ArticleThe Journal of antimicrobial chemotherapy2013
Computational models can predict response to HIV therapy without a genotype and may reduce treatment failure in different resource-limited settings.
Article in The Journal of antimicrobial chemotherapy, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02454569 (Vicente Ferrer HIV Cohort Study), which is not on this map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Vicente Ferrer HIV Cohort Study
Who cites it
11 citing papers in PubMed, 34 citations in OpenAlex.
- Machine learning quantifies immuno-virological interactions: a TRIPOD+AI compliant prediction model for HIV-1 salvage therapy outcomes.Frontiers in immunology · 2026Article
- Development of machine learning algorithms to predict viral load suppression among HIV patients in Conakry (Guinea).Frontiers in artificial intelligence · 2025Article
- Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.Pathogens (Basel, Switzerland) · 2024Article
- Emerging role of artificial intelligence in global health care.Health policy and technology · 2022Article
- 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.The Journal of antimicrobial chemotherapy · 2021Article
- A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patients.BMC medical informatics and decision making · 2018Article
- 2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.The Journal of antimicrobial chemotherapy · 2018Article
- An update to the HIV-TRePS system: the development and evaluation of new global and local computational models to predict HIV treatment outcomes, with or without a genotype.The Journal of antimicrobial chemotherapy · 2016Article
- Computational models as predictors of HIV treatment outcomes for the Phidisa cohort in South Africa.Southern African journal of HIV medicine · 2016Article
- An update to the HIV-TRePS system: the development of new computational models that do not require a genotype to predict HIV treatment outcomes.The Journal of antimicrobial chemotherapy · 2014Article
- 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.BioMed research international · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 11 institutions in 7 countries.
Funding
Abstract
objectivesGenotypic HIV drug-resistance testing is typically 60%-65% predictive of response to combination antiretroviral therapy (ART) and is valuable for guiding treatment changes. Genotyping is unavailable in many resource-limited settings (RLSs). We aimed to develop models that can predict response to ART without a genotype and evaluated their potential as a treatment support tool in RLSs.
methodsRandom forest models were trained to predict the probability of response to ART (≤400 copies HIV RNA/mL) using the following data from 14 891 treatment change episodes (TCEs) after virological failure, from well-resourced countries: viral load and CD4 count prior to treatment change, treatment history, drugs in the new regimen, time to follow-up and follow-up viral load. Models were assessed by cross-validation during development, with an independent set of 800 cases from well-resourced countries, plus 231 cases from Southern Africa, 206 from India and 375 from Romania. The area under the receiver operating characteristic curve (AUC) was the main outcome measure.
resultsThe models achieved an AUC of 0.74-0.81 during cross-validation and 0.76-0.77 with the 800 test TCEs. They achieved AUCs of 0.58-0.65 (Southern Africa), 0.63 (India) and 0.70 (Romania). Models were more accurate for data from the well-resourced countries than for cases from Southern Africa and India (P < 0.001), but not Romania. The models identified alternative, available drug regimens predicted to result in virological response for 94% of virological failures in Southern Africa, 99% of those in India and 93% of those in Romania.
conclusionsWe developed computational models that predict virological response to ART without a genotype with comparable accuracy to genotyping with rule-based interpretation. These models have the potential to help optimize antiretroviral therapy for patients in RLSs where genotyping is not generally available.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.