Identifying targets for cardiovascular medication adherence interventions through latent class analysis

Use of a Smartphone Application to Promote Adherence to Oral Medications in Patients With Breast Cancer

PURPOSE

Medication nonadherence is common among patients with breast cancer (BC) and increases BC mortality and complications from comorbidities. There is growing interest in mobile health interventions such as smartphone applications (apps) to promote adherence.

METHODS

Use of Medisafe, a medication reminder and tracking app, was tested over 12 weeks among patients on BC treatment and at least one oral medication. Study participants were instructed to generate adherence reports every 4 weeks through Medisafe and were deemed to have completed the intervention if >50% of reports were generated. The primary end point was feasibility of the intervention, defined as a completion rate of ≥75% of consented patients. Secondary end points included changes in self-reported nonadherence from baseline to 12 weeks and patient-reported outcomes including reasons for nonadherence and satisfaction with Medisafe. We conducted univariable and multivariable analyses to evaluate demographic and clinical factors associated with intervention completion.

RESULTS

Among 100 patients enrolled, 78 (78.0%) completed the intervention. Age, race, ethnicity, clinical stage, and type of medication were not associated with odds of intervention completion. Self-reported nonadherence rates did not improve from baseline to postintervention in the overall study population. However, among patients with self-reported nonadherence at baseline, 26.3% reported adherence postintervention; these patients frequently reported logistical barriers to adherence. Study participants reported high levels of satisfaction with Medisafe, noting that the app was highly functional and provided high-quality information.

CONCLUSION

Smartphone apps such as Medisafe are feasible and associated with high patient satisfaction. They may improve adherence in nonadherent patients and those who face logistical challenges interfering with medication-taking. Future trials of mobile health interventions should target patients at high risk for medication nonadherence.

Psychosocial profiles and longitudinal achievement of optimal cardiovascular risk factor levels: the Coronary Artery Risk Development in Young Adults (CARDIA) study

Psychosocial factors are associated with the achievement of optimal cardiovascular disease risk factor (CVDRF) levels. To date, little research has examined multiple psychosocial factors simultaneously to identify distinguishing psychosocial profiles among individuals with CVDRF. Further, it is unknown whether profiles are associated with achievement of CVDRF levels longitudinally. Therefore, we characterized psychosocial profiles of individuals with CVDRF and assessed whether they are associated with achievement of optimal CVDRF levels over 15 years. We included 1148 CARDIA participants with prevalent hypertension, hypercholesterolemia and/or diabetes mellitus in 2000-2001. Eleven psychosocial variables reflecting psychological health, personality traits, and social factors were included. Optimal levels were deemed achieved if: Hemoglobin A1c (HbA1c) < 7.0%, low-density lipoprotein (LDL) cholesterol < 100 mg/dl, and systolic blood pressure (SBP) < 140 mm Hg. Latent profile analysis revealed three psychosocial profile groups "Healthy", "Distressed and Disadvantaged" and "Discriminated Against". There were no significant differences in achievement of CVDRF levels of the 3 targets combined across profiles. Participants in the "Distressed and Disadvantaged" profile were less likely to meet optimal HbA1c levels compared to individuals in the "Healthy" profile after demographic adjustment. Associations were attenuated after full covariate adjustment. Distinct psychosocial profiles exist among individuals with CVDRF, representing meaningful differences. Implications for CVDRF management are discussed.

Oncologist phenotypes and associations with response to a machine learning-based intervention to increase advance care planning: Secondary analysis of a randomized clinical trial

BACKGROUND

While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer.

METHODS AND FINDINGS

Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention.

CONCLUSIONS

Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients.

Measuring the multiple domains of medication nonadherence: findings from a Delphi survey of adherence experts

Consensus on a gold-standard measure of patient medication nonadherence has been elusive, in part because medication nonadherence involves multiple, distinct behaviors across three phases (initiation, implementation, and persistence). To assess these behaviors, multiple measurement approaches may be needed. The purpose of this study was to identify expert-recommended approaches to measuring nonadherence behaviors. Thirty medication nonadherence experts were e-mailed two consecutive surveys. In both, respondents rated their agreement with definitions of nonadherence behaviors and measurement approaches. In the second survey, respondents rated the suitability of each measurement approach for assessing each behavior and identified the optimal measurement approach for each behavior. Consensus was achieved for eight patient medication nonadherence behaviors: not filling initial prescription and not taking first dose (noninitiation); refilling prescription late, missing doses, taking extra doses, taking doses at wrong time, and improperly administering medication (incorrect implementation); and discontinuing medication early (nonpersistence). Consensus was achieved for seven measurement approaches: self-report, prescription fill data, pill count, drug levels, electronic drug monitoring (EDM), smart technology, and direct observation. Self-report questionnaires were most commonly rated "at least somewhat suitable" for measuring behaviors. EDM was rated as optimal for measuring missing doses, taking extra doses, and taking doses at the wrong time. Prescription fill data were rated as optimal for not filling initial prescription, refilling late, and discontinuing. Direct observation was rated as optimal for measuring improper administration. Suitable and optimal measurement approaches varied across nonadherence behaviors. Researchers should select the measurement approach best suited to assessing the behavior(s) targeted in their research.

Posttraumatic Stress Disorder Following Myocardial Infarction: A Systematic Review

The objective of the present review is to provide an overview of existing research that has reported on the association between posttraumatic stress disorder (PTSD) and ischemic heart disease. Specific focus is given to the incidence of PTSD following myocardial infarction (MI). A systematic review using Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) guidelines was performed by searching four bibliographic databases: PubMed, PsychINFO, ScienceDirect, and ProQuest Dissertations and Theses. A total of 39 articles were included in this literature review. The results of these studies suggest that the occurrence of an acute cardiac event is likely to contribute to the development of PTSD. Not only is this type of psychiatric disorder associated with significant suffering and impaired quality of life, but it is also a predictor of an increased risk of recurrent adverse cardiovascular events and mortality. Screening, assessment, and treatment of PTSD and posttraumatic stress symptoms following a major cardiac event are critical for offsetting potential deleterious psychological and physical consequences.

Intervening on fear after acute cardiac events: Rationale and design of the INFORM randomized clinical trial

OBJECTIVE

Many acute coronary syndrome (ACS) patients are nonadherent to cardiovascular medications despite their known benefits for lowering risk of recurrent cardiovascular events. Research suggests that greater cardiac-related fear of recurrence (FoR) may be associated with higher nonadherence to cardiovascular medications and avoidance of physical activity. We aim to test the effect of an intervention that targets FoR as a potentially modifiable mechanism underlying nonadherence to recommended health behaviors among patients with suspected ACS.

METHOD

The INFORM trial ("INvestigating Fear Of Recurrence as a modifiable Mechanism of behavior change to improve medication adherence in acute coronary syndrome patients") is a double-blind, parallel-group randomized clinical trial. It compares an 8-session, at-home, electronic tablet-delivered, cognitive bias modification training (CBMT) intervention with a sham control. Patients who experience high perceived threat at the time of presentation to the emergency department (ED) with a suspected ACS are enrolled and randomized within 6 weeks of their ED visit. The primary outcome, FoR, is measured by the adapted Concerns about Recurrent ACS Scale. The trial also tests the intervention's effect on a potential mechanism of health behavior change that is inversely correlated with fear: an expansive future time perspective. Additional outcomes include electronically measured adherence to a cardiovascular medication and self-reported physical activity.

CONCLUSIONS

This study takes a mechanistic approach to addressing the dangerous problem of poor health behaviors after ACS. The trial will test whether targeting FoR or future time perspective by CBMT is a promising approach to improving nonadherence after ACS. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Phenotyping physician practice patterns and associations with response to a nudge in the electronic health record for influenza vaccination: A quasi-experimental study

BACKGROUND

Health systems routinely implement changes to the design of electronic health records (EHRs). Physician behavior may vary in response and methods to identify this variation could help to inform future interventions. The objective of this study was to phenotype primary care physician practice patterns and evaluate associations with response to an EHR nudge for influenza vaccination.

METHODS AND FINDINGS

During the 2016-2017 influenza season, 3 primary care practices at Penn Medicine implemented an active choice intervention in the EHR that prompted medical assistants to template influenza vaccination orders for physicians to review during the visit. We used latent class analysis to identify physician phenotypes based on 9 demographic, training, and practice pattern variables, which were obtained from the EHR and publicly available sources. A quasi-experimental approach was used to evaluate response to the intervention relative to control practices over time in each of the physician phenotype groups. For each physician latent class, a generalized linear model with logit link was fit to the binary outcome of influenza vaccination at the patient visit level. The sample comprised 45,410 patients with a mean (SD) age of 58.7 (16.3) years, 67.1% were white, and 22.1% were black. The sample comprised 56 physicians with mean (SD) of 24.6 (10.2) years of experience and 53.6% were male. The model segmented physicians into groups that had higher (n = 41) and lower (n = 15) clinical workloads. Physicians in the higher clinical workload group had a mean (SD) of 818.8 (429.1) patient encounters, 11.6 (4.7) patient appointments per day, and 4.0 (1.1) days per week in clinic. Physicians in the lower clinical workload group had a mean (SD) of 343.7 (129.0) patient encounters, 8.0 (2.8) patient appointments per day, and 3.1 (1.2) days per week in clinic. Among the higher clinical workload group, the EHR nudge was associated with a significant increase in influenza vaccination (adjusted difference-in-difference in percentage points, 7.9; 95% CI, 0.4-9.0; P = .01). Among the lower clinical workload group, the EHR nudge was not associated with a significant difference in influenza vaccination rates (adjusted difference-in-difference in percentage points, -1.0; 95% CI, -5.3-5.8; P = .90).

CONCLUSIONS

A model-based approach categorized physician practice patterns into higher and lower clinical workload groups. The higher clinical workload group was associated with a significant response to an EHR nudge for influenza vaccination.

Content Validity and Reliability of a Self-Report Measure of Medication Nonadherence in Hepatitis C Treatment

BACKGROUND

Nonadherence to direct-acting agents (DAAs) for hepatitis C (HCV) decreases viral response. To measure nonadherence to DAAs, a reliable, valid, and easily implemented method is needed.

AIMS

The goals of this study were to refine a previously validated (in patients with hypertension) self-report measure of extent of nonadherence and reasons for nonadherence in the context of DAAs and to obtain initial evidence of content validity and reliability.

METHODS

Phase I involved two focus groups with patients with HCV (n = 12) and one focus group with prescribers of HCV medications (n = 6) to establish content validity of reasons for nonadherence. Subsequent cognitive interviews with patients (n = 11) were conducted to refine items. Phase II was a prospective cohort study involving weekly administration of the refined measure by telephone to patients (n = 75) who are prescribed DAAs to evaluate reliability and consistency with viral response.

RESULTS

In the cohort study, internal consistency ranged from acceptable (α = .69) to very high (α = 1.00) across time points and was quite high on average (α = .91). Across the 75 participants, there were 895 measurement occasions; of those, nonadherence was reported on only 27 occasions (3%), all of which occurred in the first 12 weeks. These 27 occasions represented 19 (26%) different individuals. At 12 weeks, 1 (1%) of patients had a detectable HCV viral load; at 12-24 weeks posttreatment, 4 (5%) had a sustained viral response. Nonadherent patients reported an average of 1.41 reasons for nonadherence.

CONCLUSIONS

This multi-method study established content validity of reasons for nonadherence and reliability of extent of nonadherence. High rates of adherence and viral response were consistent with previous studies using other nonadherence measurement methods.