Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019. Issue 8 (26th February 2021) - Liu, Li
Abstract : Abstract: Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression. This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model. In the training cohort, the mean age was 63.4 ± 1.5 years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR] = 4.40, P = .026), elevated lactate dehydrogenase levels (LDH, OR = 4.24, P = .030), and high-sensitivity C-reactive protein (hsCRP, OR = 1.01, P = .025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC] = 0.88, P < .001) and the best cut-off value was 0.526 with the sensitivity and specificity of 75.0% and 90.7%, respectively. Then, the model was internally validated by leave-one-out cross-validation with value of AUC 0.85 ( P < .001) and externally validated in another validation cohort (26 recovered patients and 14 progressed patients) with AUC 0.84 ( P < .001). We identified 3 clinical indicators of risk of progression and developed a severe COVID-19 prognostic prediction model, allowing early identification and intervention of high-risk patients being critically illness. Abstract : Supplemental Digital Content is available in the text