Development and validation of a simplified risk score for the prediction of critical COVID-19 illness in newly diagnosed patients

  • Scores to identify patients at high risk of progression of coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), may become instrumental for clinical decision-making and patient management. We used patient data from the multicentre Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) and applied variable selection to develop a simplified scoring system to identify patients at increased risk of critical illness or death. A total of 1946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n = 1297 and n = 649, respectively). Stability selection from over 100 baseline predictors for the combined endpoint of progression to the critical phase or COVID-19-related death enabled the development of a simplified score consisting of five predictors: C-reactive protein (CRP), age, clinical disease phase (uncomplicated vs. complicated), serum urea, and D-dimer (abbreviated as CAPS-D score). This score yielded an area under the curve (AUC) of 0.81 (95% confidence interval [CI]: 0.77–0.85) in the validation cohort for predicting the combined endpoint within 7 days of diagnosis and 0.81 (95% CI: 0.77–0.85) during full follow-up. We used an additional prospective cohort of 682 patients, diagnosed largely after the “first wave” of the pandemic to validate the predictive accuracy of the score and observed similar results (AUC for the event within 7 days: 0.83 [95% CI: 0.78–0.87]; for full follow-up: 0.82 [95% CI: 0.78–0.86]). An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was thus established and validated.
Author:Stanislas WerfelORCiDGND, Carolin Ellen Marianne JakobORCiDGND, Stefan BorgmannGND, Jochen SchneiderGND, Christoph Daniel SpinnerORCiDGND, Maximilian SchonsORCiD, Martin HowerORCiDGND, Kai WilleORCiDGND, Martina Maria HaselbergerGND, Hanno HeuzerothORCiDGND, Maria Madeleine RüthrichGND, Sebastian Conrad Johannes DolffORCiDGND, Johanna KesselGND, Uwe HeemannGND, Jörg Janne VehreschildORCiDGND, Siegbert RiegORCiDGND, Christoph Schmaderer
Parent Title (English):Journal of medical virology
Place of publication:Bognor Regis [u.a.]
Document Type:Article
Date of Publication (online):2021/07/31
Date of first Publication:2021/07/31
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Contributing Corporation:LEOSS study group
Release Date:2022/03/24
Tag:COVID‐19; logistic models; machine learning; risk factors
Page Number:11
First Page:6703
Last Page:6713
Funding information: German Centre for Infection Research; Willy Robert Pitzer Foundation
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung-Nicht kommerziell - Keine Bearbeitung 4.0