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BMC Infect Dis. 2016 Jun 13;16:280. doi: 10.1186/s12879-016-1611-2.

Predicting resistance as indicator for need to switch from first-line antiretroviral therapy among patients with elevated viral loads: development of a risk score algorithm.

Author information

1
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Sarah_rutstein@med.unc.edu.
2
Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Sarah_rutstein@med.unc.edu.
3
Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
4
UNC Project, Lilongwe, Malawi.
5
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
6
BARC-SA and Lancet Laboratories, Johannesburg, South Africa.
7
YRG Centre for AIDS Research and Education (YRG CARE), Voluntary Health Services, Taramani, Chennai, 600113, India.
8
Division of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.
9
Department of STD, AIDS, and Viral Hepatitis, Brazilian National STD and AIDS Program, Rio de Janeiro, Brazil.
10
YRG Centre for AIDS Research and Education, Chennai, India.
11
Institute for Leprosy and Other Mycobacterial Diseases, Tajganj, Agra, India.
12
Harvard T.H. Chan School of Public Health, Boston, MA, USA.
13
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Abstract

BACKGROUND:

In resource-limited settings, where resistance testing is unavailable, confirmatory testing for patients with high viral loads (VL) delays antiretroviral therapy (ART) switches for persons with resistance. We developed a risk score algorithm to predict need for ART change by identifying resistance among persons with persistently elevated VL.

METHODS:

We analyzed data from a Phase IV open-label trial. Using logistic regression, we identified demographic and clinical characteristics predictive of need for ART change among participants with VLs ≥1000 copies/ml, and assigned model-derived scores to predictors. We designed three models, including only variables accessible in resource-limited settings.

RESULTS:

Among 290 participants with at least one VL ≥1000 copies/ml, 51 % (148/290) resuppressed and did not have resistance testing; among those who did not resuppress and had resistance testing, 47 % (67/142) did not have resistance and 53 % (75/142) had resistance (ART change needed for 25.9 % (75/290)). Need for ART change was directly associated with higher baseline VL and higher VL at time of elevated measure, and inversely associated with treatment duration. Other predictors included body mass index and adherence. Area under receiver operating characteristic curves ranged from 0.794 to 0.817. At a risk score ≥9, sensitivity was 14.7-28.0 % and specificity was 96.7-98.6 %.

CONCLUSIONS:

Our model performed reasonably well and may be a tool to quickly transition persons in need of ART change to more effective regimens when resistance testing is unavailable. Use of this algorithm may result in public health benefits and health system savings through reduced transmissions of resistant virus and costs on laboratory investigations.

KEYWORDS:

HIV; Prediction models; Resistance; Resource-limited setting; Viral load monitoring

PMID:
27296625
PMCID:
PMC4906700
DOI:
10.1186/s12879-016-1611-2
[Indexed for MEDLINE]
Free PMC Article

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