Development and Internal Validation of a Clinical Risk Prediction Model for Opportunistic Infections Among People Living with HIV in Nigeria: A Retrospective Cohort Study

Background: Opportunistic infections (OIs) remain a primary cause of morbidity and mortality among people living with HIV (PLHIV). Combining routinely captured clinical variables into an individualized risk-prediction tool can optimize clinical risk stratification within resource-constrained care settings. Methods: We conducted a retrospective cohort study of 139 adults living with HIV enrolled between January 2022 and December 2023 at treatment centers in North Central Nigeria. Four candidate predictors (CD4 count category, antiretroviral therapy [ART] status, ART adherence, and WHO clinical stage) were prespecified. Multivariable binary logistic regression using Firth’s penalized maximum likelihood was fitted to mitigate quasi-complete separation and parameter distortion. Discrimination was assessed via the Area Under the Receiver Operating Characteristic Curve (AUROC), and calibration was evaluated using the Hosmer-Lemeshow test. Internal validation was performed using 1,000 bootstrap resamples. A simplified point-based score was subsequently constructed. Results: Among 139 participants (mean age 36.4±9.8 years; 58.3% female), 38 (27.3%) developed an OI within 12 months. CD4 count category was the primary independent predictor of OI development (Firth Adjusted Odds Ratio [