Development and internal validation of a risk prediction model for HIV disease severity among people living with HIV and mental illness or substance use disorder.

PURPOSE

Mental illness (MI) and substance use disorders (SUD) are highly prevalent among people living with HIV (PLWH), and have been linked to poor HIV clinical outcomes. Innovative tools for early risk identification can facilitate timely interventions for PLWH and MI/SUD to improve their health outcomes, however, this is currently lacking in Texas, a state with the 4 largest population of PLWH in the United States. To address this gap, we developed a predictive model to estimate the risk of suboptimal HIV clinical outcomes among PLWH and MI/SUD in Texas.

METHODS

The Texas Medical Monitoring Project data obtained from June 2015-May 2020 were used to develop and internally validate the predictive model. Univariate descriptive and bivariate inferential statistics were performed to describe the characteristics of the study population and unadjusted associations with HIV clinical outcomes. Multivariable logistic regression was used to develop the prediction model. Internal validation was performed using the bootstrap method.

RESULTS

A total of 518 respondents aged 18 years and above, representing 27,255 adults living with HIV and mental illness or substance use disorders in Texas were included. Most participants were male (77.0%), less than 50 years of age (60.0%), and had mild diagnosed mental illness and substance use disorder (54.8%). The risk predictive model contained eight predictors, which together yielded an area under the receiver operating characteristic (ROC) curve of 0.727. Non-retention in care appeared to be the strongest risk predictor for having suboptimal HIV clinical outcome (adjusted odds ratio (aOR) = 3.27; 95% confidence interval (CI) = 1.45, 7.42).

CONCLUSIONS

The predictive model had good discrimination between persons at risk of poor HIV clinical outcomes and those not at risk.