Multi-Factor Model for Renal Risk Prediction in HIV Patients Initiating Dolutegravir: A Thai Secondary Hospital Cohort Study

Abstract

Effective renal risk stratification for patients initiating dolutegravir (DTG) is crucial, as standard estimated glomerular filtration rate (eGFR) monitoring may be insufficient to prevent long-term kidney disease. This study aimed to develop and validate a multi-factor risk model to identify patients susceptible to a significant eGFR reduction (≥25%), thereby enabling targeted monitoring and preventive care. A retrospective cohort study at a Thai secondary care hospital analyzed 1,100 people with HIV who initiating DTG-based regimens between 2021 and 2023. The cohort was randomly divided into a training (n = 880) and a validation (n = 220) dataset. A multi-factor logistic regression model was developed, and its performance was compared against a model using only baseline eGFR. A significant eGFR reduction occurred in 17.82% of the cohort. Five independent risk factors were identified: age >40 years, BMI >23 kg/m², CD4 count <400 cells/µL, baseline eGFR <90 mL/min/1.73 m², and elevated alanine aminotransferase (ALT >40 U/L). The comprehensive 5-factor model demonstrated significantly better predictive performance (AUC 0.780; 95% CI 0.716 – 0.844) than the model using eGFR alone (AUC 0.651; 95% CI 0.571 – 0.730). Identifying at-risk patients is a critical first step in preventing long-term renal disease. This study provides a practical, novel renal risk prediction tool for DTG users, highlighting its clinical utility for proactive care. Future work should focus on prospective validation and integration into clinical workflows.