Geriatric Patients Living with HIV/AIDS (GEPPO) is a new prospective observational multicentre cohort consisting of all the HIV-positive geriatric patients being treated at 10 clinics in Italy, and HIV-negative controls attending a single geriatric clinic. The aim of this analysis of the GEPPO cohort was to compare prevalence and risk factors of individual non-communicable diseases (NCD), multi-morbidity (MM) and polypharmacy (PP) amongst HIV positive and HIV negative controls at enrolment into the GEPPO cohort. This cross-sectional study was conducted between June 2015 and May 2016. The duration of HIV infection was subdivided into three intervals: < 10, 10–20 and > 20 years. The NCD diagnoses were based on guidelines defined criteria, including cardiovascular disease, hypertension, type 2 diabetes, chronic kidney disease, dyslipidaemia, chronic obstructive pulmonary disease. MM was classified as the presence of two or more co-morbidities. The medications prescribed for the treatment of comorbidities were collected in both HIV positive and HIV negative group from patient files and were categorized using the Anatomical Therapeutic Chemical (ATC) classification. PP was defined as the presence of five or more drug components other than anti-retroviral agents. The study involved a total of 1573 patient: 1258 HIV positive and 315 HIV negative). The prevalence of individual comorbidities was similar in the two groups with the exception of dyslipidaemia, which was more frequent in the HIV-positive patients (p < 0.01). When the HIV-positive group was stratified based on the duration of HIV infection, most of the co-morbidities were significantly more frequent than in control patients, except for hypertension and cardiovascular disease, while COPD was more prevalent in the control group. MM and PP were both more prevalent in the HIV-positive group, respectively 64% and 37%. MM and PP burden in geriatric HIV positive patients are related to longer duration of HIV-infection rather than older age per se.

Aging with HIV is associated with multisystem vulnerability that might be well characterized by frailty. We sought to construct a frailty index based on health deficit accumulation in a large HIV clinical cohort and evaluate its validity including the ability to predict mortality and incident multimorbidity. This is an analysis of data from the prospective Modena HIV Metabolic Clinic cohort, 2004-2014. Routine health variables were screened for potential inclusion in a frailty index. Content, construct, and criterion validity of the frailty index were assessed. Multivariable regression models were built to investigate the ability of the frailty index to predict survival and incident multimorbidity (at least two chronic disease diagnoses) after adjusting for known HIV-related and behavioral factors. Two thousand, seven hundred and twenty participants (mean age 46 ± 8; 32% women) provided 9784 study visits; 37 non-HIV-related variables were included in a frailty index. The frailty index exhibited expected characteristics and met validation criteria. Predictors of survival were frailty index (0.1 increment, adjusted hazard ratio 1.63, 95% confidence interval 1.05-2.52), current CD4 cell count (0.48, 0.32-0.72), and injection drug use (2.51, 1.16-5.44). Predictors of incident multimorbidity were frailty index (adjusted incident rate ratio 1.98, 1.65-2.36), age (1.07, 1.05-1.09), female sex (0.61, 0.40-0.91), and current CD4 cell count (0.71, 0.59-0.85). Among people aging with HIV in northern Italy, a frailty index based on deficit accumulation predicted survival and incident multimorbidity independently of HIV-related and behavioral risk factors. The frailty index holds potential value in quantifying vulnerability among people aging with HIV.

People aging with HIV show variable health trajectories. Our objective was to identify longitudinal predictors of frailty severity and mortality among a group aging with HIV. Exploratory analyses employing a multistate transition model, with data from the prospective Modena HIV Metabolic Clinic Cohort Study, based in Northern Italy, begun in 2004. Participants were followed over four years from their first available visit. We included all 963 participants (mean age 46.8±7.1; 29% female; 89% undetectable HIV viral load; median current CD4 count 549, IQR 405–720; nadir CD4 count 180, 81–280) with four-year data. Frailty was quantified using a 31-item frailty index. Outcomes were frailty index score or mortality at four-year follow-up. Candidate predictor variables were baseline frailty index score, demographic (age, sex), HIV-disease related (undetectable HIV viral load, current CD4+ T-cell count, nadir CD4 count, duration of HIV infection, and duration of antiretroviral therapy [ARV] exposure), and behavioral factors (smoking, injection drug use (IDU), and hepatitis C virus co-infection). Four-year mortality was 3.0% (n = 29). In multivariable analyses, independent predictors of frailty index at follow-up were baseline frailty index (RR 1.06, 95% CI 1.05–1.07), female sex (RR 0.93, 95% CI 0.87–0.98), nadir CD4 cell count (RR 0.96, 95% CI 0.93–0.99), duration of HIV infection (RR 1.06, 95% CI 1.01–1.12), duration of ARV exposure (RR 1.08, 95% CI 1.02–1.14), and smoking pack-years (1.03, 1.01–1.05). Independent predictors of mortality were baseline frailty index (OR 1.19, 1.02–1.38), current CD4 count (0.34, 0.20–0.60), and IDU (2.89, 1.30–6.42). Demographic, HIV-disease related, and social and behavioral factors appear to confer risk for changes in frailty severity and mortality among people aging with HIV.

To assess the accuracy of risk prediction algorithms used in the general population and an HIV-specific algorithm to predict hard cardiovascular events. We compared the pooled equation algorithm (PE) proposed by the American Heart Association with the Framingham risk score (FRS) and the HIV-specific DAD (Data Collection on Adverse Effects of Anti-HIV Drugs) algorithm in a cohort of 2550 HIV+ patients followed for 17 337 patient-years. During follow-up we recorded 67 myocardial infarctions and 2 cardiovascular deaths. PE and FRS identified and missed the same number of events (44 of 69 identified by PE and 49 of 69 by FRS). Similarly, DAD and FRS predicted and missed the same number of events (38 of 64 and 44 of 64 identified, respectively). All algorithms showed moderate sensitivity, specificity and positive predictive values, but high negative predictive values. However, PE and DAD identified more patients with no events than FRS (13.8% and 9.3% net reclassification improvement, respectively). All algorithms showed a modest predictive ability, although the PE and DAD algorithms identified more patients at low risk.