A nomogram for predicting acute respiratory distress syndrome in COVID-19 patients

Objective: To predict the in-hospital incidence of acute respiratory distress syndrome (ARDS) in COVID-19 patients by developing a predictive nomogram. Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between 30 January 2020, and 22 February 2020 were enrolled in this study. Clinical characteristics and laboratory variables were analyzed and compared between patients with or without ARDS. Clinical characteristics and laboratory variables that were risk factors of ARDS were screened by the least absolute shrinkage and selection operator binary logistic regression. Based on risk factors, a prediction model was established by logistic regression and the final nomogram prognostic model was performed. The calibration curve was applied to evaluate the consistency between the nomogram and the ideal observation. Results: A total of 113 patients, including 99 non-ARDS patients and 14 ARDS patients were included in this study. Eight variables including hypertension, chronic obstructive pulmonary disease, cough, lactate dehydrogenase, creatine kinase, white blood count, body temperature, and heart rate were included in the model. The area under receiver operating characteristic curve, specificity, sensitivity, and accuracy of the full model were 0.969, 1.000, 0.857, and 0.875, respectively. The calibration curve also showed good agreement between the predicted and observed values in the model. Conclusions: The nomogram can be used to predict the in-hospital incidence of ARDS in COVID-19 patients.

Abstract licence: CC BY-NC-SA

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