CROI 2017 Abstract e-Book

Abstract eBook

Poster and Themed Discussion Abstracts

meeting care constancy [93% (CI: 92–94) vs. 80% (CI: 76–85)]. After adjusting for characteristics that may confound the association between care constancy and viral suppression, the prevalence ratio of viral suppression among those who did vs. did not meet care constancy was 1.24 (CI: 1.19–1.29) among patients with a lower first CD4 count and 1.15 (CI: 1.10–1.21) among those with a higher first CD4 count. Conclusion: Only half of patients in HIV care met the care constancy definition over 24 months. It may be particularly important for persons with lower CD4 counts to receive laboratory tests at recommended intervals, as they have a considerably lower prevalence of viral suppression when care constancy is not met. 908 BEYOND THE STATIC HIV CONTINUUM: CAPTURING THE DYNAMIC PROCESS OF RETENTION Hana Lee 1 , Xiaotian Wu 1 , Michael J. Mugavero 2 , Stephen R. Cole 3 , Bryan Lau 4 , Becky L. Genberg 1 , Joseph Hogan 1 1 Brown Univ, Providence, RI, USA, 2 Univ of Alabama at Birmingham, Birmingham, AL, USA, 3 Univ of North Carolina at Chapel Hill, Chapel Hill, NC, USA, 4 The Johns Hopkins Univ, Baltimore, MD, USA Background: Retention in care is essential to maximizing antiretroviral therapy coverage and compliance and optimizing patient outcomes. Researchers often rely on cross- sectional snapshots of binary patient status to estimate retention rates and to identify predictors of attrition. This approach provides only a macro-level view, and does not make full use of longitudinal patient information that may describe cyclical processes of engagement, disengagement, and re-entry into care spanning the care continuum. We characterized the longitudinal dynamics and estimated effects of patient-level covariates related to retention in care. Methods: We represent the process of engagement and retention in care using four states: Engaged in care, disengaged from care, lost from care (LFC), and deceased. Then various patient behaviors are described in terms of transition from one state to another such as transition from engaged to engaged (retention), engaged to disengaged, continued disengaged, disengaged to engaged (re-entry into care), disengaged to LFC, and mortality. The state space modeling (SSM) approach for longitudinal data was used to identify barriers of retention and sub-groups at higher risk of cycling out from care. This analysis includes data from the CFAR Network of Integrated Clinical Systems (CNICS), including 31,376 patients who enrolled and followed from 8 different sites in the US between 1996 and 2015. Results: Following enrollment, we ascertained patient state membership every 200 days to reflect CNICS patient monitoring guidelines. Among engaged patients, probability of retention, disengagement, and death are 86%, 13%, and 1%. Once disengaged, probability of return to care, continued disengagement, LFC, and death are 24%, 58%, 16%, and 2%. The SSM identified some important prognostic factors of retention and other transition dynamics, after controlling for variation due to site and cohort entry year (see Table). In particular, patients with lower CD4 counts, higher viral load, and not on ARV have lower retention rates. Heterosexual males have lower retention rates compared to men who have sex with men. In addition, gender, race/ethnicity, age, and AIDS are associated with disengagement and/or LFC. Conclusion: Beyond binary retention status, more comprehensive longitudinal patient behaviors uncover dynamic patterns of care engagement. Our findings can be used for policy, clinical, and programmatic purposes to enhance retention in care among those at greatest risk.

Poster and Themed Discussion Abstracts

909 ASSOCIATION BETWEEN 4 MEASURES OF RETENTION IN CARE AND VIROLOGIC FAILURE Mariah M. Kalmin , Amanda D. Castel, Alan Greenberg, Heather Young, Heather Hoffman, for the DC Cohort Executive Committee George Washington Univ, Washington, DC, USA

Background: The relationship between retention in care (RIC) and achievement of viral suppression (VS) varies depending on the method used to measure retention. Furthermore, PLWH often cycle between VS and virologic failure (VF). We sought to apply different RIC measures to assess the relationship with VF as a recurrent event. Methods: Data used were collected by the DC Cohort study, a longitudinal, observational study of HIV-infected patients receiving care at 13 clinics in Washington, DC. Patients ≥18 yrs enrolled between Jan 2011 and Sep 2014, on antiretroviral therapy, with an undetectable viral load at consent and with at least 12 months of study follow-up were included. RIC measures included no 6-month gaps in care, 4-month visit constancy, the Institute of Medicine (IOM) measure defined as >2 visits at least 90 days apart in a 12-month period, and the Health and Human Services (HHS) measure defined as >1 visit in each 6-month interval in 24-months with >60 days between visits. VF (viral load >200 copies/ml) was modeled as a recurrent event in order to describe the cyclical nature of VF followed by VS before again experiencing VF. Cox proportional hazards regression was used to evaluate the relationship between retention and time to VF. Results: Among 1,958 participants, the median percent of follow-up time in which participants met each RIC measure was 50% for the HHS measure, 67% for the 4-month visit constancy and IOMmeasures, and 100% for the no 6-month gaps in care measure. VF was experienced by 18.8% of participants with 4% achieving VS before experiencing at least one subsequent VF event. During the first 2 yrs of follow up, an increased percentage of time not spent in a gap in care >6 months was associated with an increased rate of VF (aHR: 1.24, 95% CI: 1.06-1.45). During the last 2 yrs of follow-up, an increased percentage of time in which the IOMmeasure was met was associated with a decreased rate of VF (aHR: 0.74, 95% CI: 0.62-0.89). Other RIC measures were not associated with time to VF. Conclusion: Early in follow up, meeting the no 6-month gap in care measure significantly increased the rate of VF; however, after 2 yrs, the rate of VF declined when the more stringent IOMmeasure was met, while other RIC measures did not impact time to VF. Our results suggest that the clinical implications of monitoring patient care and its effect on VF may vary depending on how retention is defined. Further follow up is needed to determine whether these patterns are maintained over time.

CROI 2017 394