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.
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.



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.



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 %.



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.
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Sarah_rutstein@med.unc.edu.|Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Sarah_rutstein@med.unc.edu.
Sarah E
SE
Rutstein
Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|UNC Project, Lilongwe, Malawi.
Mina C
MC
Hosseinipour
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Morris
M
Weinberger
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Stephanie B
SB
Wheeler
Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Andrea K
AK
Biddle
BARC-SA and Lancet Laboratories, Johannesburg, South Africa.
Carole L
CL
Wallis
YRG Centre for AIDS Research and Education (YRG CARE), Voluntary Health Services, Taramani, Chennai, 600113, India.
Pachamuthu
P
Balakrishnan
Division of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA.
John W
JW
Mellors
Department of STD, AIDS, and Viral Hepatitis, Brazilian National STD and AIDS Program, Rio de Janeiro, Brazil.
Mariza
M
Morgado
YRG Centre for AIDS Research and Education, Chennai, India.
Shanmugam
S
Saravanan
Institute for Leprosy and Other Mycobacterial Diseases, Tajganj, Agra, India.
Srikanth
S
Tripathy
Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Saran
S
Vardhanabhuti
Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Joseph J
JJ
Eron
Division of Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.|Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
William C
WC
Miller
D000328:Adult; D000368:Aged; D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D019540:Area Under Curve; D024882:Drug Resistance, Viral / Q000502:physiology; D057915:Drug Substitution / Q000706:statistics & numerical data*; D005260:Female; D015658:HIV Infections / Q000188:drug therapy* / Q000821:virology; D015497:HIV-1 / Q000502:physiology; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012372:ROC Curve; D012306:Risk; D018570:Risk Assessment; D019562:Viral Load; D055815:Young Adult
D019380:Anti-HIV Agents
HIV; Prediction models; Resistance; Resource-limited setting; Viral load monitoring