Predictors of In-Hospital Mortality among Liver Disease Patients: A Logistic Regression Approach

Background: Liver disease constitutes a major cause of morbidity and mortality in Nigeria. In-hospital outcomes are influenced by demographic, clinical, and therapeutic factors. Accurate identification of mortality predictors is essential for risk assessment and optimized resource utilization. Objective: This study aimed to identify independent predictors of in-hospital mortality among patients admitted with liver disease at a Nigerian tertiary hospital, viz University of Ilorin Teaching Hospital (UITH) using logistic regression modeling. Methods: A retrospective hospital-based study analyzed 295 complete records of liver disease admissions at the University of Ilorin Teaching Hospital, Ilorin, from the study period until 2024. Data included age, sex, body mass index (BMI), length of stay, and outcome (discharged or deceased). Normality was assessed via the Anderson-Darling test; group differences were evaluated using the Wilcoxon rank-sum test and chi-square test. Binary logistic regression was employed to estimate crude odds ratios (ORs) for mortality predictors. Results: Numerical variables (age, BMI, length of stay) were non-normally distributed. Wilcoxon tests revealed significant differences in length of stay between survivors and non-survivors (p < 0.05). A chi-square test indicated borderline association between sex and outcome. Logistic regression identified length of stay as the only significant predictor (OR = 0.944, 95% CI: [0.920, 0.968], p < 0.001), with each additional day reducing mortality odds by approximately 5.6%. Age, sex, and BMI were non-significant. Conclusion: Prolonged hospital stay emerged as a strong protective factor against in-hospital mortality in liver disease patients, highlighting the critical role of timely admission and sustained management. These findings underscore the need for early intervention and improved ‘in-patient’ care to improve survival. Keywords: Liver disease, In-hospital mortality, Logistic regression, Length of stay.