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
Authors: Revell Andrew D , Wang Dechao , Perez-Elias Maria-Jesus , Wood Robin , Cogill Dolphina , Tempelman Hugo , Hamers Raph L , Reiss Peter , van Sighem Ard , Rehm Catherine A , Agan Brian , Alvarez-Uria Gerardo , Montaner Julio S G , Lane H Clifford , Larder Brendan A ,   the RDI study group, Reiss Peter , van Sighem Ard , Montaner Julio , Harrigan Richard , de Wit Tobias Rinke , Hamers Raph , Sigaloff Kim , Agan Brian , Marconi Vincent , Wegner Scott , Sugiura Wataru , Zazzi Maurizio , Kaiser Rolf , Schuelter Eugen , Streinu-Cercel Adrian , Alvarez-Uria Gerardo , Garcia Federico , de Oliveira Tulio , Gatell Jose , Lazzari Elisa , Gazzard Brian , Nelson Mark , Pozniak Anton , Mandalia Sundhiya , Smith Colette , Ruiz Lidia , Clotet Bonaventura , Staszewski Schlomo , Torti Carlo , Lane Cliff , Metcalf Julie , Rehm Catherine A , Perez-Elias Maria-Jesus , Vella Stefano , Dettorre Gabrielle , Carr Andrew , Norris Richard , Hesse Karl , Vlahakis Emanuel , Tempelman Hugo , Barth Roos , Wood Robin , Morrow Carl , Cogill Dolphina , Hoffmann Chris , Ene Luminita , Dragovic Gordana , Diaz Ricardo , Sucupira Cecilia , Sued Omar , Cesar Carina , Madero Juan Sierra , Balakrishnan Pachamuthu , Saravanan Shanmugam , Emery Sean , Cooper David , Torti Carlo , Baxter John , Monno Laura , Torti Carlo , Gatell Jose , Clotet Bonventura , Picchio Gaston , deBethune Marie-Pierre , Perez-Elias Maria-Jesus , Emery Sean , Khabo Paul , Ledwaba Lotty 
Published: March 2021

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 log10 copies HIV RNA/mL in cross-validation and 0.69 log10 copies HIV RNA/mL in independent testing. Models with missing variables achieved values of 0.65–0.75 log10 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.