Background: Acute respiratory distress HP syndrome MESHD (ARDS) was the most common complication of coronavirus disease MESHD-2019(COVID-19), leading to poor clinical outcomes. However, the model to predict the in-hospital incidence of ARDS in patients with COVID-19 is limited. Therefore, we aimed to develop a predictive nomogram for the in-hospital incidence of ARDS in COVID-19 patients.Methods: Patients with COVID-19 admitted to Changsha Public Health Centre between Jan 30, 2020, and Feb 22, 2020, were enrolled. Clinical characteristics and laboratory variables were analyzed in patients with ARDS. Risk factors for ARDS were selected by LASSO binary logistic regression. Nomogram was established based on risk factors and validated by the dataset.Results: A total of 113 patients, involving 99 in the non-ARDS group and 14 in the ARDS group were included in the study. 8 variables including hypertension MESHD hypertension HP, chronic obstructive pulmonary disease MESHD chronic obstructive pulmonary disease HP (COPD), cough MESHD cough HP, lactate dehydrogenase (LDH), creatine kinase (CK), white blood SERO count (WBC), body temperature, and heart rate were identified to be included in the model. The specificity, sensitivity SERO, and accuracy of the full model were 100%, 85.7%, and 87.5% respectively. The calibration curve also showed good agreement between the predicted and observed values in the model.Conclusions: The nomogram can predict the in-hospital incidence of ARDS in COVID-19 patients. It helps physicians to make an individualized treatment plan for each patient.