Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study
| Autor: | Renée A. G. Brüggemann, Aimée E. M. J. H. Linkens, Paul M. E. L. van Dam, Sander M. J. van Kuijk, Iwan C. C. van der Horst, Patricia M. Stassen, Bart Spaetgens, Noortje Zelis |
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| Přispěvatelé: | MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), Interne Geneeskunde, RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, RS: CAPHRI - R2 - Creating Value-Based Health Care, Epidemiologie, MUMC+: KIO Kemta (9), RS: Carim - B04 Clinical thrombosis and Haemostasis, MUMC+: MA Alg Interne Geneeskunde (9), RS: Carim - V04 Surgical intervention, Intensive Care, MUMC+: MA Medische Staf IC (9), MUMC+: MA Intensive Care (3), RS: CAPHRI - R5 - Optimising Patient Care |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Male
2019-20 coronavirus outbreak medicine.medical_specialty Coronavirus disease 2019 (COVID-19) emergency department 030204 cardiovascular system & hematology Outcome (game theory) Risk Assessment DISEASE VALIDATION RISK STRATIFICATION 03 medical and health sciences 0302 clinical medicine SCORE Medicine Humans 030212 general & internal medicine Hospital Mortality Aged Netherlands Retrospective Studies CURB-65 PNEUMONIA SEVERITY SEPSIS business.industry SARS-CoV-2 COVID-19 Retrospective cohort study General Medicine Emergency department prediction Length of Stay Middle Aged Prognosis mortality Term (time) SEVERITY Logistic Models ROC Curve Emergency medicine Emergency Medicine Feasibility Studies Female business Emergency Service Hospital Predictive modelling Healthcare system Research Article |
| Zdroj: | Annals of Medicine article-version (VoR) Version of Record Annals of Medicine, 53(1), 402-409. Routledge/Taylor & Francis Group |
| ISSN: | 1365-2060 0785-3890 |
| Popis: | Introduction Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). Methods In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). Results We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. Conclusion The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources. |
| Databáze: | OpenAIRE |
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