Development and validation of a multivariable prediction model for missed HIV health care provider visits in a large US clinical cohort

Background: Identifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual level. Methods: We developed prediction models for missed visits among people with HIV (PWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010 to 2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with the highest area under the curve (AUC) were identified. Results: Data from 382 432 visits among 20 807 PWH followed for a median of 3.8 years were included; the median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76, and the strongest predictors were at the individual level (prior visit adherence, age, CD4+ count) and community level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of .700. Conclusions: Prediction models validated using multilevel data had a similar AUC to previous models developed using only individual-level data. The strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PWH with previous missed visits should be prioritized by interventions to improve visit adherence

Assessing effects of behavioral intervention on treatment outcomes among patients initiating HIV care: Rationale and design of iENGAGE intervention trial

During the initial year of HIV diagnosis, while patients are often overwhelmed adjusting to this life changing diagnosis, they must develop self-care behaviors for attending regular medical care visits and antiretroviral therapy (ART) adherence to achieve and sustain viral suppression (VS). Maintaining “HIV adherence” and integrating it into one's daily life is required to sustain VS over time. The HIV care continuum or “treatment cascade,” an epidemiological snapshot of the national epidemic in the United States (US), indicates that a minority of persons living with HIV (PLWH) have achieved VS. Little evidence exists regarding the effects of interventions focusing on PLWH newly initiating outpatient HIV care. An intervention that focuses on both retention in care and ART adherence skills delivered during the pivotal first year of HIV care is lacking. To address this, we developed a theory-based intervention evaluated in the Integrating Engagement and Adherence Goals upon Entry (iENGAGE) study, a National Institute of Allergy and Infectious Diseases (NIAID) funded randomized behavioral intervention trial. Here we present the study objectives, design and rationale, as well as the intervention components, targeting rapid and sustained VS through retention in HIV care and ART adherence during participants' first year of HIV care. The primary outcome of the study is 48-week VS (<200 c/mL). The secondary outcomes are retention in care, including HIV visit adherence and visit constancy, as well as ART adherence

Beyond Core Indicators of Retention in HIV Care: Missed Clinic Visits Are Independently Associated With All-Cause Mortality

Background. The continuum of care is at the forefront of the domestic human immunodeficiency virus (HIV) agenda, with the Institute of Medicine (IOM) and Department of Health and Human Services (DHHS) recently releasing clinical core indicators. Core indicators for retention in care are calculated based on attended HIV care clinic visits. Beyond these retention core indicators, we evaluated the additional prognostic value of missed clinic visits for all-cause mortality

Clinic-level factors associated with retention in care among people living with human immunodeficiency virus in a multisite US Cohort, 2010–2016

Background. Retention in care (RIC) leads to reduced HIV transmission and mortality. Few studies have investigated clinic services and RIC among people living with HIV (PLWH) in the United States. We conducted a multisite retrospective cohort study to identify clinic services associated with RIC from 2010–2016 in the United States. Methods. PLWH with ≥1 HIV primary care visit from 2010–2016 at 7 sites in the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) were included. Clinic-level factors evaluated via site survey included patients per provider/ trainee, navigation, RIC posters/brochures, laboratory test timing, flexible scheduling, appointment reminder methods, and stigma support services. RIC was defined as ≥2 encounters per year, ≥90 days apart, observed until death, administrative censoring (31 December 2016), or loss to follow-up (censoring at first 12-month interval without a visit with no future visits). Poisson regression with robust error variance, clustered by site adjusting for calendar year, age, sex, race/ethnicity, and HIV transmission risk factor, estimated risk ratios (RRs) and 95% confidence intervals (CIs) for RIC. Results. Among 21 046 PLWH contributing 103 348 person-years, 67% of person-years were retained. Availability of text appointment reminders (RR, 1.13; 95% CI, 1.03–1.24) and stigma support services (RR, 1.11; 95% CI, 1.04–1.19) were associated with better RIC. Disparities persisted for age, sex, and race. Conclusions. Availability of text appointment reminders and stigma support services was associated with higher rates of RIC, indicating that these may be feasible and effective approaches for improving RIC

Racial and ethnic disparities in coronavirus disease 2019 disease incidence independent of comorbidities, among people with HIV in the United States

Objectives: To define the incidence of clinically detected coronavirus disease 2019 (COVID-19) in people with HIV (PWH) in the United States and evaluate how racial and ethnic disparities, comorbidities, and HIV-related factors contribute to risk of COVID-19. Design: Observational study within the CFAR Network of Integrated Clinical Systems cohort in seven cities during 2020. Methods: We calculated cumulative incidence rates of COVID-19 diagnosis among PWH in routine care by key characteristics including race/ethnicity, current and lowest CD4þ cell count, and geographic area. We evaluated risk factors for COVID-19 among PWH using relative risk regression models adjusted with disease risk scores. Results: Among 16 056 PWH in care, of whom 44.5% were black, 12.5% were Hispanic, with a median age of 52 years (IQR 40 - 59), 18% had a current CD4þ cell count less than 350 cells/ml, including 7% less than 200; 95.5% were on antiretroviral therapy (ART), and 85.6% were virologically suppressed. Overall in 2020, 649 PWH were diagnosed with COVID-19 for a rate of 4.94 cases per 100 person-years. The cumulative incidence of COVID-19 was 2.4-fold and 1.7-fold higher in Hispanic and black PWH respectively, than non-Hispanic white PWH. In adjusted analyses, factors associated with COVID-19 included female sex, Hispanic or black identity, lowest historical CD4þ cell count less than 350 cells/ml (proxy for CD4þ nadir), current low CD4þ : CD8þ ratio, diabetes, and obesity. Conclusion: Our results suggest that the presence of structural racial inequities above and beyond medical comorbidities increased the risk of COVID-19 among PWH. PWH with immune exhaustion as evidenced by lowest historical CD4þ cell count or current low CD4þ : CD8þ ratio had greater risk of COVID-19

Associations between alcohol and cigarette use and type 1 and 2 myocardial infarction among people with HIV

Objectives: People with HIV have a higher risk of myocardial infarction (MI) than the general population, with a greater proportion of type 2 MI (T2MI) due to oxygen demand–supply mismatch compared with type 1 (T1MI) resulting from atherothrombotic plaque disruption. People living with HIV report a greater prevalence of cigarette and alcohol use than do the general population. Alcohol use and smoking as risk factors for MI by type are not well studied among people living with HIV. We examined longitudinal associations between smoking and alcohol use patterns and MI by type among people living with HIV. Design and Methods: Using longitudinal data from the Centers for AIDS Research Network of Integrated Clinical Systems cohort, we conducted time-updated Cox proportional hazards models to determine the impact of smoking and alcohol consumption on adjudicated T1MI and T2MI. Results: Among 13 506 people living with HIV, with a median 4 years of follow-up, we observed 177 T1MI and 141 T2MI. Current smoking was associated with a 60% increase in risk of both T1MI and T2MI. In addition, every cigarette smoked per day was associated with a 4% increase in risk of T1MI, with a suggestive, but not significant, 2% increase for T2MI. Cigarette use had a greater impact on T1MI for men than for women and on T2MI for women than for men. Increasing alcohol use was associated with a lower risk of T1MI but not T2MI. Frequency of heavy episodic alcohol use was not associated with MI. Conclusions: Our findings reinforce the prioritization of smoking reduction, even without cessation, and cessation among people living with HIV for MI prevention and highlight the different impacts on MI type by gender

Factors Associated with Severity of COVID-19 Disease in a Multicenter Cohort of People with HIV in the United States, March-December 2020

Background: Understanding the spectrum of COVID-19 in people with HIV (PWH) is critical to provide clinical guidance and risk reduction strategies.Setting:Centers for AIDS Research Network of Integrated Clinic System, a US multisite clinical cohort of PWH in care.Methods:We identified COVID-19 cases and severity (hospitalization, intensive care, and death) in a large, diverse HIV cohort during March 1, 2020-December 31, 2020. We determined predictors and relative risks of hospitalization among PWH with COVID-19, adjusted for disease risk scores. Results: Of 16,056 PWH in care, 649 were diagnosed with COVID-19 between March and December 2020. Case fatality was 2%; 106 (16.3%) were hospitalized, and 12 died. PWH with current CD4 count <350 cells/mm3[aRR 2.68; 95% confidence interval (CI): 1.93 to 3.71; P < 0.001] or lowest recorded CD4 count <200 cells/mm3(aRR 1.67; 95% CI: 1.18 to 2.36; P < 0.005) had greater risks of hospitalization. HIV viral load and antiretroviral therapy status were not associated with hospitalization, although most of the PWH were suppressed (86%). Black PWH were 51% more likely to be hospitalized with COVID-19 compared with other racial/ethnic groups (aRR 1.51; 95% CI: 1.04 to 2.19; P = 0.03). Chronic kidney disease, chronic obstructive pulmonary disease, diabetes, hypertension, obesity, and increased cardiovascular and hepatic fibrosis risk scores were associated with higher hospitalization risk. PWH who were older, not on antiretroviral therapy, and with current CD4 count <350 cells/mm3, diabetes, and chronic kidney disease were overrepresented among PWH who required intubation or died. Conclusions: PWH with CD4 count <350 cells/mm3, and a history of CD4 count <200 cells/mm3, have a clear excess risk of severe COVID-19, accounting for comorbidities associated with severe outcomes. PWH with these risk factors should be prioritized for COVID-19 vaccination and early treatment and monitored closely for worsening illness

Acceptability of a mobile health intervention to enhance HIV care coordination for patients with substance use disorders

Abstract Background Persons living with HIV and substance use disorders face barriers to sustained engagement in medical care, leading to suboptimal antiretroviral treatment outcomes. Innovative mobile technology tools such as customizable smartphone applications have the potential to enhance existing care coordination programs, but have not been rigorously studied. Methods We developed and implemented a two-component intervention consisting of peer health navigation supported by a smartphone application conducting ecologic momentary assessment (EMA) of barriers to care and medication adherence. Patients with a history of antiretroviral treatment failure and substance use were recruited to participate in the 9-month pilot intervention. Three peer health navigators were trained to provide social and logistical support while participants re-engaged in HIV care. We assessed the acceptability of the intervention components using qualitative analysis of in-depth interviews conducted with study participants and peer navigators. Results Of 19 patients enrolled in the study, 17 participated for at least 2 months and 15 completed the entire 9-month study protocol. The acceptability of the peer navigation intervention was rated favorably by all participants interviewed, who felt that peer support was instrumental in helping them re-engage in HIV care. Participants also responded favorably to the smartphone application, but described its usefulness mostly as providing reminders to take medications and attend appointments, rather than as a facilitator of patient navigation. Conclusions Peer health navigation and smartphone-based EMA are acceptable approaches to facilitating engagement in HIV care for drug using populations. Future studies to evaluate the efficacy of this approach for improving long-term retention in care and antiretroviral treatment outcomes are warranted. ClinicalTrials.gov Identifier NCT01941108; registered on September 4, 201

Development and validation of a multivariable prediction model for missed HIV health care provider visits in a large US clinical cohort

Background: Identifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual level. Methods: We developed prediction models for missed visits among people with HIV (PWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010 to 2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with the highest area under the curve (AUC) were identified. Results: Data from 382 432 visits among 20 807 PWH followed for a median of 3.8 years were included; the median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76, and the strongest predictors were at the individual level (prior visit adherence, age, CD4+ count) and community level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of .700. Conclusions: Prediction models validated using multilevel data had a similar AUC to previous models developed using only individual-level data. The strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PWH with previous missed visits should be prioritized by interventions to improve visit adherence

Who Will Show? Predicting Missed Visits Among Patients in Routine HIV Primary Care in the United States

Missed HIV medical visits predict poor clinical outcomes. We sought to identify patients at high risk of missing visits. We analyzed 2002-2014 data from six large US HIV clinics. At each visit, we predicted the likelihood of missing the next scheduled visit using demographic, clinical, and patient-reported psychosocial variables. Overall, 10,374 participants contributed 105,628 HIV visits. For 17% of visits, the next scheduled appointment was missed. The strongest predictor of a future missed visit was past-year missed visits. A model with only this predictor had area under the receiver operator curve = 0.65; defining "high risk" as those with any past-year missed visits had 73% sensitivity and 51% specificity in correctly identifying a future missed visit. Inclusion of other clinical and psychosocial predictors only slightly improved performance. Past visit attendance can identify those at increased risk for future missed visits, allowing for proactive allocation of resources to those at greatest risk