2021 update to HIV-TRePS: a highly flexible and accurate system for the prediction of treatment response from incomplete baseline information in different healthcare settings

Objectives: With the goal of facilitating the use of HIV-TRePS to optimize therapy in settings with Limited healthcare resources, we aimed to develop computational models to predict treatment responses accurately in the absence of commonly used baseline data. Methods: Twelve sets of random forest models were trained using very Large, global datasets to predict either the probability of virological response (classifier models) or the absolute change in viral Load in response to a new regimen (absolute models) following virological failure. Two 'standard' models were developed with all baseline variables present and 10 others developed without HIV genotype, time on therapy, CD4 count or any combination of the above. Results: The standard classifier models achieved an AUC of 0.89 in cross-validation and independent testing. Models with missing variables achieved AUC values of 0.78-0.90. The standard absolute models made predictions that correlated significantly with observed changes in viral Load with a mean absolute error of 0.65 Log(10) copies HIV RNA/mL in cross-validation and 0.69 Log(10) copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65-0.75 Log(10)( )copies HIV RNA/mL. ALL models identified alternative regimens that were predicted to be effective for the vast majority of cases where the new regimen prescribed in the clinic failed. ALL models were significantly better predictors of treatment response than genotyping with rules-based interpretation. Conclusions: These Latest models that predict treatment responses accurately, even when a number of baseline variables are not available, are a major advance with greatly enhanced potential benefit, particularly in resource-Limited settings. The only obstacle to realizing this potential is the willingness of healthcare professions to use the system.

Automated summary

Novel computational models were developed to accurately predict treatment responses in HIV therapy, even in the absence of baseline data. These models showed strong performance in predicting virological response and absolute changes, offering potential benefits in resource-limited settings.