COVID outcome prediction in the emergency department (COPE): using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19
Open Access
- 16 September 2021
- Vol. 11 (9), e051468
- https://doi.org/10.1136/bmjopen-2021-051468
Abstract
Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model—COVID outcome prediction in the emergency department (COPE)—with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.Funding Information
- Patient-Centered Outcomes Research Institute (ME-1606-35555)
- ZonMw (10430 01 201 0019)
This publication has 23 references indexed in Scilit:
- Geographic and temporal validity of prediction models: different approaches were useful to examine model performanceJournal of Clinical Epidemiology, 2016
- A new concordance measure for risk prediction models in external validation settingsStatistics in Medicine, 2016
- Prediction models need appropriate internal, internal–external, and external validationJournal of Clinical Epidemiology, 2015
- Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD StatementBMC Medicine, 2015
- Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and ElaborationAnnals of Internal Medicine, 2015
- Regression Modeling StrategiesPublished by Springer Science and Business Media LLC ,2015
- Individualized Cost-Effectiveness AnalysisPLoS Medicine, 2011
- REALCOM-IMPUTESoftware for Multilevel Multiple Imputation with Mixed Response TypesJournal of Statistical Software, 2011
- What do we mean by validating a prognostic model?Statistics in Medicine, 2000