Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department : a retrospective study
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:53 |
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| Enthalten in: |
Annals of medicine - 53(2021), 1 vom: 02. Dez., Seite 402-409 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
van Dam, Paul M E L [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 08.03.2021 Date Revised 02.11.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1080/07853890.2021.1891453 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM321881656 |
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| 100 | 1 | |a van Dam, Paul M E L |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department |b a retrospective study |
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| 500 | |a Date Completed 08.03.2021 | ||
| 500 | |a Date Revised 02.11.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a 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) | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Comparative Study | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Validation Study | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a emergency department | |
| 650 | 4 | |a mortality | |
| 650 | 4 | |a prediction | |
| 650 | 4 | |a prognosis | |
| 700 | 1 | |a Zelis, Noortje |e verfasserin |4 aut | |
| 700 | 1 | |a van Kuijk, Sander M J |e verfasserin |4 aut | |
| 700 | 1 | |a Linkens, Aimée E M J H |e verfasserin |4 aut | |
| 700 | 1 | |a Brüggemann, Renée A G |e verfasserin |4 aut | |
| 700 | 1 | |a Spaetgens, Bart |e verfasserin |4 aut | |
| 700 | 1 | |a van der Horst, Iwan C C |e verfasserin |4 aut | |
| 700 | 1 | |a Stassen, Patricia M |e verfasserin |4 aut | |
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