Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

• Published in: Egyptian journal of radiology and nuclear medicine 2023-12, Vol.54 (1), p.73-9
• Main Authors: Zakariaee, Seyed Salman, Abdi, Aza Ismail, Naderi, Negar, Babashahi, Mashallah
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Chest CT severity score; Coronaviruses; COVID-19; CT-SS; Datasets; Hospital patients; Iran; Machine learning; Medical research; Medicine, Experimental; Mortality; Mortality prediction; Patient outcomes; Prognosis
• ISSN: 0378-603X; 2090-4762
• DOI: 10.1186/s43055-023-01022-z

Background The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Methods This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Results After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (± SD) age of participants was 57.22(± 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusions The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.