Abstract
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.
Highlights
Treatment with effective antiretroviral therapy (ART) lowers morbidity and mortality among Human Imunodeficiency Virus (HIV) positive individuals
The purpose of this study was to assess the performance of 3 logistic regression based machine learning methods at predicting early virological failure in HIV patients initiating ART
The Infectious Diseases Institute (IDI) cohort data was used for training the prediction model and testing its generalizability while data from the efavirenz (EFV) cohort was used to test the model’s ability to predict outside the studied population
Summary
Treatment 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. In this paper three logistic regression based machine learning approaches are developed to predict early virological outcomes using measurable baseline demographic and clinical variables (age, body weight, sex, TB disease status, ART regimen, viral load, CD4 count). Effective antiretroviral therapy (ART) should lead to undetectable viral load within 6 months of initiation of therapy [3]. Achievement of early viral suppression (suppression by 24 weeks) predicts long term treatment success as measured by virological suppression, CD4+ cell count increase and reduction in mortality [4, 5]. Failure to achieve and maintain viral suppression may lead to development of resistance and increase the risk of viral transmission [6, 10, 11]
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