#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Cumulative viral load as a predictor of CD4+ T-cell response to antiretroviral therapy using Bayesian statistical models


Autoři: Joseph B. Sempa aff001;  Theresa M. Rossouw aff002;  Emmanuel Lesaffre aff003;  Martin Nieuwoudt aff001
Působiště autorů: South African Department of Science and Technology (DST)/ National Research Foundation (NRF) Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa aff001;  Institute for Cellular and Molecular Medicine, Department of Immunology, University of Pretoria, Pretoria, South Africa aff002;  Interuniversity Institute for Biostatistics and Statistical Bioinformatics, KU Leuven, Leuven, Belgium aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224723

Souhrn

Introduction

There are Challenges in statistically modelling immune responses to longitudinal HIV viral load exposure as a function of covariates. We define Bayesian Markov Chain Monte Carlo mixed effects models to incorporate priors and examine the effect of different distributional assumptions. We prospectively fit these models to an as-yet-unpublished data from the Tshwane District Hospital HIV treatment clinic in South Africa, to determine if cumulative log viral load, an indicator of long-term viral exposure, is a valid predictor of immune response.

Methods

Models are defined, to express ‘slope’, i.e. mean annual increase in CD4 counts, and ‘asymptote’, i.e. the odds of having a CD4 count ≥500 cells/μL during antiretroviral treatment, as a function of covariates and random-effects. We compare the effect of using informative versus non-informative prior distributions on model parameters. Models with cubic splines or Skew-normal distributions are also compared using the conditional Deviance Information Criterion.

Results

The data of 750 patients are analyzed. Overall, models adjusting for cumulative log viral load provide a significantly better fit than those that do not. An increase in cumulative log viral load is associated with a decrease in CD4 count slope (19.6 cells/μL (95% credible interval: 28.26, 10.93)) and a reduction in the odds of achieving a CD4 counts ≥500 cells/μL (0.42 (95% CI: 0.236, 0.730)) during 5 years of therapy. Using informative priors improves the cumulative log viral load estimate, and a skew-normal distribution for the random-intercept and measurement error results is a better fit compared to using classical Gaussian distributions.

Discussion

We demonstrate in an unpublished South African cohort that cumulative log viral load is a strong and significant predictor of both CD4 count slope and asymptote. We argue that Bayesian methods should be used more frequently for such data, given their flexibility to incorporate prior information and non-Gaussian distributions.

Klíčová slova:

Immune response – Normal distribution – Polynomials – Skewness – Statistical models – Viral load


Zdroje

1. Palella FJ, Delaney KM, Moorman AC, Loveless MO, Fuhrer J, Satten GA, et al. Declining morbidity and mortality among patients with advanced human immunodeficiency virus infection. HIV Outpatient Study Investigators. N Engl J Med. 1998;338: 853–60. doi: 10.1056/NEJM199803263381301 9516219

2. Siedner MJ, Ng CK, Bassett I V., Katz IT, Bangsberg DR, Tsai AC. Trends in CD4 count at presentation to care and treatment initiation in Sub-Saharan Africa, 2002–2013: A meta-analysis. Clin Infect Dis. 2015;60: 1120–1127. doi: 10.1093/cid/ciu1137 25516189

3. Reynolds SJ, Sendagire H, Newell K, Castelnuovo B, Nankya I, Kamya M, et al. Virologic versus immunologic monitoring and the rate of accumulated genotypic resistance to first-line antiretroviral drugs in Uganda. BMC Infect Dis. 2012;12: 381. doi: 10.1186/1471-2334-12-381 23270482

4. Castelnuovo B, Sempa J, Agnes KN, Kamya MR, Manabe YC. Evaluation of WHO Criteria for Viral Failure in Patients on Antiretroviral Treatment in Resource-Limited Settings. AIDS Res Treat. 2011;2011: 736938. doi: 10.1155/2011/736938 21541225

5. Haynes BF, Markert ML, Sempowski GD, Patel DD, Hale LP. The role of the thymus in immune reconstitution in aging, bone marrow transplantation, and HIV-1 infection. Annu Rev Immunol. 2000;18: 529–60. doi: 10.1146/annurev.immunol.18.1.529 10837068

6. Okoye AA, Picker LJ. CD4(+) T-cell depletion in HIV infection: mechanisms of immunological failure. Immunol Rev. 2013;254: 54–64. doi: 10.1111/imr.12066 23772614

7. Abbas AK, Lichtman AH, Pillai S. Basic Immunology: Functions and Disorders of the Immune System. 4th ed. Basic Immunology: Functions and Disorders of the Immune System. 4th ed. Elsevier USA; 2012. pp. 303–305.

8. Pinzone MR, Di Rosa M, Cacopardo B, Nunnari G. HIV RNA suppression and immune restoration: can we do better? Clin Dev Immunol. 2012;2012: 515962. doi: 10.1155/2012/515962 22489250

9. Semeere AS, Lwanga I, Sempa J, Parikh S, Nakasujja N, Cumming R, et al. Mortality and immunological recovery among older adults on antiretroviral therapy at a large urban HIV clinic in Kampala, Uganda. J Acquir Immune Defic Syndr. 2014;67: 382–9. doi: 10.1097/QAI.0000000000000330 25171733

10. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10: 37–48. Available: http://www.ncbi.nlm.nih.gov/pubmed/9888278 9888278

11. Prague M, Commenges D, Gran JM, Ledergerber B, Young J, Furrer H, et al. Dynamic models for estimating the effect of HAART on CD4 in observational studies: Application to the Aquitaine Cohort and the Swiss HIV Cohort Study. Biometrics. 2016;61: 899–911. doi: 10.1111/biom.12564 27461460

12. Sempa JB, Ujeneza EL, Nieuwoudt M. Systematic review of statistically-derived models of immunological response in HIV-infected adults on antiretroviral therapy in Sub-Saharan Africa. PLoS One. 2017;12: e0171658. doi: 10.1371/journal.pone.0171658 28199360

13. Sempa JB, Dushoff J, Daniels MJ, Castelnuovo B, Kiragga AN, Nieuwoudt M, et al. Reevaluating Cumulative HIV-1 Viral Load as a Prognostic Predictor: Predicting Opportunistic Infection Incidence and Mortality in a Ugandan Cohort. Am J Epidemiol. 2016. doi: 10.1093/aje/kwv303 27188943

14. Marconi VC, Grandits G, Okulicz JF, Wortmann G, Ganesan A, Crum-Cianflone N, et al. Cumulative viral load and virologic decay patterns after antiretroviral therapy in HIV-infected subjects influence CD4 recovery and AIDS. PLoS One. 2011;6: e17956. doi: 10.1371/journal.pone.0017956 21625477

15. Gordon CL, Cheng AC, Cameron PU, Bailey M, Crowe SM, Mills J. Quantitative Assessment of Intra-Patient Variation in CD4+ T Cell Counts in Stable, Virologically-Suppressed, HIV-Infected Subjects. PLoS One. 2015;10: e0125248. doi: 10.1371/journal.pone.0125248 26110761

16. van Rood Y, Goulmy E, Blokland E, Pool J, van Rood J, van Houwelingen H. Month-related variability in immunological test results; implications for immunological follow-up studies. Clin Exp Immunol. 1991;86: 349–54. doi: 10.1111/j.1365-2249.1991.tb05821.x 1834381

17. Raboud JM, Haley L, Montaner JS, Murphy C, Januszewska M, Schechter MT. Quantification of the variation due to laboratory and physiologic sources in CD4 lymphocyte counts of clinically stable HIV-infected individuals. J Acquir Immune Defic Syndr Hum Retrovirol. 1995;10 Suppl 2: S67–73. Available: http://www.ncbi.nlm.nih.gov/pubmed/7552516

18. Lesaffre E, Lawson AB. Bayesian Biostatistics. Chichester, UK: John Wiley & Sons, Ltd; 2012. doi: 10.1002/9781119942412

19. Gelman A, Andrew. Bayesian data analysis. 3rd Edition. Chapman & Hall/CRC texts in statistical science. CRC Press; 2014. doi: 10.1007/S13398-014-0173-7.2

20. Lawrie D, Coetzee LM, Becker P, Mahlangu J, Stevens W, Glencross DK. Local reference ranges for full blood count and CD4 lymphocyte count testing. S Afr Med J. 2009;99: 243–8. Available: http://www.ncbi.nlm.nih.gov/pubmed/19588777 19588777

21. Lewden C, Chene G, Morlat P, Raffi F, Dupon M, Dellamonica P, et al. HIV-infected adults with a CD4 cell count greater than 500 cells/mm3 on long-term combination antiretroviral therapy reach same mortality rates as the general population. J Acquir Immune Defic Syndr. 2007;46: 72–7. doi: 10.1097/QAI.0b013e318134257a 17621240

22. Nash D, Katyal M, Brinkhof MWG, Keiser O, May M, Hughes R, et al. Long-term immunologic response to antiretroviral therapy in low-income countries: a collaborative analysis of prospective studies. AIDS. 2008;22: 2291–302. doi: 10.1097/QAD.0b013e3283121ca9 18981768

23. Grennan JT, Loutfy MR, Su D, Harrigan PR, Cooper C, Klein M, et al. Magnitude of virologic blips is associated with a higher risk for virologic rebound in HIV-infected individuals: a recurrent events analysis. J Infect Dis. 2012;205: 1230–8. doi: 10.1093/infdis/jis104 22438396

24. Easterbrook PJ, Ives N, Waters A, Mullen J, O’Shea S, Peters B, et al. The natural history and clinical significance of intermittent viraemia in patients with initial viral suppression to < 400 copies/ml. AIDS. 2002;16: 1521–7. Available: http://www.ncbi.nlm.nih.gov/pubmed/12131190 doi: 10.1097/00002030-200207260-00009 12131190

25. Maman D, Pujades-Rodriguez M, Subtil F, Pinoges L, McGuire M, Ecochard R, et al. Gender differences in immune reconstitution: a multicentric cohort analysis in sub-Saharan Africa. PLoS One. 2012;7: e31078. doi: 10.1371/journal.pone.0031078 22363550

26. Reda AA, Biadgilign S, Deribew A, Gebre B, Deribe K. Predictors of change in CD4 lymphocyte count and weight among HIV infected patients on anti-retroviral treatment in Ethiopia: a retrospective longitudinal study. PLoS One. 2013;8: e58595. doi: 10.1371/journal.pone.0058595 23573191

27. Maskew M, MacPhail AP, Whitby D, Egger M, Fox MP. Kaposi sarcoma-associated herpes virus and response to antiretroviral therapy: a prospective study of HIV-infected adults. J Acquir Immune Defic Syndr. 2013;63: 442–8. doi: 10.1097/QAI.0b013e3182969cc1 23614996

28. Sax PE. Editorial commentary: can we break the habit of routine CD4 monitoring in HIV care? Clin Infect Dis. 2013;56: 1344–6. doi: 10.1093/cid/cit008 23315314

29. Corbeau P, Reynes J. Immune reconstitution under antiretroviral therapy: the new challenge in HIV-1 infection. Blood. 2011;117: 5582–90. doi: 10.1182/blood-2010-12-322453 21403129

30. Awoke T, Worku A, Kebede Y, Kasim A, Birlie B, Braekers R, et al. Modeling outcomes of first-line antiretroviral therapy and rate of CD4 counts change among a cohort of HIV/AIDS patients in Ethiopia: A retrospective cohort study. PLoS One. 2016;11: 1–18. doi: 10.1371/journal.pone.0168323 27997931

31. De Beaudrap P, Etard J-F, Diouf A, Ndiaye I, Guèye NF, Guèye PM, et al. Modeling CD4+ cell count increase over a six-year period in HIV-1-infected patients on highly active antiretroviral therapy in Senegal. Am J Trop Med Hyg. 2009;80: 1047–53. Available: http://www.ncbi.nlm.nih.gov/pubmed/19478274 19478274

32. Sarfo FS, Sarfo MA, Kasim A, Phillips R, Booth M, Chadwick D. Long-term effectiveness of first-line non-nucleoside reverse transcriptase inhibitor (NNRTI)-based antiretroviral therapy in Ghana. J Antimicrob Chemother. 2014;69: 254–61. doi: 10.1093/jac/dkt336 24003181

33. Chen R. Bayesian Inference on Mixed-effects Models with Skewed Distributions for HIV longitudinal Data. University of South Florida. 2012. Available: http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=5494&context=etd

34. Sahu SK, Dey DK, Branco MD. A New Class of Multivariate Skew Distributions with Applications to Bayesian Regression. Source Can J Stat / La Rev Can Stat. 2003;31: 129–150. Available: http://www.jstor.org/stable/3316064

35. Tanner MA, Hung Wong W. The Calculation of Posterior Distributions by Data Augmentation. Source J Am Stat Assoc. 1987. Available: https://www.jstor.org/stable/pdf/2289457.pdf?refreqid=excelsior%3A551f5c70218443392ce5505ac1aaf32d

36. Boullé C, Kouanfack C, Laborde-Balen G, Carrieri MP, Dontsop M, Boyer S, et al. Task shifting HIV care in rural district hospitals in Cameroon: evidence of comparable antiretroviral treatment-related outcomes between nurses and physicians in the Stratall ANRS/ESTHER trial. J Acquir Immune Defic Syndr. 2013;62: 569–76. doi: 10.1097/QAI.0b013e318285f7b6 23337365

37. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B (Statistical Methodol. 2002;64: 583–639. doi: 10.1111/1467-9868.00353

38. Quintero A, Lesaffre E. Comparing hierarchical models via the marginalized deviance information criterion. 2018; 2440–2454. doi: 10.1002/sim.7649 29579777

39. Abbas AK, Lichtman AH, Pillai S. Basic Immunology: Functions and Disorders of the Immune System. 4th ed. Basic Immunology: Functions and Disorders of the Immune System. Elsevier USA; 2012. doi: 10.1002/bmb.2004.494032069999

40. Gras L, Kesselring AM, Griffin JT, van Sighem AI, Fraser C, Ghani AC, et al. CD4 cell counts of 800 cells/mm3 or greater after 7 years of highly active antiretroviral therapy are feasible in most patients starting with 350 cells/mm3 or greater. J Acquir Immune Defic Syndr. 2007;45: 183–92. doi: 10.1097/QAI.0b013e31804d685b 17414934

41. Sempa JB, Kiragga AN, Castelnuovo B, Kamya MR, Manabe YC. Among patients with sustained viral suppression in a resource-limited setting, CD4 gains are continuous although gender-based differences occur. PLoS One. 2013;8: e73190. doi: 10.1371/journal.pone.0073190 24013838

42. Lawn SD, Myer L, Bekker L-G, Wood R. CD4 cell count recovery among HIV-infected patients with very advanced immunodeficiency commencing antiretroviral treatment in sub-Saharan Africa. BMC Infect Dis. 2006;6: 59. doi: 10.1186/1471-2334-6-59 16551345

43. Nakanjako D, Kiragga AN, Musick B, Yiannoutsos C, Wools-Kaloustian K, Diero L, et al. Frequency and impact of suboptimal immune recovery on first-line antiretroviral therapy (ART) within the IeDEA-East Africa cohort. AIDS. 2016. doi: 10.1097/QAD.0000000000001085 26959510

44. Kulkarni H, Okulicz JF, Grandits G, Crum-Cianflone NF, Landrum ML, Hale B, et al. Early postseroconversion CD4 cell counts independently predict CD4 cell count recovery in HIV-1-postive subjects receiving antiretroviral therapy. J Acquir Immune Defic Syndr. 2011;57: 387–95. doi: 10.1097/QAI.0b013e3182219113 21546844

45. Williams BG, Korenromp EL, Gouws E, Schmid GP, Auvert B, Dye C. HIV infection, antiretroviral therapy, and CD4+ cell count distributions in African populations. J Infect Dis. 2006;194: 1450–8. doi: 10.1086/508206 17054076

46. Sia D, Onadja Y, Hajizadeh M, Heymann SJ, Brewer TF, Nandi A. What explains gender inequalities in HIV/AIDS prevalence in sub-Saharan Africa? Evidence from the demographic and health surveys. BMC Public Health. 2016;16: 1136. doi: 10.1186/s12889-016-3783-5 27809824

47. Strategies for Management of Antiretroviral Therapy (SMART) Study Group, El-Sadr WM, Lundgren JD, Neaton JD, Gordin F, Abrams D, et al. CD4+ count-guided interruption of antiretroviral treatment. N Engl J Med. 2006;355: 2283–96. doi: 10.1056/NEJMoa062360 17135583

48. INSIGHT START Study Group, Lundgren JD, Babiker AG, Gordin F, Emery S, Grund B, et al. Initiation of Antiretroviral Therapy in Early Asymptomatic HIV Infection. N Engl J Med. 2015;373: 795–807. doi: 10.1056/NEJMoa1506816 26192873

49. TEMPRANO ANRS 12136 Study Group, Danel C, Moh R, Gabillard D, Badje A, Le Carrou J, et al. A Trial of Early Antiretrovirals and Isoniazid Preventive Therapy in Africa. N Engl J Med. 2015;373: 808–22. doi: 10.1056/NEJMoa1507198 26193126

50. IeDEA and COHERE Cohort Collaborations. Global Trends in CD4 Cell Count at the Start of Antiretroviral Therapy: Collaborative Study of Treatment Programs. Clin Infect Dis. 2018;66: 893–903. doi: 10.1093/cid/cix915 29373672

51. Fung ICH, Gambhir M, van Sighem A, de Wolf F, Garnett GP. The clinical interpretation of viral blips in HIV patients receiving antiviral treatment: are we ready to infer poor adherence? J Acquir Immune Defic Syndr. 2012;60: 5–11. doi: 10.1097/QAI.0b013e3182487a20 22267019

52. Wei X, Ghosh SK, Taylor ME, Johnson VA, Emini EA, Deutsch P, et al. Viral dynamics in human immunodeficiency virus type 1 infection. Nature. 1995;373: 117–122. doi: 10.1038/373117a0 7529365

53. Patrikar S, Basannar DR, Bhatti VK, Kotwal A, Gupta RM, Grewal RS. Rate of decline in CD4 count in HIV patients not on antiretroviral therapy. Med J Armed Forces India. 2014;70: 134–138. doi: 10.1016/j.mjafi.2013.08.005 24843201

54. Siedner MJ. START or SMART? Timing of Antiretroviral Therapy Initiation and Cardiovascular Risk for People With Human Immunodeficiency Virus Infection. Open forum Infect Dis. 2016;3: ofw032. doi: 10.1093/ofid/ofw032 26989755

55. Kassanjee R, Pilcher CD, Keating SM, Facente SN, McKinney E, Price MA, et al. Independent assessment of candidate HIV incidence assays on specimens in the CEPHIA repository. Aids. 2014;28: 2439–2449. doi: 10.1097/QAD.0000000000000429 25144218

56. Grebe E, Welte A, Hall J, Keating SM, Facente SN, Marson K, et al. Infection Staging and Incidence Surveillance Applications of High Dynamic Range Diagnostic Immuno-Assay Platforms. JAIDS J Acquir Immune Defic Syndr. 2017;76: 547–555. doi: 10.1097/QAI.0000000000001537 28914669

57. Verbeke G, Lesaffre E. A Linear Mixed-Effects Model With Heterogeneity in the Random-Effects Population. Source J Am Stat Assoc. 1996;91: 217–221. Available: http://www.jstor.org/stable/2291398

58. Ghosh P, Branco MD, Chakraborty H. Bivariate random effect model using skew-normal distribution with application to HIV-RNA. Stat Med. 2007;26: 1255–1267. doi: 10.1002/sim.2667 16998836

59. Komárek A, Lesaffre E. Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution. Comput Stat Data Anal. 2008;52: 3441–3458. doi: 10.1016/j.csda.2007.10.024


Článek vyšel v časopise

PLOS One


2019 Číslo 11
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

KOST
Koncepce osteologické péče pro gynekology a praktické lékaře
nový kurz
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Svět praktické medicíny 5/2023 (znalostní test z časopisu)

Imunopatologie? … a co my s tím???
Autoři: doc. MUDr. Helena Lahoda Brodská, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se

#ADS_BOTTOM_SCRIPTS#