In Journal of cardiovascular development and disease
Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3-5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.
Ovcharenko Evgeny, Kutikhin Anton, Gruzdeva Olga, Kuzmina Anastasia, Slesareva Tamara, Brusina Elena, Kudasheva Svetlana, Bondarenko Tatiana, Kuzmenko Svetlana, Osyaev Nikolay, Ivannikova Natalia, Vavin Grigory, Moses Vadim, Danilov Viacheslav, Komossky Egor, Klyshnikov Kirill
2023-Jan-23
C-reactive protein, COVID-19, blood urea nitrogen, chronic kidney disease, coronary artery disease, lymphocyte count, machine learning, neural networks, neutrophil-to-lymphocyte ratio, prognostication
