Developing a predictive risk model for first‐line antiretroviral therapy failure in South Africa

Introduction A substantial number of patients with HIV in South Africa have failed first‐line antiretroviral therapy (ART). Although individual predictors of first‐line ART failure have been identified, few studies in resource‐limited settings have been large enough for predictive modelling. Understanding the absolute risk of first‐line failure is useful for patient monitoring and for effectively targeting limited resources for second‐line ART. We developed a predictive model to identify patients at the greatest risk of virologic failure on first‐line ART, and to estimate the proportion of patients needing second‐line ART over five years on treatment. Methods A cohort of patients aged ≥18 years from nine South African HIV clinics on first‐line ART for at least six months were included. Viral load measurements and baseline predictors were obtained from medical records. We used stepwise selection of predictors in accelerated failure‐time models to predict virologic failure on first‐line ART (two consecutive viral load levels >1000 copies/mL). Multiple imputations were used to assign missing baseline variables. The final model was selected using internal‐external cross‐validation maximizing model calibration at five years on ART, and model discrimination, measured using Harrell's C‐statistic. Model covariates were used to create a predictive score for risk group of ART failure. Results A total of 72,181 patients were included in the analysis, with an average of 21.5 months (IQR: 8.8–41.5) of follow‐up time on first‐line ART. The final predictive model had a Weibull distribution and the final predictors of virologic failure were men of all ages, young women, nevirapine use in first‐line regimen, low baseline CD4 count, high mean corpuscular volume, low haemoglobin, history of TB and missed visits during the first six months on ART. About 24.4% of patients in the highest quintile and 9.4% of patients in the lowest quintile of risk were predicted to experience treatment failure over five years on ART. Conclusions Age, sex, CD4 count and having any missed visits during the first six months on ART were the strongest predictors of ART failure. The predictive model identified patients at high risk of failure, and the predicted failure rates over five years closely reflected actual rates of failure.