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Background: Chronic hepatitis C (HCV) infection presents global health challenges with significant morbidity and mortality implications. Successfully treating patients with cirrhosis may lead to mortality rates comparable to the general population. This study aims to utilize machine learning techniques to create predictive mortality models for individuals with chronic HCV infections. Methods: Data from chronic HCV patients at Sultan Qaboos University Hospital (2009-2017) underwent analysis. Data pre-processing handled missing values and scaled features using Python via Anaconda. Model training involved SelectKBest feature selection and algorithms such as logistic regression, random forest, gradient boosting, and SVM. The evaluation included diverse metrics, with 5-fold cross-validation, ensuring consistent performance assessment. Results: A cohort of 702 patients meeting the eligibility criteria, predominantly male, with a median age of 47, was analyzed across a follow-up period of 97.4 months. Survival probabilities at 12, 36, and 120 months were 90.0%, 84.0%, and 73.0%, respectively. Ten key features selected for mortality prediction included hemoglobin levels, alanine aminotransferase, comorbidities, HCV genotype, coinfections, follow-up duration, and treatment response. Machine learning models, including the logistic regression, random forest, gradient boosting, and support vector machine models, showed high discriminatory power, with logistic regression consistently achieving an AUC value of 0.929. Factors associated with increased mortality risk included cardiovascular diseases, coinfections, and failure to achieve a SVR, while lower ALT levels and specific HCV genotypes were linked to better survival outcomes. Conclusions: This study presents the use of machine learning models to predict mortality in chronic HCV patients, providing crucial insights for risk assessment and tailored treatments. Further validation and refinement of these models are essential to enhance their clinical utility, optimize patient care, and improve outcomes for individuals with chronic HCV infections.
RESUMO
BACKGROUND: Chronic hepatitis C (CHC) is a leading cause of cirrhosis and hepatocellular carcinoma (HCC) worldwide. This study aimed to determine rates and predictors of survival among Omani patients with CHC at a tertiary hospital in Muscat, Oman. METHODS: This ambidirectional cohort study included all CHC patients who presented to the Sultan Qaboos University Hospital between January 2009 and December 2017. Baseline demographic, clinical, laboratory, and radiological data were analyzed. Patients were followed-up until death or the endpoint of the study (April 2022) to determine survival and associations with other parameters. RESULTS: A total of 702 CHC patients were included, of which 398 (56.7%) were under 50 years of age and 477 (67.9%) were male. Overall, 180 patients (25.6%) died by the study endpoint. The mean duration of follow-up was 93.3 ± 48.0 months. The 5-year survival rate was estimated to be 80.5%, while the 10-year survival was 73%. Sustained virological response and the absence of diabetes mellitus, chronic kidney disease, HCC, or other malignancies were associated with significantly better overall survival. The 3- and 5-year survival rate of patients with hepatitis C virus (HCV)-related HCC was 46.5% and 27.6%, respectively, with a median survival of 29.5 months. Co-infection with hepatitis B was associated with poor survival among this subgroup; conversely, early HCV screening and the presence of a single HCC lesion were associated with better overall survival. CONCLUSIONS: National policies for early CHC screening and rapid treatment are needed to improve survival rates in this population.
