Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study

• Published in: Biomedicines Vol. 12; no. 7; p. 1511
• Main Authors: Gracida-Osorno, Carlos, Molina-Salinas, Gloria María, Góngora-Hernández, Roxana, Brito-Loeza, Carlos, Uc-Cachón, Andrés Humberto, Paniagua-Sierra, José Ramón
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08-07-2024
• Subjects: acute kidney injury; Artificial intelligence; boosting; chronic kidney disease progression; Coronaviruses; COVID-19; Creatinine; Epidemiology; Feasibility studies; Hospitalization; Hospitals; Informed consent; Kidney diseases; Laboratories; Learning algorithms; logistic regression; Machine learning; Missing data; Patients; Pneumonia; Severe acute respiratory syndrome coronavirus 2; Variables; COVID-19; chronic kidney disease progression; prediction model; support vector machine; boosting; acute kidney injury; random forest; logistic regression; machine learning
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines12071511

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.