Predictors of chronic kidney disease and utility of risk prediction scores in HIV positive individuals

• Published in: AIDS (London) Vol. 32; no. 13; pp. 1829 - 1835
• Main Authors: Woolnough, Emily L, Hoy, Jennifer F, Cheng, Allen C, Walker, Rowan G, Chrysostomou, Anastasia, Woolley, Ian, Langham, Freya, Moso, Michael A, Weeraratne, Achini, Trevillyan, Janine M
• Format: Journal Article
• Language: English
• Published: England Copyright Wolters Kluwer Health, Inc 24.08.2018
• Subjects: AIDS/HIV
• ISSN: 0269-9370; 1473-5571
• DOI: 10.1097/QAD.0000000000001901

OBJECTIVE:This study aimed to validate existing risk prediction scores and identify predictors of chronic kidney disease (CKD) in the setting of HIV. DESIGN AND METHODS:A retrospective cohort study of HIV-positive individuals (n = 748) with baseline estimated glomerular filtration rate (eGFR) >60 ml/min was conducted at the Alfred Hospital, Melbourne Australia. Multivariable regression analysis was performed to determine factors associated with development of CKD, defined as two consecutive measurements of eGFR<60 ml/min. The performance of CKD risk scores proposed by the Data Collection on Adverse Effects of Anti-HIV Drugs (D:A:D) Study Group and Scherzer et al. were estimated by the area under the receiver operator curve (AUROC). RESULTS:CKD developed in 37 individuals (5.0%), at a median of 4.7 (IQR 2.2, 6.2) years. Older age (OR 3.03, 95% CI1.20, 7.65, p = 0.02) and lower baseline eGFR (OR 10.39, 95% CI4.73, 22.83, p < 0.001) were associated with the development of CKD. Neither current nor cumulative tenofovir disoproxil fumarate (TDF) use was associated with progression to CKD (current TDF HR 1.05, 95% CI0.54, 2.07, p = 0.88; cumulative TDF HR 1.03, 95% CI0.86, 1.24, p = 0.75). The Short D:A:D and Scherzer scores were well calibrated, with the Short D:A:D score demonstrating superior discrimination (Short D:A:D AUROC 0.85, Scherzer AUROC 0.78, p = 0.02). CONCLUSION:Older individuals and those with a lower baseline eGFR are at higher risk for CKD. Risk prediction tools may be useful in identifying those at greatest risk, who may benefit from aggressive management of risk factors.