Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study.

Autor:	van Dam PMEL; Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, the Netherlands., Lievens S; Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, the Netherlands., Zelis N; Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, the Netherlands., van Doorn WPTM; Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center, Maastricht, the Netherlands., Meex SJR; Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center, Maastricht, the Netherlands., Cals JWL; Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht University, the Netherlands., Stassen PM; Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, the Netherlands.; School for Cardiovascular Diseases (CARIM), Maastricht University, the Netherlands.
Jazyk:	angličtina
Zdroj:	Annals of medicine [Ann Med] 2023; Vol. 55 (2), pp. 2290211. Date of Electronic Publication: 2023 Dec 08.
DOI:	10.1080/07853890.2023.2290211
Abstrakt:	Introduction: Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients. Methods: In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients. Primary outcome was 31-day mortality, secondary outcomes were 1-day mortality, 7-day mortality, and a composite endpoint of 31-day mortality and admission to intensive care unit (ICU).The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Finally, the prediction models with the highest performance to predict 31-day mortality were selected to further examine calibration and appropriate clinical cut-off points. Results: We included 19 prediction models and applied these to 2185 ED patients. Thirty-one-day mortality was 10.6% (231 patients), 1-day mortality was 1.4%, 7-day mortality was 4.4%, and 331 patients (15.1%) met the composite endpoint. The RISE UP and COPE score showed similar and very good discriminatory performance for 31-day mortality (AUC 0.86), 1-day mortality (AUC 0.87), 7-day mortality (AUC 0.86) and for the composite endpoint (AUC 0.81). Both scores were well calibrated. Almost no patients with RISE UP and COPE scores below 5% had an adverse outcome, while those with scores above 20% were at high risk of adverse outcome. Some of the other prediction models (i.e. APACHE II, NEWS, WPSS, MEWS, EWS and SOFA) showed significantly higher discriminatory performance for 1-day and 7-day mortality than for 31-day mortality. Conclusions: Head-to-head validation of 19 prediction models in medical ED patients showed that the RISE UP and COPE score outperformed other models regarding 31-day mortality.
Databáze:	MEDLINE
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