Cargando…
Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa
INTRODUCTION: A substantial number of patients with HIV in South Africa have failed first-line antiretroviral therapy (ART). Although individual predictors of first-line ART failure have been identified, few studies in resource-limited settings have been large enough for predictive modelling. Unders...
Autores principales: | , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International AIDS Society
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039239/ https://www.ncbi.nlm.nih.gov/pubmed/27677395 http://dx.doi.org/10.7448/IAS.19.1.20987 |
_version_ | 1782456015777693696 |
---|---|
author | Rohr, Julia K Ive, Prudence Horsburgh, C Robert Berhanu, Rebecca Shearer, Kate Maskew, Mhairi Long, Lawrence Sanne, Ian Bassett, Jean Ebrahim, Osman Fox, Matthew P |
author_facet | Rohr, Julia K Ive, Prudence Horsburgh, C Robert Berhanu, Rebecca Shearer, Kate Maskew, Mhairi Long, Lawrence Sanne, Ian Bassett, Jean Ebrahim, Osman Fox, Matthew P |
author_sort | Rohr, Julia K |
collection | PubMed |
description | INTRODUCTION: A substantial number of patients with HIV in South Africa have failed first-line antiretroviral therapy (ART). Although individual predictors of first-line ART failure have been identified, few studies in resource-limited settings have been large enough for predictive modelling. Understanding the absolute risk of first-line failure is useful for patient monitoring and for effectively targeting limited resources for second-line ART. We developed a predictive model to identify patients at the greatest risk of virologic failure on first-line ART, and to estimate the proportion of patients needing second-line ART over five years on treatment. METHODS: A cohort of patients aged ≥18 years from nine South African HIV clinics on first-line ART for at least six months were included. Viral load measurements and baseline predictors were obtained from medical records. We used stepwise selection of predictors in accelerated failure-time models to predict virologic failure on first-line ART (two consecutive viral load levels >1000 copies/mL). Multiple imputations were used to assign missing baseline variables. The final model was selected using internal-external cross-validation maximizing model calibration at five years on ART, and model discrimination, measured using Harrell's C-statistic. Model covariates were used to create a predictive score for risk group of ART failure. RESULTS: A total of 72,181 patients were included in the analysis, with an average of 21.5 months (IQR: 8.8–41.5) of follow-up time on first-line ART. The final predictive model had a Weibull distribution and the final predictors of virologic failure were men of all ages, young women, nevirapine use in first-line regimen, low baseline CD4 count, high mean corpuscular volume, low haemoglobin, history of TB and missed visits during the first six months on ART. About 24.4% of patients in the highest quintile and 9.4% of patients in the lowest quintile of risk were predicted to experience treatment failure over five years on ART. CONCLUSIONS: Age, sex, CD4 count and having any missed visits during the first six months on ART were the strongest predictors of ART failure. The predictive model identified patients at high risk of failure, and the predicted failure rates over five years closely reflected actual rates of failure. |
format | Online Article Text |
id | pubmed-5039239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | International AIDS Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-50392392016-09-28 Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa Rohr, Julia K Ive, Prudence Horsburgh, C Robert Berhanu, Rebecca Shearer, Kate Maskew, Mhairi Long, Lawrence Sanne, Ian Bassett, Jean Ebrahim, Osman Fox, Matthew P J Int AIDS Soc Research Article INTRODUCTION: A substantial number of patients with HIV in South Africa have failed first-line antiretroviral therapy (ART). Although individual predictors of first-line ART failure have been identified, few studies in resource-limited settings have been large enough for predictive modelling. Understanding the absolute risk of first-line failure is useful for patient monitoring and for effectively targeting limited resources for second-line ART. We developed a predictive model to identify patients at the greatest risk of virologic failure on first-line ART, and to estimate the proportion of patients needing second-line ART over five years on treatment. METHODS: A cohort of patients aged ≥18 years from nine South African HIV clinics on first-line ART for at least six months were included. Viral load measurements and baseline predictors were obtained from medical records. We used stepwise selection of predictors in accelerated failure-time models to predict virologic failure on first-line ART (two consecutive viral load levels >1000 copies/mL). Multiple imputations were used to assign missing baseline variables. The final model was selected using internal-external cross-validation maximizing model calibration at five years on ART, and model discrimination, measured using Harrell's C-statistic. Model covariates were used to create a predictive score for risk group of ART failure. RESULTS: A total of 72,181 patients were included in the analysis, with an average of 21.5 months (IQR: 8.8–41.5) of follow-up time on first-line ART. The final predictive model had a Weibull distribution and the final predictors of virologic failure were men of all ages, young women, nevirapine use in first-line regimen, low baseline CD4 count, high mean corpuscular volume, low haemoglobin, history of TB and missed visits during the first six months on ART. About 24.4% of patients in the highest quintile and 9.4% of patients in the lowest quintile of risk were predicted to experience treatment failure over five years on ART. CONCLUSIONS: Age, sex, CD4 count and having any missed visits during the first six months on ART were the strongest predictors of ART failure. The predictive model identified patients at high risk of failure, and the predicted failure rates over five years closely reflected actual rates of failure. International AIDS Society 2016-09-26 /pmc/articles/PMC5039239/ /pubmed/27677395 http://dx.doi.org/10.7448/IAS.19.1.20987 Text en © 2016 Rohr JK et al; licensee International AIDS Society http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution 3.0 Unported (CC BY 3.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Rohr, Julia K Ive, Prudence Horsburgh, C Robert Berhanu, Rebecca Shearer, Kate Maskew, Mhairi Long, Lawrence Sanne, Ian Bassett, Jean Ebrahim, Osman Fox, Matthew P Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title | Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title_full | Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title_fullStr | Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title_full_unstemmed | Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title_short | Developing a predictive risk model for first-line antiretroviral therapy failure in South Africa |
title_sort | developing a predictive risk model for first-line antiretroviral therapy failure in south africa |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039239/ https://www.ncbi.nlm.nih.gov/pubmed/27677395 http://dx.doi.org/10.7448/IAS.19.1.20987 |
work_keys_str_mv | AT rohrjuliak developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT iveprudence developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT horsburghcrobert developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT berhanurebecca developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT shearerkate developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT maskewmhairi developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT longlawrence developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT sanneian developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT bassettjean developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT ebrahimosman developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica AT foxmatthewp developingapredictiveriskmodelforfirstlineantiretroviraltherapyfailureinsouthafrica |