Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

BackgroundSelecting the optimal combination of HIV drugs for an individual in resource-limited settings is challenging because of the limited availability of drugs and genotyping.ObjectiveThe evaluation as a potential treatment support tool of computational models that predict response to therapy without a genotype, using cases from the Phidisa cohort in South Africa.MethodsCases from Phidisa of treatment change following failure were identified that had the following data available: baseline CD4 count and viral load, details of failing and previous antiretroviral drugs, drugs in new regimen and time to follow-up. The HIV Resistance Response Database Initiative’s (RDI’s) models used these data to predict the probability of a viral load < 50 copies/mL at follow-up. The models were also used to identify effective alternative combinations of three locally available drugs.ResultsThe models achieved accuracy (area under the receiver–operator characteristic curve) of 0.72 when predicting response to therapy, which is less accurate than for an independent global test set (0.80) but at least comparable to that of genotyping with rules-based interpretation. The models were able to identify alternative locally available three-drug regimens that were predicted to be effective in 69% of all cases and 62% of those whose new treatment failed in the clinic.ConclusionThe predictive accuracy of the models for these South African patients together with the results of previous studies suggest that the RDI’s models have the potential to optimise treatment selection and reduce virological failure in different patient populations, without the use of a genotype.

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“…Indeed, a recent study of cases treated in India revealed that substantial cost savings may be possible through use of the system. 13 …”

Section: Discussionmentioning

confidence: 99%

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Revell¹,

Khabo²,

Ledwaba

et al. 2016

South. Afr. j. HIV med.

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“…The models described here are freely available online as part of the HIV Treatment Response Prediction System (HIV‐TRePS) which also enables physicians to model therapy costs. In a recent retrospective study using data from an Indian cohort, the system identified alternative, locally available regimens with a higher probability of response and lower annual costs than those prescribed in the clinic . The debate about the optimum allocation of resources to drugs, diagnostics and systems in such settings is an important one that is set to continue .…”

Section: Discussionmentioning

confidence: 99%

“…the clinic [10]. The debate about the optimum allocation of resources to drugs, diagnostics and systems in such settings is an important one that is set to continue [11].…”

Section: Discussionmentioning

confidence: 99%

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

Revell¹,

Boyd

Wang³

et al. 2014

HIV Medicine

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ObjectivesWe compared the use of computational models developed with and without HIV genotype vs. genotyping itself to predict effective regimens for patients experiencing first-line virological failure. MethodsTwo sets of models predicted virological response for 99 three-drug regimens for patients on a failing regimen of two nucleoside/nucleotide reverse transcriptase inhibitors and one nonnucleoside reverse transcriptase inhibitor in the Second-Line study. One set used viral load, CD4 count, genotype, plus treatment history and time to follow-up to make its predictions; the second set did not include genotype. Genotypic sensitivity scores were derived and the ranking of the alternative regimens compared with those of the models. The accuracy of the models and that of genotyping as predictors of the virological responses to second-line regimens were compared. ResultsThe rankings of alternative regimens by the two sets of models were significantly correlated in 60−69% of cases, and the rankings by the models that use a genotype and genotyping itself were significantly correlated in 60% of cases. The two sets of models identified alternative regimens that were predicted to be effective in 97% and 100% of cases, respectively. The area under the receiver-operating curve was 0.72 and 0.74 for the two sets of models, respectively, and significantly lower at 0.55 for genotyping. ConclusionsThe two sets of models performed comparably well and significantly outperformed genotyping as predictors of response. The models identified alternative regimens predicted to be effective in almost all cases. It is encouraging that models that do not require a genotype were able to predict responses to common second-line therapies in settings where genotyping is unavailable.

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“…Leveraging the knowledge of a combination of all or some of these factors through rapid risk calculation to predict early viral outcomes in individual patients before initiation of ART would enhance clinical decision making and prevent adverse outcomes of treatment failure and the costs associated with switching to second line ART [20]. …”

Section: Introductionmentioning

confidence: 99%

“…Attainment of early virological suppression depends on a number of factors including choice of initial ART regimen especially in ART naïve patients, ART adherence, comorbidities, and inter-individual variability in drug pharmacokinetics, demographic and genetic factors and drug resistance, baseline viral load and CD4 count [ 12 – 19 ]. Leveraging the knowledge of a combination of all or some of these factors through rapid risk calculation to predict early viral outcomes in individual patients before initiation of ART would enhance clinical decision making and prevent adverse outcomes of treatment failure and the costs associated with switching to second line ART [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients

Bisaso

Karungi

Kiragga

et al. 2018

BMC Med Inform Decis Mak

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BackgroundTreatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among HIV positive individuals. Effective highly active antiretroviral therapy (HAART) should lead to undetectable viral load within 6 months of initiation of therapy. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using easily measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count). The predictive performance and generalizability of the approaches are compared.MethodsThe multitask temporal logistic regression (MTLR), patient specific survival prediction (PSSP) and simple logistic regression (SLR) models were developed and validated using the IDI research cohort data and predictive performance tested on an external dataset from the EFV cohort. The model calibration and discrimination plots, discriminatory measures (AUROC, F1) and overall predictive performance (brier score) were assessed.ResultsThe MTLR model outperformed the PSSP and SLR models in terms of goodness of fit (RMSE = 0.053, 0.1, and 0.14 respectively), discrimination (AUROC = 0.92, 0.75 and 0.53 respectively) and general predictive performance (Brier score= 0.08, 0.19, 0.11 respectively). The predictive importance of variables varied with time after initiation of ART. The final MTLR model accurately (accuracy = 92.9%) predicted outcomes in the external (EFV cohort) dataset with satisfactory discrimination (0.878) and a low (6.9%) false positive rate.ConclusionMultitask Logistic regression based models are capable of accurately predicting early virological suppression using readily available baseline demographic and clinical variables and could be used to derive a risk score for use in resource limited settings.

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Potential Impact of a Free Online HIV Treatment Response Prediction System for Reducing Virological Failures and Drug Costs after Antiretroviral Therapy Failure in a Resource-Limited Setting

Cited by 3 publications

References 14 publications

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa

A comparison of computational models with and without genotyping for prediction of response to second‐line HIV therapy

A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients

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