Predicting Severe/Critical Outcomes in Patients With SARS-CoV2 Pneumonia: Development of the prediCtion seveRe/crItical ouTcome in COVID-19 (CRITIC) Model - Salaffi, Fausto; Carotti, Marina; Di Carlo, Marco; Ceccarelli, Luca; Galli, Massimo; Sarzi-Puttini, Piercarlo; Giovagnoni, Andrea (Frontiers in Medicine, 2021)
Objective. To create a prediction model of the risk of severe/critical disease in patients with Coronavirus disease (COVID-19). Methods. Clinical, laboratory, and lung computed tomography (CT) severity score were collected from patients admitted for COVID-19 pneumonia and considered as independent variables for the risk of severe/critical disease in a logistic regression analysis. The discriminative properties of the variables were analyzed through the area under the receiver operating characteristic curve analysis and included in a prediction model based on Fagan’s nomogram to calculate the post-test probability of severe/critical disease. All analyses were conducted using Medcalc (version 19.0, MedCalc Software, Ostend, Belgium). Results. 171 patients with COVID-19 pneumonia, including 37 severe/critical cases (21.6%) and 134 mild/moderate cases were evaluated. Among all the analyzed variables, Charlson Comorbidity Index (CCI) was that with the highest relative importance (p = 0.0001), followed by CT severity score (p = 0.0002), and age (p = 0.0009). The optimal cut-off points for the predictive variables resulted: 3 for CCI (sensitivity 83.8%, specificity 69.6%, positive likelihood ratio [+LR] 2.76), 69.9 for age (sensitivity 94.6%, specificity 68.1, +LR 2.97), and 53 for CT severity score (sensitivity 64.9%, specificity 84.4%, +LR 4.17). Conclusion. The nomogram including CCI, age, and CT severity score, may be used to stratify patients with COVID-19 pneumonia.