Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score

Background We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. Methods All consecutive patients with COVID-19 admitted to Fuyang Second People's Hospital or the Fifth Medical Center of Chinese PLA General Hospital between 20 January and 22 February 2020 were enrolled and their clinical data were retrospectively collected. Multivariate Cox regression was used to identify risk factors associated with progression, which were then were incorporated into a nomogram to establish a novel prediction scoring model. ROC was used to assess the performance of the model. Results Overall, 208 patients were divided into a stable group (n = 168, 80.8%) and a progressive group (n = 40,19.2%) based on whether their conditions worsened during hospitalization. Univariate and multivariate analyses showed that comorbidity, older age, lower lymphocyte count, and higher lactate dehydrogenase at presentation were independent high-risk factors for COVID-19 progression. Incorporating these 4 factors, the nomogram achieved good concordance indexes of .86 (95% confidence interval [CI], .81-.91) and well-fitted calibration curves. A novel scoring model, named as CALL, was established; its area under the ROC was .91 (95% CI, .86-.94). Using a cutoff of 6 points, the positive and negative predictive values were 50.7% (38.9-62.4%) and 98.5% (94.7-99.8%), respectively. Conclusions Using the CALL score model, clinicians can improve the therapeutic effect and reduce the mortality of COVID-19 with more accurate and efficient use of medical resources. This multicenter retrospective study showed underlying comorbidity, older age, higher lactate dehydrogenase, and lower lymphocyte count were independent high-risk factors associated with COVID-19 progression; a novel scoring model (CALL score) can predict progression with optimal sensitivity and specificity.