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@article{196850,
        author = {Nnabude Chinelo Ijeoma},
        title = {Multivariable Survival Prediction in Hepatitis: Model Development and Internal Validation from a Retrospective Cohort},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4984-4995},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196850},
        abstract = {Prognosis in hepatitis is heterogeneous, and risk prediction tools may improve early risk stratification especially in resource-limited settings. This study aimed to develop and internally validate a multivariable prediction model to identify clinical and biochemical predictors of survival in hepatitis patients. A retrospective cohort of 155 patients (32 deaths, 123 survivors) from the Kaggle Hepatitis Survival Dataset was analyzed using multivariable logistic regression. Predictors included age, bilirubin, albumin, and prothrombin time. Albumin emerged as the strongest independent predictor of survival (Adjusted OR = 0.210, 95% CI: 0.061–0.724, p = 0.016). The multivariable logistic regression model demonstrated excellent discrimination (AUC = 0.895), good calibration (Hosmer-Lemeshow p = 0.354), and high accuracy (Brier Score = 0.087). Albumin is an accurate and sensitive predictor of mortality in hepatitis patients. External validation in larger prospective African cohorts should be carried out before clinical implementation.},
        keywords = {Risk Stratification, Resource-Limited Settings, Internal Validation, Multivariable Logistic Regression, Hepatitis Prognosis},
        month = {April},
        }