Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring

Journal of Acquired Immune Deficiency Syndromes

Volumn 69, Issue 1, 2015, Pages 109-118

  Petersen, Maya L ^a   LeDell, Erin ^a   Schwab, Joshua ^a   Sarovar, Varada ^a   Gross, Robert ^b   Reynolds, Nancy ^c   Haberer, Jessica E ^d   Goggin, Kathy ^e   Golin, Carol ^f   Arnsten, Julia ^g   Rosen, Marc I ^h   Remien, Robert H ⁱ   Etoori, David ^a   Wilson, Ira B ^j   Simoni, Jane M ^k   Erlen, Judith A ^l   Van Der Laan, Mark J ^a   Liu, Honghu ^m   Bangsberg, David R ⁿ  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c YALE UNIVERSITY SCHOOL OF NURSING   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

^e UNIVERSITY OF MISSOURI KANSAS CITY   (United States)

^f University of North Carolina at Chapel Hill   (United States)

^g MONTEFIORE MEDICAL CENTER   (United States)

^h YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

ⁱ NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^j BROWN UNIVERSITY   (United States)

^k UNIVERSITY OF WASHINGTON   (United States)

^l UNIVERSITY OF PITTSBURGH   (United States)

^m UCLA SCHOOL OF DENTISTRY   (United States)

ⁿ MASSACHUSETTS GENERAL HOSPITAL   (United States)

more..

Author keywords

adherence; antiretroviral therapy; HIV; HIV RNA monitoring; Medication Event Monitoring System; Super Learner; virological failure

Indexed keywords

ANTI HUMAN IMMUNODEFICIENCY VIRUS AGENT; VIRUS RNA;

ADULT; ARTICLE; CD4 LYMPHOCYTE COUNT; COHORT ANALYSIS; COST CONTROL; DRUG MONITORING; DRUG TREATMENT FAILURE; ELECTRONIC MEDICAL RECORD; FOLLOW UP; HIGHLY ACTIVE ANTIRETROVIRAL THERAPY; HUMAN; HUMAN IMMUNODEFICIENCY VIRUS; HUMAN IMMUNODEFICIENCY VIRUS INFECTED PATIENT; HUMAN IMMUNODEFICIENCY VIRUS INFECTION; LONGITUDINAL STUDY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MEDICATION COMPLIANCE; PATIENT COMPLIANCE; PREDICTIVE VALUE; PRIORITY JOURNAL; PROSPECTIVE STUDY; VIRAL LOAD TESTING; VIRUS LOAD; ARTIFICIAL INTELLIGENCE; BLOOD; CLINICAL TRIAL; DECISION SUPPORT SYSTEM; FEMALE; HIV INFECTIONS; MALE; MIDDLE AGED; MULTICENTER STUDY; PROCEDURES; STATISTICS AND NUMERICAL DATA; TREATMENT FAILURE;

ADULT; ANTI-HIV AGENTS; ARTIFICIAL INTELLIGENCE; COHORT STUDIES; DECISION SUPPORT TECHNIQUES; DRUG MONITORING; FEMALE; HIV INFECTIONS; HUMANS; LONGITUDINAL STUDIES; MALE; MEDICATION ADHERENCE; MIDDLE AGED; PROSPECTIVE STUDIES; RNA, VIRAL; TREATMENT FAILURE;

EID: 84928390562     PISSN: 15254135     EISSN: 10779450     Source Type: Journal    
DOI: 10.1097/QAI.0000000000000548     Document Type: Article

References (46)

1. Hatano H, Hunt P, Weidler J, et al. Rate of viral evolution and risk of losing future drug options in heavily pretreated, HIV-infected patients who continue to receive a stable, partially suppressive treatment regimen. Clin Infect Dis. 2006;43:1329-1336.
2. Petersen ML, van der Laan MJ, Napravnik S, et al. Long-term consequences of the delay between virologic failure of highly active antiretroviral therapy and regimen modification. AIDS. 2008;22: 2097-2106.
3. Barth RE, Wensing AM, Tempelman HA, et al. Rapid accumulation of nonnucleoside reverse transcriptase inhibitor-associated resistance: evidence of transmitted resistance in rural South Africa. AIDS. 2008;22: 2210-2212.
4. Abouyannis M, Menten J, Kiragga A, et al. Development and validation of systems for rational use of viral load testing in adults receiving firstline ART in sub-Saharan Africa. AIDS. 2011;25:1627-1635.
5. Meya D, Spacek LA, Tibenderana H, et al. Development and evaluation of a clinical algorithm to monitor patients on antiretrovirals in resourcelimited settings using adherence, clinical and CD4 cell count criteria. J Int AIDS Soc. 2009;12:3.
6. Colebunders R, Moses KR, Laurence J, et al. A new model to monitor the virological efficacy of antiretroviral treatment in resource-poor countries. Lancet Infect Dis. 2006;6:53-59.
7. Bisson GP, Gross R, Bellamy S, et al. Pharmacy refill adherence compared with CD4 count changes for monitoring HIV-infected adults on antiretroviral therapy. PLoS Med. 2008;5:e109.
8. Genberg BL, Wilson IB, Bangsberg DR, et al. Patterns of antiretroviral therapy adherence and impact on HIV RNA among patients in North America. AIDS. 2012;26:1415-1423.
9. de Boer IM, Prins JM, Sprangers MA, et al. Using different calculations of pharmacy refill adherence to predict virological failure among HIVinfected patients. J Acquir Immune Defic Syndr. 2010;55:635-640.
10. McMahon JH, Manoharan A, Wanke CA, et al. Pharmacy and self-report adherence measures to predict virological outcomes for patients on free antiretroviral therapy in Tamil Nadu, India. AIDS Behav. 2013;17: 2253-2259.
11. Goldman JD, Cantrell RA, Mulenga LB, et al. Simple adherence assessments to predict virologic failure among HIV-infected adults with discordant immunologic and clinical responses to antiretroviral therapy. AIDS Res Hum Retroviruses. 2008;24:1031-1035.
12. Liu H, Golin CE, Miller LG, et al. A comparison study of multiple measures of adherence to HIV protease inhibitors. Ann Intern Med. 2001; 134:968-977.
13. Haberer JE, Robbins GK, Ybarra M, et al. Real-time electronic adherence monitoring is feasible, comparable to unannounced pill counts, and acceptable. AIDS Behav. 2012;16:375-382.
14. Oyugi JH, Byakika-Tusiime J, Ragland K, et al. Treatment interruptions predict resistance in HIV-positive individuals purchasing fixed-dose combination antiretroviral therapy in Kampala, Uganda. AIDS. 2007;21:965-971.
15. Parienti JJ, Ragland K, Lucht F, et al. Average adherence to boosted protease inhibitor therapy, rather than the pattern of missed doses, as a predictor of HIV RNA replication. Clin Infect Dis. 2010;50: 1192-1197.
16. Parienti JJ, Das-Douglas M, Massari V, et al. Not all missed doses are the same: sustained NNRTI treatment interruptions predict HIV rebound at low-to-moderate adherence levels. PLoS One. 2008;3:e2783.
17. Parienti JJ, Massari V, Descamps D, et al. Predictors of virologic failure and resistance in HIV-infected patients treated with nevirapine- or efavirenz-based antiretroviral therapy. Clin Infect Dis. 2004;38:1311-1316.
18. Polley EC, van der Laan MJ. Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Available at: http://biostats. bepress.com/ucbbiostat/paper266/2010. Accessed February 16, 2015.
19. van der Laan MJ, Polley EC, Hubbard AE. Super learner. Stat Appl Genet Mol Biol. 2007;6:Article25.
20. Liu H, Wilson IB, Goggin K, et al. MACH14: a multi-site collaboration on ART adherence among 14 institutions. AIDS Behav. 2013;17:127-141.
21. Havlir DV, Koelsch KK, Strain MC, et al. Predictors of residual viremia in HIV-infected patients successfully treated with efavirenz and lamivudine plus either tenofovir or stavudine. J Infect Dis. 2005;191:1164-1168.
22. Phan V, Thai S, Koole O, et al. Validation of a clinical prediction score to target viral load testing in adults with suspected first-line treatment failure in resource-constrained settings. J Acquir Immune Defic Syndr. 2013;62:509-516.
23. Rosenblum M, Deeks SG, van der Laan M, et al. The risk of virologic failure decreases with duration of HIV suppression, at greater than 50% adherence to antiretroviral therapy. PLoS One. 2009;4:e7196.
24. Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. 1996;58:267-288.
25. Hastie T, Tibshirani R. Generalized additive models. Stat Sci. 1986;1: 297-318.
26. Hastie T. Generalized additive models. In: Chambers JM, Hastie TJ, eds. Statistical Models in S. Boca Raton, FL: Chapman & Hall/CRC Press. 1992:249-304.
27. Friedman JH. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29:1189-1232.
28. Friedman JH. Multivariate adaptive regression splines (with discussion). Ann Stat. 1991;19:1-141.
29. Rose S, Fireman B, van der Laan M. Nested Case-Control risk score prediction. In: van der Laan M, Rose S, eds. Targeted Learning: Causal Inference for Observational and Experimental Data. New York, NY: Springer; 2011:239-245.
30. van der Laan M, Dudoit S, Keles S. Asymptotic Optimality of likelihoodbased cross-validation. Stat Appl Genet Mol Biol. 2006;3:Article4.
31. LeDell E, Petersen ML, van der Laan MJ. Computationally Efficient Confidence Intervals for Cross-validated Area Under the ROC Curve Estimates U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 312. 2012.
32. MATLAB 8.0 and Statistics Toolbox 8.1, The MathWorks, Inc., Natick, Massachusetts, United States.
33. R Core Team (2013). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL. Available at: http://www.R-project.org/. Accessed February 16, 2015.
34. Sing T, Sander O, Beerenwinkel N, et al. ROCR: Visualizing the performance of scoring classifiers. R package version 1.0-4. Available at: http://CRAN.R-project.org/package=ROCR. 2009. Accessed February 16, 2015.
35. Polley E, van der Laan MJ. SuperLearner: Super Learner Prediction. R package version 2.0-9. Available at: http://CRAN.R-project.org/package=SuperLearner. 2012. Accessed February 16, 2015.
36. LeDell E, Petersen ML, van der Laan MJ. cvAUC: Cross-Validated Area Under the ROC Curve Confidence Intervals. R package version 1.0-0. Available at: http://CRAN.R-project.org/package=cvAUC. 2013. Accessed February 16, 2015.
37. Haberer JE, Kahane J, Kigozi I, et al. Real-time adherence monitoring for HIV antiretroviral therapy. AIDS Behav. 2010;14:1340-1346.
38. Haberer JE, Kiwanuka J, Nansera D, et al. Real-time adherence monitoring of antiretroviral therapy among HIV-infected adults and children in rural uganda. AIDS. 2013;27(13):2166-2168.
39. Freedberg KA, Hirschhorn LR, Schackman BR, et al. Cost-effectiveness of an intervention to improve adherence to antiretroviral therapy in HIVinfected patients. J Acquir Immune Defic Syndr. 2006;43:S113-S118.
40. Westley BP, DeLong AK, Tray CS, et al. Prediction of treatment failure using 2010 World Health Organization Guidelines is associated with high misclassification rates and drug resistance among HIV-infected Cambodian children. Clin Infect Dis. 2012;55:432-440.
41. Ferreyra C, Yun O, Eisenberg N, et al. Evaluation of clinical and immunological markers for predicting virological failure in a HIV/AIDS treatment cohort in Busia, Kenya. PLoS One. 2012;7:e49834.
42. Ingole N, Mehta P, Pazare A, et al. Performance of immunological response in predicting virological failure. AIDS Res Hum Retroviruses. 2013;29:541-546.
43. Keiser O, MacPhail P, Boulle A, et al. Accuracy of WHO CD4 cell count criteria for virological failure of antiretroviral therapy. Trop Med Int Health. 2009;14:1220-1225.
44. Lynen L, An S, Koole O, et al. An algorithm to optimize viral load testing in HIV-positive patients with suspected first-line antiretroviral therapy failure in Cambodia. J Acquir Immune Defic Syndr. 2009;52:40-48.
45. Robbins GK, Johnson KL, Chang Y, et al. Predicting virologic failure in an HIV clinic. Clin Infect Dis. 2010;50:779-786.
46. Liu T, Hogan JW, Wang L, et al. Optimal Allocation of Gold standard testing under constrained Availability: application to assessment of HIV treatment failure. J Am Stat Assoc. 2013;108:1173-1188.