COVID Outcome Prediction in the Emergency Department (COPE): Development and validation of a model for predicting death and need for intensive care in COVID-19 patients

  1. David van Klaveren
  2. Alexandros Rekkas
  3. Jelmer Alsma
  4. Rob JCG Verdonschot
  5. Dick TJJ Koning
  6. Marlijn JA Kamps
  7. Tom Dormans
  8. Robert Stassen
  9. Sebastiaan Weijer
  10. Klaas-Sierk Arnold
  11. Benjamin Tomlow
  12. Hilde RH de Geus
  13. Rozemarijn L van Bruchem-Visser
  14. Jelle R Miedema
  15. Annelies Verbon
  16. Els van Nood
  17. David M Kent
  18. Stephanie CE Schuit
  19. Hester Lingsma
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Abstract

Background and aim

COVID-19 is putting extraordinary pressure on emergency departments (EDs). To support decision making in the ED, we aimed to develop a simple and valid model for predicting mortality and need for intensive care unit (ICU) admission in suspected COVID-19 patients.

Methods

For model development, we retrospectively collected data of patients who were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020 (first wave of the pandemic). Based on prior literature we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of the need for ICU admission, both within 28 days after hospital admission. We assessed model performance (Area Under the ROC curve (AUC); calibration plots) with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing values.

Results

The development data included 5,831 patients, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. ICU admission was fully recorded for 2,633 first wave patients in 2 hospitals, with 214 (8%) ICU admissions within 28 days. A simple model – COVID Outcome Prediction in the Emergency Department (COPE) – with age, respiratory rate, C-reactive protein, lactic dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well-calibrated and showed good discrimination in 3,252 second wave patients (AUC in 4 hospitals: 0.82 [0.78; 0.86]; 0.82 [0.74; 0.90]; 0.79 [0.70; 0.88]; 0.83 [0.79; 0.86]). COPE was also able to identify patients at high risk of needing IC in 706 second wave patients with complete information on ICU admission (AUC: 0.84 [0.78; 0.90]; 0.81 [0.66; 0.95]). The models are implemented in web-based and mobile applications.

Conclusion

COPE is a simple tool that is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

CONTRIBUTION TO THE LITERATURE

What is already known on this topic

  • Prediction models have the potential to support decision making about hospital admission of patients presenting to the emergency department with suspected COVID-19

  • Most currently available models that were independently assessed contain a high risk of bias

  • Promising models were developed in different patient selections and included predictors that are not quickly and objective obtainable in emergency departments

What this study adds

  • A simple and objective tool (“COPE”) is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19

  • COPE may support ED physicians to identify high-risk patients – i.e. those at high risk of deterioration and/or death – requiring treatment in the ICU, intermediate-risk patients requiring admission to the clinical ward, and low-risk patients who can potentially be sent home

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