1. An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
- Author
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Stephen Wai Hang Kwok, Guanjin Wang, Ferdous Sohel, Kianoush B. Kashani, Ye Zhu, Zhen Wang, Eduardo Antpack, Kanika Khandelwal, Sandeep R. Pagali, Sanjeev Nanda, Ahmed D. Abdalrhim, Umesh M. Sharma, Sumit Bhagra, Sagar Dugani, Paul Y. Takahashi, Mohammad H. Murad, and Mohammed Yousufuddin
- Subjects
COVID-19 ,Mortality ,Organ failure ,Prediction models ,Machine learning algorithms ,Diseases of the respiratory system ,RC705-779 - Abstract
Abstract Background We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. Methods This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. Results Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849–0.856, calibration slopes 0.911–1.173, and Hosmer–Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P
- Published
- 2023
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