Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China

Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine

Rong Yao¹  Yiwu Zhou¹  He Yu²  Ting Wang²  Zongan Liang²  Yanqi He²  Huan Yang²  Zhu Chen³ 

[1] Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan, China;Disaster Medical Center, Sichuan University, No.37 Guoxue Roud, 610041, Chengdu, Sichuan, China;Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No.37 Guoxue Roud, 610041, Chengdu, Sichuan, China;Public Health Clinical Center of Chengdu, 610000, Chengdu, China;

关键词: Coronavirus disease 2019;    Nomogram;    ICU admission;    Prediction;    Early warning;   

DOI  :  10.1186/s13049-020-00795-w

来源: Springer

【 摘 要 】

BackgroundNovel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU).MethodsData of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort.ResultsThe individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful.ConclusionWe established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

【 授权许可】

CC BY