Simulating the development and progression of Chronic Kidney Disease and osteoporosis in people living with HIV

Simulating the development and progression of Chronic Kidney Disease and osteoporosis in people living with HIV

Authors

  • Silvano Adami Rheumatology, University of Verona
  • Paolo Maggi Institute of Infectious Disease, University of Bari
  • Vincenzo Montinaro Division of Nephrology, University of Bari, Polyclinic
  • Massimiliano Povero AdRes HE & OR, Turin, Italy
  • Lorenzo Pradelli AdRes HE & OR, Turin, Italy
  • Rita Bellagamba National Institute for Infectious Diseases L. Spallanzani, Rome, Italy
  • Paolo Bonfanti AO Provincia di Lecco - Ospedale "A.Manzoni", Lecco, Italy
  • Antonio Di Biagio Clinica Malattie Infettive. IRCCS Azienda Ospedaliera Universitaria San Martino - IST Istituto Nazionale di Ricerca sul Cancro di Genova, Italy
  • Stefano Rusconi Divisione Clinicizzata di Malattie Infettive. Dipartimento di Scienze Biomediche e Cliniche "Luigi Sacco". Università degli Studi di Milano
  • Francesco Maria Di Campli ViiV Healthcare
  • Giuseppe Forastieri ViiV Healthcare
  • Michele Mancini ViiV Healthcare

DOI:

https://doi.org/10.7175/fe.v17i1S.1236

Keywords:

HIV, Chronic Kidney Disease, Osteoporosis

Abstract

The “chronicization” of HIV infection brings about a growing necessity to attentively evaluate current and potential complications when prescribing the individual therapeutic regimen. Starting from this need, we developed two HIV-comorbidity simulators that, basing on the evidence available in medical literature and starting from the current clinical and demographic features of the individual patient, project and compare the risks of developing and worsening of nephropathy and osteopathy associated with possible ARV regimens. These simulators are embedded in a desktop, user-friendly software thought to be used by the treating physician during prescription discussion with his/her patients, in order to highlight expected clinical outcomes and healthcare resource consumption that may differ according to the therapeutic strategy selected. In this article we present the sources and methods used in developing the mathematical models, alongside a set of examples and the results of cohort-level validation runs.

References

Joint United Nations Programme on HIV/AIDS (UNAIDS). Global report: UNAIDS report on the global AIDS epidemic. Geneva: UNAIDS, 2013

Antiretroviral Therapy Cohort Collaboration. Life expectancy of individuals on combination antiretroviral therapy in high-income countries: a collaborative analysis of 14 cohort studies. Lancet 2008; 372: 293-9; http://dx.doi.org/10.1016/S0140-6736(08)61113-7

Ross MJ, Klotman PE. Recent progress in HIV-associated nephropathy. J Am Soc Nephrol 2002; 13: 2997-3004; http://dx.doi.org/10.1097/01.ASN.0000040750.40907.99

Abbott KC, Hypolite I, Welch PG, et al. J Human immunodeficiency virus/acquired immunodeficiency syndrome-associated nephropathy at end-stage renal disease in the United States: patient characteristics and survival in the pre highly active antiretroviral therapy era. J Nephrol 2001; 14: 377-83

Mallipattu SK, Wyatt CM, He JC. The New Epidemiology of HIVRelated Kidney Disease. J AIDS Clin Res 2012; Suppl 4: 001

Tordato F, Cozzi Lepri A, Cicconi P, et al.; ICONA Foundation Study Group. Evaluation of glomerular filtration rate in HIV-1-infected patients before and after combined antiretroviral therapy exposure. HIV Med 2011; 12: 4-13; http://dx.doi.org/10.1111/j.1468-1293.2010.00855.x.

Ryom L, Mocroft A, Kirk O, et al.; D:A:D Study Group. Association between antiretroviral exposure and renal impairment among HIV-positive persons with normal baseline renal function: the D:A:D study. J Infect Dis 2013; 207: 1359-69; http://dx.doi.org/10.1093/infdis/jit043

Mocroft A, Kirk O, Reiss P, et al.; EuroSIDA Study Group. Estimated glomerular filtration rate, chronic kidney disease and antiretroviral drug use in HIV-positive patients. AIDS 2010; 24: 1667-78; http://dx.doi.org/10.1097/QAD.0b013e328339fe53

Hammer SM, Saag MS, Schechter M, et al.; International AIDS Society--USA Panel. Treatment for adult HIV infection: 2006 recommendations of the International AIDS Society-USA panel. JAMA 2006; 296: 827-43; http://dx.doi.org/10.1001/jama.296.7.827

Gallant JE, DeJesus E, Arribas JR, et al.; Study 934 Group. Tenofovir DF, emtricitabine, and efavirenz vs. zidovudine, lamivudine, and efavirenz for HIV. N Engl J Med 2006; 354: 251-60; http://dx.doi.org/10.1056/NEJMoa051871

Saag MS, Cahn P, Raffi F, et al.; FTC-301A Study Team. Efficacy and safety of emtricitabine vs stavudine in combination therapy in antiretroviral-naive patients: a randomized trial. JAMA 2004; 292: 180-9; http://dx.doi.org/10.1001/jama.292.2.180

Gallant JE, Parish MA, Keruly JC, et al. Changes in renal function associated with tenofovir disoproxil fumarate treatment, compared with nucleoside reverse-transcriptase inhibitor treatment. Clin Infect Dis 2005; 40: 1194-8; http://dx.doi.org/10.1086/428840

Scherzer R, Estrella M, Li Y, et al. Association of tenofovir exposure with kidney disease risk in HIV infection. AIDS 2012; 26: 867-75; http://dx.doi.org/10.1097/QAD.0b013e328351f68f

Islam FM, Wu J, Jansson J, et al. Relative risk of renal disease among people living with HIV: a systematic review and meta-analysis. BMC Public Health 2012; 12: 234; http://dx.doi.org/10.1186/1471-2458-12-234

Jose S, Hamzah L, Campbell LJ, et al.; UK Collaborative HIV Cohort Study Steering Committee. Incomplete reversibility of estimated glomerular filtration rate decline following tenofovir disoproxil fumarate exposure. J Infect Dis 2014; 210: 363-73; http://dx.doi.org/10.1093/infdis/jiu107

Ryom L, Mocroft A, Kirk O, et al. Predictors of advanced chronic kidney disease and

end-stage renal disease in HIV-positive persons. AIDS 2014; 28: 187-99; http://dx.doi.org/10.1097/QAD.0000000000000042

Laprise C, Baril JG, Dufresne S, et al. Association between tenofovir exposure and reduced kidney function in a cohort of HIV-positive patients: results from 10 years of follow-up. Clin Infect Dis 2013; 56: 567-75; http://dx.doi.org/10.1093/cid/cis937

Khan S, Amedia CA Jr. Economic burden of chronic kidney disease. J Eval Clin Pract 2008; 14: 422-34; http://dx.doi.org/10.1111/j.1365-2753.2007.00883.x

Levey AS, Coresh J. Chronic kidney disease. Lancet 2012; 379: 165-80; http://dx.doi.org/10.1016/S0140-6736(11)60178-5

Echouffo-Tcheugui JB, Kengne AP. Risk models to predict chronic kidney disease and its progression: a systematic review. PLoS Med 2012; 9: e1001344; http://dx.doi.org/10.1371/journal.pmed.1001344

Ando M, Yanagisawa N, Ajisawa A, et al. A simple model for predicting incidence of chronic kidney disease in HIV-infected patients. Clin Exp Nephrol 2011; 15: 242-7; http://dx.doi.org/10.1007/s10157-010-0393-x

Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31-41; http://dx.doi.org/10.7326/0003-4819-150-9-200905050-00006

Levey AS, Bosch JP, Lewis JB, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999; 130: 461-70

Levey AS, Stevens LA, Schmid CH, et al.; CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration). A new equation to estimate glomerular filtration rate. Ann Intern Med 2009; 150: 604-12; http://dx.doi.org/10.7326/0003-4819-150-9-200905050-00006

Rule AD, Larson TS, Bergstralh EJ, et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med 2004; 141: 929-37; http://dx.doi.org/10.7326/0003-4819-141-12-200412210-00009

Hoerger TJ, Wittenborn JS, Segel JE, et al.; Centers for Disease Control and Prevention CKD Initiative. A health policy model of CKD: 1. Model construction, assumptions, and validation of health consequences. Am J Kidney Dis 2010; 55: 452-62; http://dx.doi.org/10.1053/j.ajkd.2009.11.016

27. Coresh J, Turin TC, Matsushita K, et al.; CKD Prognosis Consortium. Decline in estimated glomerular filtration rate and subsequent risk of end-stage renal disease and mortality. JAMA 2014; 311: 2518-31

Cohen MS, Chen YQ, McCauley M, et al.; HPTN 052 Study Team. Prevention of HIV-1 infection with early antiretroviral therapy. N Engl J Med 2011; 365: 493-505; http://dx.doi.org/10.1056/NEJMoa1105243

Maggi P, Montinaro V, Bellacosa C, et al. Early markers of tubular dysfunction in antiretroviral-experienced HIV-infected patients treated with tenofovir versus abacavir. AIDS Patient Care STDS 2012; 26: 5-11; http://dx.doi.org/10.1089/apc.2011.0185

Landray MJ, Emberson JR, Blackwell L, et al. Prediction of ESRD and Death Among People With CKD: The Chronic Renal Impairment in Birmingham (CRIB) Prospective Cohort Study. Am J Kidney Dis 2010; 56: 1082-94; http://dx.doi.org/10.1053/j.ajkd.2010.07.016

Desai AS, Toto R, Jarolim P, et al. Association between cardiac biomarkers and the development of ESRD in patients with type 2 diabetes mellitus, anemia, and CKD. Am J Kidney Dis 2011; 58: 717-28; http://dx.doi.org/10.1053/j.ajkd.2011.05.020

Tangri N, Stevens LA, Griffith J, et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 2011; 305: 1553-9; http://dx.doi.org/10.1001/jama.2011.451

Hallan SI, Ritz E, Lydersen S, et al. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol 2009; 20: 1069-77; http://dx.doi.org/10.1681/ASN.2008070730

NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy. Osteoporosis prevention, diagnosis, and therapy. JAMA 2001; 285: 785-95

Adami S, Bianchi G, Brandi ML, et al. Validation and further development of the WHO 10-year fracture risk assessment tool in Italian postmenopausal women: project rationale and description. Clin Exp Rheumatol 2010; 28: 561-70

Bonjoch A, Figueras M, Estany C, et al. High prevalence of and progression to low bone mineral density in HIV-infected patients: a longitudinal cohort study. AIDS 2010; 24: 2827-33; http://dx.doi.org/10.1097/QAD.0b013e328340a28d

Brown TT, Qaqish RB. Antiretroviral therapy and the prevalence of osteopenia and osteoporosis: a meta-analytic review. AIDS 2006; 20: 2165-74; http://dx.doi.org/10.1097/QAD.0b013e32801022eb

Bruera D, Luna N, David DO, et al. Decreased bone mineral density in HIV-infected patients is independent of antiretroviral therapy. AIDS 2003; 17: 1917-23; http://dx.doi.org/10.1097/00002030-200309050-00010

Trombetti A, Herrmann F, Hoffmeyer P, et al. Survival and potential years of life lost after hip fracture in men and agematched women. Osteoporos Int 2002; 13: 731-7; http://dx.doi.org/10.1007/s001980200100

Black DM, Cummings SR, Karpf DB, et al. Randomised trial of effect of alendronate on risk of fracture in women with existing vertebral fractures. Fracture Intervention Trial Research Group. Lancet 1996; 348: 1535-41; http://dx.doi.org/10.1016/S0140-6736(96)07088-2

Cummings SR, Black DM, Thompson DE, et al. Effect of alendronate on risk of fracture in women with low bone density but without vertebral fractures: results from the Fracture Intervention Trial. JAMA 1998; 280: 2077-82; http://dx.doi.org/10.1001/jama.280.24.2077

Reginster JY, Seeman E, De Vernejoul MC, et al. Strontium ranelate reduces the risk of nonvertebral fractures in postmenopausal women with osteoporosis: treatment of Peripheral Osteoporosis (TROPOS) study. J Clin Endocrinol Metab 2005; 90: 2816-22; http://dx.doi.org/10.1210/jc.2004-1774

Black DM, Delmas PD, Eastell R, et al. Once-yearly zoledronic acid for treatment of postmenopausal osteoporosis. N Engl J Med 2007; 356: 1809-22; http://dx.doi.org/10.1056/NEJMoa067312

McClung MR, Geusens P, Miller PD, et al.; Hip Intervention Program Study Group. Effect of risedronate on the risk of hip fracture in elderly women. N Engl J Med 2001; 344: 333-40; http://dx.doi.org/10.1056/NEJM200102013440503

Adami S, Bertoldo F, Gatti D, et al. Treatment thresholds for osteoporosis and reimbursability criteria: perspectives associated with fracture risk-assessment tools. Calcif Tissue Int 2013; 93: 195-200; http://dx.doi.org/10.1007/s00223-013-9748-0

Black DM, Arden NK, Palarmo L, et al. Prevalent vertebral deformities predict hip fractures and new vertebral deformities but not wrist fractures. J Bone Miner Res 1999; 14: 821-8; http://dx.doi.org/10.1359/jbmr.1999.14.5.821

Klotzbuecher CM, Ross PD, Landsman P, et al. Patients with prior fractures have an increased risk of future fractures: a summary of the literature and statistical synthesis. J Bone Miner Res 2000; 15: 721-39; http://dx.doi.org/10.1359/jbmr.2000.15.4.721

Cummings SR, Nevitt MC, Browner WS, et al. Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 1995; 332: 767-73; http://dx.doi.org/10.1056/NEJM199503233321202

Kanis JA, Johnell O, De Laet C, et al. A meta-analysis of previous fracture and subsequent fracture risk. Bone 2004; 35: 375-82; http://dx.doi.org/10.1016/j.bone.2004.06.017

Ross PD, Davis JW, Epstein RS, et al. Pre-existing fractures and bone mass predict vertebral fracture incidence in women. Ann Intern Med 1991; 114: 919-23; http://dx.doi.org/10.7326/0003-4819-114-11-919

Black DM, Steinbuch M, Palermo L, et al. An assessment tool for predicting fracture risk in postmenopausal women. Osteoporos Int 2001; 12: 519-28; http://dx.doi.org/10.1007/s001980170072

Ismail A, Cockerill W, Cooper C, et al. Prevalent vertebral deformity predicts incident hip though not distal forearm fracture results from the European prospective osteoporosis. Osteoporos Int 2001; 12: 85-90; http://dx.doi.org/10.1007/s001980170138

Hasserius R, Johnell O, Nilsson BE, et al. Hip fracture patients have more vertebral deformities than subjects in population-based studies. Bone 2003; 32: 180-4; http://dx.doi.org/10.1016/S8756-3282(02)00951-1

Davis JW, Grove JS, Wasnich RD, et al. Spatial relationships between prevalent and incident spine fractures. Bone 1999; 24: 261-4; http://dx.doi.org/10.1016/S8756-3282(98)00176-8

Kanis JA, Johnell O, Oden A, et al. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 2008; 19: 385-97; http://dx.doi.org/10.1007/s00198-007-0543-5

Kanis JA, Johnell O, Oden A, et al. Smoking and fracture risk: a meta-analysis. Osteoporos Int 2005; 16: 155-62; http://dx.doi.org/10.1007/s00198-004-1755-6

Vestergaard P, Mosekilde L. Fracture risk associated with smoking: a meta-analysis analysis. J Intern Med 2003; 254: 572-83; http://dx.doi.org/10.1111/j.1365-2796.2003.01232.x

Chakkalakal DA. Alcohol-induced bone loss and deficient bone repair. Alcohol Clin Exp Res 2005; 29: 2077-90; http://dx.doi.org/10.1097/01.alc.0000192039.21305.55

Kanis JA, Johansson H, Johnell O, et al. Alcohol intake as a risk factor for fracture. Osteoporos Int 2005; 16: 737-42; http://dx.doi.org/10.1007/s00198-004-1734-y

Berg KM, Kunins HV, Jackson JL, et al. Association between alcohol consumption and both osteoporotic fracture and bone density. Am J Med 2008; 121: 406-18; http://dx.doi.org/10.1016/j.amjmed.2007.12.012

Hooyman JR, Melton LJ, Nelson AM, et al. Fractures after rheumatoid arthritis: a population-based study. Arthritis Rheum 1984; 27: 1353-61; http://dx.doi.org/10.1002/art.1780271205

Ørstavik RE, Haugeberg G, Mowinckel P, et al. Vertebral deformities in rheumatoid arthritis: a comparison with population-based controls. Arch Intern Med 2004; 164: 420-5; http://dx.doi.org/10.1001/archinte.164.4.420

Van Staa TP, Geusens P, Bijlsma JW, et al. Clinical assessment of the long-term risk of fracture in patients with rheumatoid arthritis. Arthritis Rheum 2006; 54: 3104-12; http://dx.doi.org/10.1002/art.22117

Harrison BJ, Hutchinson CE, Adams J, et al. Assessing periarticular bone mineral density in patients with early psoriatic arthritis or rheumatoid arthritis. Ann Rheum Dis 2002; 61: 1007-11; http://dx.doi.org/10.1136/ard.61.11.1007

Cooper C, Carbone L, Michet CJ, et al. Fracture risk in patients with ankylosing spondylitis: a population based study. J Rheumatol 1994; 21: 1877-82

Vosse D, Landewé R, Van Der Heijde D, et al. Ankylosing spondylitis and the risk of fracture: results from a large primary carebased nested case control study. Ann Rheum Dis 2009; 68: 1839-42; http://dx.doi.org/10.1136/ard.2008.100503

Womack JA, Goulet JL, Gibert C, et al. Increased Risk of Fragility Fractures among HIV Infected Compared to Uninfected Male Veterans. PLoS ONE 2011; 6: e17217; doi:10.1371/journal.pone.0017217

Yin MT, Shi Q, Hoover DR, et al. Fracture incidence in HIV-infected women: results from the Women’s Interagency HIV Study. AIDS 2010; 24: 2679-86; doi:10.1097/QAD.0b013e32833f6294

Young B, Dao CN, Buchacz K, et al. Increased Rates of Bone Fracture Among HIV-infected

Persons in the HIV Outpatient Study (HOPS) Compared With the US General Population, 2000–2006. Clin Infect Dis 2011; 52: 1061-8; http://dx.doi.org/10.1093/cid/ciq242

Cazanave C, Dupon M, Lavignolle-Aurillac V, et al. Reduced bone mineral density in HIV-infected patients: prevalence and associated factors. AIDS 2008; 22: 395-402; http://dx.doi.org/10.1097/QAD.0b013e3282f423dd

Gallant JE, Staszewski S, Pozniak AL, et al. Efficacy and safety of tenofovir DF vs stavudine in combination therapy in antiretroviral-naive patients: a 3-year randomized trial. JAMA 2004; 292: 191-201; http://dx.doi.org/10.1001/jama.292.2.191

McComsey GA, Kitch D, Daar ES, et al. Bone mineral density and fractures in antiretroviral-naïve persons randomized to receive abacavir-lamivudine or tenofovir disoproxil fumarate-emtricitabine along with efavirenz or atazanavir-ritonavir: Aids Clinical Trials Group A5224 s, a substudy of ACTG A5202. J Infect Dis 2011; 203: 1791-801; http://dx.doi.org/10.1093/infdis/jir188

Mondy K, Yarasheski K, Powderly WG, et al. Longitudinal evolution of bone mineral density and bone markers in human immunodeficiency virus-infected individuals. Clin Infect Dis 2003; 36: 482-90; http://dx.doi.org/10.1086/367569

van Vonderen MG, Lips P, van Agtmael MA, et al. First line zidovudine/lamivudine/ lopinavir/ritonavir leads to greater bone loss compared to nevirapine/lopinavir/ritonavir. AIDS 2009; 23: 1367-76; http://dx.doi.org/10.1097/QAD.0b013e32832c4947

van Vonderen MG, Mallon P, Murray B, et al. Changes in Bone Biomarkers in ARVnaïve HIVRMen Randomized to NVP/LPV/r or AZT/3TC/LPV/ r Help Explain Limited Loss of Bone Mineral Density over the First 12 Months after ART Initiation. 18th Conference on Retroviruses and Opportunistic Infections. Boston, 2011

Ofotokun I, Weitzmann N, Vunnava A, et al. HAART-induced Immune Reconstitution: A Driving Force Behind Bone Resorption in HIV/AIDS. 18th Conference on Retroviruses and Opportunistic Infections. Boston, 2011

Bedimo R, Maalouf NM, Zhang S, et al. Osteoporotic fracture risk associated with cumulative exposure to tenofovir and other antiretroviral agents. AIDS 2012; 26: 825-31; http://dx.doi.org/10.1097/QAD.0b013e32835192ae

Johansson H, Oden A, Johnell Oet al. Optimization of BMD measurements to identify high risk groups for treatment – a test analysis. J Bone Miner Res 2004; 19: 906-13; http://dx.doi.org/10.1359/jbmr.2004.19.6.906

Krege JH, Wan X, Lentle BC, et al.; CaMos Research Group. Fracture risk prediction: importance of age, BMD and spine fracture status. Bonekey Rep 2013; 2: 404; http://dx.doi.org/10.1038/bonekey.2013.138

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2016-02-29

How to Cite

Adami, S., Maggi, P., Montinaro, V., Povero, M., Pradelli, L., Bellagamba, R., … Mancini, M. (2016). Simulating the development and progression of Chronic Kidney Disease and osteoporosis in people living with HIV. Farmeconomia. Health Economics and Therapeutic Pathways, 17(1S), 3–23. https://doi.org/10.7175/fe.v17i1S.1236

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