Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center
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ORIGINAL RESEARCH
Open Access
Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China Yiwu Zhou1,2†, Yanqi He3†, Huan Yang3, He Yu3, Ting Wang3, Zhu Chen4, Rong Yao1,2*
and Zongan Liang3*
Abstract Background: Novel 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). Methods: Data 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. Results: The 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. Conclusion: We 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. Keywords: Coronavirus disease 2019, Nomogram, ICU admission, Prediction, Early warning
* Correspondence: [email protected]; [email protected] † Yiwu Zhou and Yanqi He contributed equally to this work. 1 Department of Emergency Medicine, Emergency Medical Laboratory, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China 3 Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, No.37 Guoxue Roud, Chengdu 610041, Sichuan, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated oth
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