Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study
Related Experiment Video
Updated: Nov 16, 2025
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Paul M E L van Dam1, Noortje Zelis1, Sander M J van Kuijk2
1Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Insights
The RISE UP score and 4C mortality score effectively predict 30-day mortality in COVID-19 patients, aiding in identifying high-risk individuals for better resource allocation.
Area of Science:
- Medical research
- Clinical prediction models
- Public health
Background:
- Coronavirus disease 2019 (COVID-19) significantly burdens healthcare systems.
- Effective patient triaging is crucial for managing COVID-19 cases.
- Prediction models can aid in assessing patient risk and guiding clinical decisions.
Purpose of the Study:
- To evaluate the effectiveness of various prediction models for COVID-19 patients in the emergency department.
- To identify models that accurately predict mortality and severe outcomes.
Main Methods:
- Retrospective study of 403 COVID-19 patients in the emergency department.
- Assessment of eleven pre-selected prediction models for feasibility.
- Primary outcome: 30-day mortality; Secondary outcomes: 14-day mortality, composite endpoint of 30-day mortality and ICU/MCU admission.
- Performance measured by Area Under the Curve (AUC).
Main Results:
- 30-day mortality was 23.6%, 14-day mortality was 19.1%.
- The RISE UP score and 4C mortality score demonstrated strong discriminatory performance for 30-day mortality (AUCs 0.83 and 0.84).
- These two models significantly outperformed other evaluated prediction models.
Conclusions:
- The RISE UP score and 4C mortality score are valuable tools for identifying COVID-19 patients at high risk of poor outcomes.
- These validated models can assist in clinical decision-making and resource allocation within healthcare settings.
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.
More Related Videos
Related Concept Videos
01:18Steps in Outbreak Investigation
01:29Pulmonary Embolism II: Diagnostic Studies and Interprofessional Care