Person:

Triant, Virginia

Importance The Human Immunodeficiency Virus (HIV) epidemic has evolved, with an increasing non-communicable disease (NCD) burden emerging and need for long-term management, yet there are limited data to help delineate the optimal care model to screen for NCDs for this patient population. Objective: The primary aim was to compare rates of NCD preventive screening in persons living with HIV/AIDS (PLWHA) by type of HIV care model, focusing on metabolic/cardiovascular disease (CVD) and cancer screening. We hypothesized that primary care models that included generalists would have higher preventive screening rates. Design: Prospective observational cohort study. Setting: Partners HealthCare System (PHS) encompassing Brigham & Women’s Hospital, Massachusetts General Hospital, and affiliated community health centers. Participants: PLWHA age >18 engaged in active primary care at PHS. Exposure HIV care model categorized as infectious disease (ID) providers only, generalist providers only, or ID plus generalist providers. Main Outcome(s) and Measures(s) Odds of screening for metabolic/CVD outcomes including hypertension (HTN), obesity, hyperlipidemia (HL), and diabetes (DM) and cancer including colorectal cancer (CRC), cervical cancer, and breast cancer. Results: In a cohort of 1565 PLWHA, distribution by HIV care model was 875 ID (56%), 90 generalists (6%), and 600 ID plus generalists (38%). Patients in the generalist group had lower odds of viral suppression but similar CD4 counts and ART exposure as compared with ID and ID plus generalist groups. In analyses adjusting for sociodemographic and clinical covariates and clustering within provider, there were no significant differences in metabolic/CVD or cancer screening rates among the three HIV care models. Conclusions: There were no notable differences in metabolic/CVD or cancer screening rates by HIV care model after adjusting for sociodemographic and clinical factors. These findings suggest that HIV patients receive similar preventive health care for NCDs independent of HIV care model.

Objectives: HIV-infected patients are at increased risk for cardiovascular disease (CVD). However, general population CVD risk prediction equations that identify HIV-infected patients at elevated risk have not been widely assessed in sub-Saharan African (SSA). Methods: HIV-infected adults from 30–50 years of age with documented viral suppression were enrolled into a cross-sectional study in Gaborone, Botswana. Participants were screened for CVD risk factors. Bilateral carotid intima-media thickness (cIMT) was measured and 10-year predicted risk of cardiovascular disease was calculated using the Pooled Cohorts Equation for atherosclerotic CVD (ASCVD) and the 2008 Framingham Risk Score (FRS) (National Cholesterol Education Program III–NCEP III). ASCVD ≥7.5%, FRS ≥10%, and cIMT≥75th percentile were considered elevated risk for CVD. Agreement in classification of participants as high-risk for CVD by cIMT and FRS or ASCVD risk score was assessed using McNemar`s Test. The optimal cIMT cut off-point that matched ASCVD predicted risk of ≥7.5% was assessed using Youden’s J index. Results: Among 208 HIV-infected patients (female: 55%, mean age 38 years), 78 (38%) met criteria for ASCVD calculation versus 130 (62%) who did not meet the criteria. ASCVD classified more participants as having elevated CVD risk than FRS (14.1% versus 2.6%, McNemar’s exact test p = 0.01), while also classifying similar proportion of participants as having elevated CVD like cIMT (14.1% versus 19.2%, McNemar’s exact test p = 0.34). Youden’s J calculated the optimal cut point at the 81st percentile for cIMT to correspond to an ASCVD score ≥7.5% (sensitivity = 72.7% and specificity = 88.1% with area under the curve for the receiver operating characteristic [AUC] of 0.82, 95% Mann-Whitney CI: 0.66–0.99). Conclusion: While the ASCVD risk score classified more patients at elevated CVD risk than FRS, ASCVD score classified similar proportion of patients as high risk when compared with established subclinical atherosclerosis. However, potential CVD risk category misclassification by established equations such as ASCVD may still exist among HIV-infected patients; hence there is still a need for development of a CVD risk prediction equation tailored to HIV-infected patients in SSA.

Background: Cigarette smoking is widespread among HIV-infected patients, who confront increased risk of smoking-related co-morbidities. The effects of HIV infection and HIV-related variables on smoking and smoking cessation are incompletely understood. We investigated the correlates of smoking and quitting in an HIV-infected cohort using a validated natural language processor to determine smoking status. Method We developed and validated an algorithm using natural language processing (NLP) to ascertain smoking status from electronic health record data. The algorithm was applied to records for a cohort of 3487 HIV-infected from a large health care system in Boston, USA, and 9446 uninfected control patients matched 3:1 on age, gender, race and clinical encounters. NLP was used to identify and classify smoking-related portions of free-text notes. These classifications were combined into patient-year smoking status and used to classify patients as ever versus never smokers and current smokers versus non-smokers. Generalized linear models were used to assess associations of HIV with 3 outcomes, ever smoking, current smoking, and current smoking in analyses limited to ever smokers (persistent smoking), while adjusting for demographics, cardiovascular risk factors, and psychiatric illness. Analyses were repeated within the HIV cohort, with the addition of CD4 cell count and HIV viral load to assess associations of these HIV-related factors with the smoking outcomes. Results: Using the natural language processing algorithm to assign annual smoking status yielded sensitivity of 92.4, specificity of 86.2, and AUC of 0.89 (95% confidence interval [CI] 0.88–0.91). Ever and current smoking were more common in HIV-infected patients than controls (54% vs. 44% and 42% vs. 30%, respectively, both P<0.001). In multivariate models HIV was independently associated with ever smoking (adjusted rate ratio [ARR] 1.18, 95% CI 1.13–1.24, P <0.001), current smoking (ARR 1.33, 95% CI 1.25–1.40, P<0.001), and persistent smoking (ARR 1.11, 95% CI 1.07–1.15, P<0.001). Within the HIV cohort, having a detectable HIV RNA was significantly associated with all three smoking outcomes. Conclusions: HIV was independently associated with both smoking and not quitting smoking, using a novel algorithm to ascertain smoking status from electronic health record data and accounting for multiple confounding clinical factors. Further research is needed to identify HIV-related barriers to smoking cessation and develop aggressive interventions specific to HIV-infected patients.