, AmbS, National Early Warning Score 2 (NEWS2) and presenting complaint and achieved an AUROC of 0.850 (95% CI 0.845 to 0.854) on internal validation. Stakeholders considered the tool acceptable and suitable for deployment across settings. DISCUSSION: The SDEC-T demonstrated improved discrimination using routinely available variables, balancing predictive performance with clinical practicality. Its design supports implementation across hospitals with varying digital maturity. CONCLUSIONS: In a diverse patient cohort, the SDEC-T outperformed existing tools for identifying patients suitable for medical SDEC services.