Predicting short-term interruptions of antiretroviral therapy from summary adherence data: Development and test of a probability model

Development and Validation of a Risk Score for Predicting Non-Adherence to Antiretroviral Therapy

Several patient-related factors that influence adherence to antiretroviral therapy (ART) have been described. However, studies that propose a practical and simple tool to predict nonadherence after ART initiation are still scarce. In this study, we develop and validate a score to predict the risk of nonadherence in people starting ART. The model/score was developed and validated using a cohort of people living with HIV starting ART at the Hospital del Mar, Barcelona, between 2012 and 2015 (derivation cohort) and between 2016 and 2018 (validation cohort),. Adherence was evaluated every 2 months using both pharmacy refills and patient self-reports. Nonadherence was defined as taking <90% of the prescribed dose and/or ART interruption for more than 1 week. Predictive factors for nonadherence were identified by logistic regression. Beta coefficients were used to develop a predictive score. Optimal cutoffs were identified using the bootstrapping methodology, and performance was evaluated with the C statistic. Our study is based on 574 patients: 349 in the derivation cohort and 225 in the validation cohort. A total of 104 patients (29.8%) of the derivation cohort were nonadherent. Nonadherence predictors were patient prejudgment; previous medical appointment failures; cultural and/or idiomatic barriers; heavy alcohol use; substance abuse; unstable housing; and severe mental illness. The cutoff point (receiver operating characteristic curve) for nonadherence was 26.3 (sensitivity 0.87 and specificity 0.86). The C statistic (95% confidence interval) was 0.91 (0.87-0.94). These results were consistent with those predicted by the score in the validation cohort. This easy-to-use, highly sensitive, and specific tool could be easily used to identify patients at highest risk for nonadherence, thus allowing resource optimization and achieving optimal treatment goals.

Influence of polypharmacy in the simultaneous presence of high pharmacotherapeutic complexity, drug interactions and non-adherence to medication in patients with HIV infection. Project 3-HIT

INTRODUCTION

The high pharmacotherapeutic complexity, drug interactions and lack of adherence to concomitant medication are circumstances with negative consequences in the clinical evolution of patients with HIV infection. The 3-HIT phenomenon refers to the simultaneous occurrence of these situations. The objective of the study is to determine the prevalence of the phenomenon 3-HIT in the polymedicated HIV population as well as to determine factors related to its occurrence.

METHODS

Observational, retrospective and single-center study that included all elderly patients on active antiretroviral treatment in pharmacotherapeutic follow-up between January and March 2020. A logistic regression model was carried out to evaluate the factors associated with the occurrence of the 3-HIT concept with the variables significantly associated with this phenomenon and those considered clinically relevant.

RESULTS

428 patients were included, registering a prevalence of polypharmacy in 25.9% of the study sample. The 3-HIT phenomenon was detected in 6.3% of the patients. For each concomitant drug prescribed the risk of developing the phenomenon 3-HIT increases 1.5 times.

CONCLUSION

Prevalence of the phenomenon 3-HIT is high in HIV patients with polymedication. A change in the pharmaceutical care model to a multidimensional setting is essential, together with pharmacotherapeutic optimization strategies to improve patient health outcomes.

Measuring patient engagement with HIV care in sub-Saharan Africa: a scoping study

INTRODUCTION

Engagement with HIV care is a multi-dimensional, dynamic process, critical to maintaining successful treatment outcomes. However, measures of engagement are not standardized nor comprehensive. This undermines our understanding of the scope of challenges with engagement and whether interventions have an impact, complicating patient and programme-level decision-making. This study identified and characterized measures of engagement to support more consistent and comprehensive evaluation.

METHODS

We conducted a scoping study to systematically categorize measures the health system could use to evaluate engagement with HIV care for those on antiretroviral treatment. Key terms were used to search literature databases (Embase, PsychINFO, Ovid Global-Health, PubMed, Scopus, CINAHL, Cochrane and the World Health Organization Index Medicus), Google Scholar and stakeholder-identified manuscripts, ultimately including English evidence published from sub-Saharan Africa from 2014 to 2021. Measures were extracted, organized, then reviewed with key stakeholders.

RESULTS AND DISCUSSION

We screened 14,885 titles/abstracts, included 118 full-texts and identified 110 measures of engagement, categorized into three engagement dimensions ("retention," "adherence" and "active self-management"), a combination category ("multi-dimensional engagement") and "treatment outcomes" category (e.g. viral load as an end-result reflecting that engagement occurred). Retention reflected status in care, continuity of attendance and visit timing. Adherence was assessed by a variety of measures categorized into primary (prescription not filled) and secondary measures (medication not taken as directed). Active self-management reflected involvement in care and self-management. Three overarching use cases were identified: research to make recommendations, routine monitoring for quality improvement and strategic decision-making and assessment of individual patients.

CONCLUSIONS

Heterogeneity in conceptualizing engagement with HIV care is reflected by the broad range of measures identified and the lack of consensus on "gold-standard" indicators. This review organized metrics into five categories based on the dimensions of engagement; further work could identify a standardized, minimum set of measures useful for comprehensive evaluation of engagement for different use cases. In the interim, measurement of engagement could be advanced through the assessment of multiple categories for a more thorough evaluation, conducting sensitivity analyses with commonly used measures for more comparable outputs and using longitudinal measures to evaluate engagement patterns. This could improve research, programme evaluation and nuanced assessment of individual patient engagement in HIV care.

Measuring patient engagement with HIV care in sub-Saharan Africa: a scoping study

INTRODUCTION

Engagement with HIV care is a multi-dimensional, dynamic process, critical to maintaining successful treatment outcomes. However, measures of engagement are not standardized nor comprehensive. This undermines our understanding of the scope of challenges with engagement and whether interventions have an impact, complicating patient and programme-level decision-making. This study identified and characterized measures of engagement to support more consistent and comprehensive evaluation.

METHODS

We conducted a scoping study to systematically categorize measures the health system could use to evaluate engagement with HIV care for those on antiretroviral treatment. Key terms were used to search literature databases (Embase, PsychINFO, Ovid Global-Health, PubMed, Scopus, CINAHL, Cochrane and the World Health Organization Index Medicus), Google Scholar and stakeholder-identified manuscripts, ultimately including English evidence published from sub-Saharan Africa from 2014 to 2021. Measures were extracted, organized, then reviewed with key stakeholders.

RESULTS AND DISCUSSION

We screened 14,885 titles/abstracts, included 118 full-texts and identified 110 measures of engagement, categorized into three engagement dimensions ("retention," "adherence" and "active self-management"), a combination category ("multi-dimensional engagement") and "treatment outcomes" category (e.g. viral load as an end-result reflecting that engagement occurred). Retention reflected status in care, continuity of attendance and visit timing. Adherence was assessed by a variety of measures categorized into primary (prescription not filled) and secondary measures (medication not taken as directed). Active self-management reflected involvement in care and self-management. Three overarching use cases were identified: research to make recommendations, routine monitoring for quality improvement and strategic decision-making and assessment of individual patients.

CONCLUSIONS

Heterogeneity in conceptualizing engagement with HIV care is reflected by the broad range of measures identified and the lack of consensus on "gold-standard" indicators. This review organized metrics into five categories based on the dimensions of engagement; further work could identify a standardized, minimum set of measures useful for comprehensive evaluation of engagement for different use cases. In the interim, measurement of engagement could be advanced through the assessment of multiple categories for a more thorough evaluation, conducting sensitivity analyses with commonly used measures for more comparable outputs and using longitudinal measures to evaluate engagement patterns. This could improve research, programme evaluation and nuanced assessment of individual patient engagement in HIV care.

Forgiveness of Dolutegravir-Based Triple Therapy Compared With Older Antiretroviral Regimens: A Prospective Multicenter Cohort of Adherence Patterns and HIV-RNA Replication

Background

For many people with HIV (PWH), taking antiretroviral therapy (ARV) every day is difficult.

Methods

Average adherence (Av-Adh) and log-transformed treatment interruption (TI) to ARV were prospectively measured over 6 months using electronic drug monitoring (EDM) in several cohorts of PWH. Multivariate linear regression models including baseline confounders explored the influence of EDM-defined adherence (R ²) on 6-month log₁₀ HIV-RNA. Multivariate logistic regression models were used to compare the risk of HIV-RNA detection (VR) within subgroups stratified by lower (≤95%) and higher (>95%) Av-Adh.

Results

Three hundred ninety-nine PWH were analyzed with different ARVs: dolutegravir (n = 102), raltegravir (n = 90), boosted PI (bPI; n = 107), and NNRTI (n = 100). In the dolutegravir group, the influence of adherence pattern measures on R ² for HIV-RNA levels was marginal (+2%). Av-Adh, TI, and Av-Adh × TI increased the R ² for HIV-RNA levels by 54% and 40% in the raltegravir and bPI treatment groups, respectively. TI increased the R ² for HIV-RNA levels by 36% in the NNRTI treatment group. Compared with the dolutegravir-based regimen, the risk of VR was significantly increased for raltegravir (adjusted odds ratio [aOR], 45.6; 95% CI, 4.5-462.1; P = .001), NNRTIs (aOR, 24.8; 95% CI, 2.7-228.4; P = .005), and bPIs (aOR, 28.3; 95% CI, 3.4-239.4; P = .002) in PWH with Av-Adh ≤95%. Among PWH with >95% Av-Adh, there were no significant differences in the risk of VR among the different ARVs.

Conclusions

These findings support the concept that dolutegravir in combination with 2 other active ARVs achieves greater virological suppression than older ARVs, including raltegravir, NNRTI, and bPI, among PWH with lower adherence.

Patterns of non-adherence to oral antiretroviral medication: frequencies of consecutively missed doses

PURPOSE

The therapeutic effect of a once-daily oral drug will be maintained if there are no occurrences of consecutively missed doses that exceed the duration of the drug's effect. The durations of effect of antiretroviral drugs are typically in the range of 1-4 days. Here, we report the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses for patients taking a once-daily oral antiretroviral drug for HIV infection.

PATIENTS AND METHODS

Medication Event Monitoring System (MEMS) data were extracted from an electronic database of MEMS records, for a 30-day period for 555 patients taking once-daily oral HIV drug therapy. We recorded the number of days with missed doses and occurrences of ≥2, ≥3, or ≥4 consecutively missed doses. Distributions of the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses as a proportion of number of missed doses were compared to calculated random distributions using the Wilcoxon signed-rank test.

RESULTS

The frequencies of 0, 1, and ≥2 missed daily doses were 0.279, 0.312, and 0.409, respectively. The frequencies of ≥2, ≥3, and ≥4 consecutively missed doses were 0.184, 0.110, and 0.065, respectively. The probabilities that the observed frequencies of ≥2, ≥3, and ≥4 consecutively missed doses were as expected from random chance were P=0.345, P<0.01, and P<0.01, respectively.

CONCLUSION

Observed runs of ≥3 and ≥4 consecutively missed doses - and hence loss of therapeutic effect for drugs of duration of action of <3 and <4 days, respectively - occurred more frequently than expected if missed doses were randomly distributed.