CONFERENCE ON RETROVIRUSES
AND OPPORTUNISTIC INFECTIONS

Boston, Massachusetts
March 8–11, 2020

 

Conference Dates and Location: 
March 4–7, 2018 | Boston, Massachusetts
Abstract Number: 
1119

IS CLINICAL STABILITY STABLE? MULTI-STATE SURVIVAL ANALYSIS OF HIV PATIENTS IN ZAMBIA

Author(s): 

Monika Roy1, Charles B. Holmes2, Izukanji Sikazwe3, Theodora Savory3, Mwanza wa Mwanza3, Carolyn Bolton Moore3, Nancy Czaicki1, David Glidden1, Nancy Padian4, Elvin Geng1

1University of California San Francisco, San Francisco, CA, USA,2Johns Hopkins University, Baltimore, MD, USA,3Centre for Infectious Disease Research in Zambia, Lusaka, Zambia,4University of California Berkeley, Berkeley, CA, USA

Abstract Body: 

Differentiated service delivery (DSD) models increase the efficiency of HIV services by de-intensifying contact with the health system for clinically stable patients. Although a large proportion of patients may be clinically stable at any given time, the durability of clinical stability under routine care will influence the potential Impact of these models. We used a multi-state survival analysis to describe the rate of becoming stable on treatment after enrollment in care and the dynamics of becoming unstable to assess the durability of clinical stability.

We evaluated visit data in a cohort of HIV-infected adults who made at least one visit between March 1, 2013 and February 28, 2015 at 56 clinics in Zambia. Clinical, laboratory, and visit data were collected from an existing electronic medical record system. Definition of stability was based on Zambian guidelines and WHO criteria for stability in the absence of viral load. We developed a 6-state model: States 1: never stable on ART; 2: lapse in care before stable on ART; 3: stable on ART; 4: previously stable on ART; 5: lapse in care after stable on ART; 6: death. Cumulative incidence and Incidence rates for transitions between states (from time from enrollment) were calculated overall and by gender, age, and time.

Among 160,487 patients, cumulative incidence of stability on ART was 39.6% (95% CI: 39.3-39.8) 12 months post-enrollment. However, among those who had achieved stability, only 39% were still stable on ART, 54% had already become unstable, and 7.8% had lapsed in care at 12 months. Once stable, the rate of becoming unstable was highest in the first two years post-enrollment (45 and 37 events per 100 person years (pyrs) in Years 1 and 2 respectively) but remained greater than 20 events per 100pyrs thereafter. Rates for lapse in care after being stable on ART were similar regardless of gender, age, or time period, ranging from 45 to 58 events per 100 pyrs. Rate of lapse in care was greater before becoming stable on ART compared to after becoming stable on ART (118 vs 51 events per 100pyrs).

Although most patients became clinically stable shortly after enrollment, many stable patients subsequently became unstable or experienced lapses in care. DSD models targeting stable patients need to account for transient clinical stability among enrollees. Robust systems to detect and react to clinical instability (including viral load testing) will strengthen DSD models.

Session Number: 
P-V4
Session Title: 
ADHERENCE AND TREATMENT OUTCOMES
Presenting Author: 
Monika Roy
Presenter Institution: 
UCSF