Background: Early identification of patients with COVID-19 who may develop critical illness is of great importance.
Methods: In this study a retrospective cohort of 264 COVID-19 cases admitted at Macarena University was used for development and internal validation of a risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. Backward stepwise logistic regression was used to derive the model, including clinical and laboratory variables predictive of critical illness. Internal validation of the final model used bootstrapped samples and the model scoring derived from the coefficients. External validation was performed in a cohort of 154 cases admitted at Valme and Virgen del Rocio University Hospital.
Results: A total of 62 (23.5%) patients developed a critical illness during their hospitalization stay, 21 (8.0%) patients needed invasive ventilation, 34 (12.9%) were admitted at the ICU and the overall mortality was of 14.8% (39 cases). 5 variables were included in the final model: age >59.5 years (OR: 3.11;95%CI 1.39-6.97), abnormal CRP results (OR: 5.76;95%CI 2.32-14.30), abnormal lymphocytes count (OR: 3.252;95%CI 1.56-6.77), abnormal CK results (OR: 3.38;95%CI 1.59-7.20) and abnormal creatinine (OR: 3.30;95%CI 1.42-7.68). The AUC of this model was 0.850 with sensitivity of 65% and specificity of 87% and the IDI and NRI were 0.1744 and 0.2785, respectively. The validation indicated a good discrimination for the external population.
Conclusions: Biomarkers add prognostic information in COVID-19 patients. Our risk-score provides an easy to use tool to identify patients who are likely to develop critical illness during their hospital stay.
Keywords: COVID-19; biomarkers; critical illness; logistic regression; risk factors.