J Med Virol . Development and Validation of a Simplified Risk Score for the Prediction of Critical COVID-19 Illness in Newly Diagnosed Patients

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J Med Virol


. 2021 Jul 31.
doi: 10.1002/jmv.27252. Online ahead of print.
Development and Validation of a Simplified Risk Score for the Prediction of Critical COVID-19 Illness in Newly Diagnosed Patients


Stanislas Werfel ¹ , Carolin E M Jakob ^{2 3} , Stefan Borgmann ⁴ , Jochen Schneider ^{5 6} , Christoph Spinner ^{5 6} , Maximilian Schons ² , Martin Hower ⁷ , Kai Wille ⁸ , Martina Haselberger ⁹ , Hanno Heuzeroth ¹⁰ , Maria M Rüthrich ¹¹ , Sebastian Dolff ¹² , Johanna Kessel ¹³ , Uwe Heemann ¹ , Jörg Janne Vehreschild ^{2 3 13} , Siegbert Rieg ¹⁴ , Christoph Schmaderer ¹ , LEOSS study group



Affiliations

• PMID: 34331717
• DOI: 10.1002/jmv.27252

Abstract

Objectives: 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.
Methods: 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 1,946 patients who tested positive for SARS-CoV-2 were included in the initial analysis and assigned to derivation and validation cohorts (n=1,297 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 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).
Results: This score yielded an area under the curve (AUC) of 0.81 [95%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 event within 7 days: 0.83, [95%CI: 0.78-0.87]; for full follow-up: 0.82, [95%CI: 0.78-0.86]).
Conclusion: An easily applicable score to calculate the risk of COVID-19 progression to critical illness or death was established and validated. This article is protected by copyright. All rights reserved.

Keywords: COVID-19; Logistic Models; Machine Learning; Risk Factors.