A Community Participatory Approach to Creating Contextually Tailored mHealth Notifications: myBPmyLife Project

Just-in-time adaptive interventions (JITAIs) are a novel approach to mobile health (mHealth) interventions, sending contextually tailored behavior change notifications to participants when they are more likely to engage, determined by data from wearable devices. We describe a community participatory approach to JITAI notification development for the myBPmyLife Project, a JITAI focused on decreasing sodium consumption and increasing physical activity to reduce blood pressure. Eighty-six participants were interviewed, 50 at a federally qualified health center (FQHC) and 36 at a university clinic. Participants were asked to provide encouraging physical activity and low-sodium diet notifications and provided feedback on researcher-generated notifications to inform revisions. Participant notifications were thematically analyzed using an inductive approach. Participants noted challenging vocabulary, phrasing, and culturally incongruent suggestions in some of the researcher-generated notifications. Community-generated notifications were more direct, used colloquial language, and contained themes of grace. The FQHC participants' notifications expressed more compassion, religiosity, and addressed health-related social needs. University clinic participants' notifications frequently focused on office environments. In summary, our participatory approach to notification development embedded a distinctive community voice within our notifications. Our approach may be generalizable to other communities and serve as a model to create tailored mHealth notifications to their focus population.

Cytomegalovirus infection is associated with impaired myocardial flow reserve after heart transplantation

BACKGROUND

Cardiac allograft vasculopathy (CAV) limits long-term survival after heart transplantation (HT). This study evaluates the relationship between clinically significant cytomegalovirus infection (CS-CMVi) and CAV using cardiac positron emission tomography (PET).

METHODS

We retrospectively evaluated HT patients from 2005 to 2019 who underwent cardiac PET for CAV evaluation. Multivariable linear and logistic regression models were used to evaluate the association between CS-CMVi and myocardial flow reserve (MFR). Kaplan-Meier and Cox regression analyses were used to assess the relationship between CS-CMV, MFR, and clinical outcomes.

RESULTS

Thirty-two (31.1%) of 103 HT patients developed CS-CMVi at a median 9 months after HT. Patients with CS-CMVi had a significantly lower MFR at year 1 and 3, driven by reduction in stress myocardial blood flow. Patients with CS-CMVi had a faster rate of decline in MFR compared to those without infection (-0.10 vs -0.06 per year, p < 0.001). CS-CMVi was an independent predictor of abnormal MFR (<2.0) (odds ratio: 3.8, 95% confidence intervals (CI): 1.4-10.7, p = 0.001) and a lower MFR (β = -0.39, 95% CI: -0.63 to -0.16, p = 0.001) at year 3. In adjusted survival analyses, both abnormal MFR (log-rank p < 0.001; hazard ratio [HR]: 5.7, 95% CI: 4.2-7.2) and CS-CMVi (log-rank p = 0.028; HR: 3.3, 95% CI: 1.8-4.8) were significant predictors of the primary outcome of all-cause mortality, retransplantation, heart failure hospitalization, and acute coronary syndrome.

CONCLUSIONS

CS-CMVi is an independent predictor of reduced MFR following HT. These findings suggest that CMV infection is an important risk factor in the development and progression of CAV.

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation. In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively. The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels.

A Physical Activity and Diet Just-in-Time Adaptive Intervention to Reduce Blood Pressure: The myBPmyLife Study Rationale and Design

BACKGROUND

Smartphone applications and wearable devices are promising mobile health interventions for hypertension self-management. However, most mobile health interventions fail to use contextual data, potentially diminishing their impact. The myBPmyLife Study is a just-in-time adaptive intervention designed to promote personalized self-management for patients with hypertension.

METHODS AND RESULTS

The study is a 6-month prospective, randomized-controlled, remotely administered trial. Participants were recruited from the University of Michigan Health in Ann Arbor, Michigan or the Hamilton Community Health Network, a federally qualified health center network in Flint, Michigan. Participants were randomized to a mobile application with a just-in-time adaptive intervention promoting physical activity and lower-sodium food choices as well as weekly goal setting or usual care. The mobile study application encourages goal attainment through a central visualization displaying participants' progress toward their goals for physical activity and lower-sodium food choices. Participants in both groups are followed for up for 6 months with a primary end point of change in systolic blood pressure. Exploratory analyses will examine the impact of notifications on step count and self-reported lower-sodium food choices. The study launched on December 9, 2021, with 484 participants enrolled as of March 31, 2023. Enrollment of participants was completed on July 3, 2023. After 6 months of follow-up, it is expected that results will be available in the spring of 2024.

CONCLUSIONS

The myBPmyLife study is an innovative mobile health trial designed to evaluate the effects of a just-in-time adaptive intervention focused on improving physical activity and dietary sodium intake on blood pressure in diverse patients with hypertension.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT05154929.

Acceptability of a Text Message-Based Mobile Health Intervention to Promote Physical Activity in Cardiac Rehabilitation Enrollees: A Qualitative Substudy of Participant Perspectives

BACKGROUND

Mobile health (mHealth) interventions have the potential to deliver longitudinal support to users outside of episodic clinical encounters. We performed a qualitative substudy to assess the acceptability of a text message-based mHealth intervention designed to increase and sustain physical activity in cardiac rehabilitation enrollees.

METHODS AND RESULTS

Semistructured interviews were conducted with intervention arm participants of a randomized controlled trial delivered to low- and moderate-risk cardiac rehabilitation enrollees. Interviews explored participants' interaction with the mobile application, reflections on tailored text messages, integration with cardiac rehabilitation, and opportunities for improvement. Transcripts were thematically analyzed using an iteratively developed codebook. Sample size consisted of 17 participants with mean age of 65.7 (SD 8.2) years; 29% were women, 29% had low functional capacity, and 12% were non-White. Four themes emerged from interviews: engagement, health impact, personalization, and future directions. Participants engaged meaningfully with the mHealth intervention, finding it beneficial in promoting increased physical activity. However, participants desired greater personalization to their individual health goals, fitness levels, and real-time environment. Generally, those with lower functional capacity and less experience with exercise were more likely to view the intervention positively. Finally, participants identified future directions for the intervention including better incorporation of exercise physiologists and social support systems.

CONCLUSIONS

Cardiac rehabilitation enrollees viewed a text message-based mHealth intervention favorably, suggesting the potentially high usefulness of mHealth technologies in this population. Addressing participant-identified needs on increased user customization and inclusion of clinical and social support is crucial to enhancing the effectiveness of future mHealth interventions.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

Evaluation of atrial fibrillation using wearable device signals and home blood pressure data in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) Study: A Subgroup Analysis (MIPACT-AFib)

Background

The rising adoption of wearable technology increases the potential to identify arrhythmias. However, specificity of these notifications is poorly defined and may cause anxiety and unnecessary resource utilization. Herein, we report results of a follow-up screening protocol for incident atrial fibrillation/flutter (AF) within a large observational digital health study.

Methods

The MIPACT Study enrolled 6,765 adult patients who were provided an Apple Watch and blood pressure (BP) monitors. From March to July 2019, participants were asked to contact the study team for any irregular heart rate (HR) notification. They were assessed using structured questionnaires and asked to provide 6 Apple Watch EKGs. Those with arrhythmias or non-diagnostic EKGs were sent 7-day monitors. The EHR was reviewed after 3 years to determine if participants developed arrhythmias.

Results

86 participants received notifications and met inclusion criteria. Mean age was 50.5 (SD 16.9) years, and 46 (53.3%) were female. Of 76 participants assessed by the study team, 32 (42.1%) reported anxiety surrounding notifications. Of 59 participants who sent at least 1 EKG, 52 (88.1%) were in sinus rhythm, 3 (5.1%) AF, 2 (3.4%) indeterminate, and 2 (3.4%) sinus bradycardia. Cardiac monitor demonstrated AF in 2 of 3 participants with AF on Apple Watch EKGs. 2 contacted their PCPs and were diagnosed with AF. In total, 5 cases of AF were diagnosed with 1 additional case identified during EHR review.

Conclusion

Wearable devices produce alarms that can frequently be anxiety provoking. Research is needed to determine the implications of these alarms and appropriate follow-up.

Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network

BACKGROUND

Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization.

METHODS

Michigan Medicine heart failure patients from 2013-2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness of the model were evaluated with the mean and standard deviation of the area under the receiver operating curve (AUC), the area under the precision-recall curve (AUPRC), and the F1 score of the ensemble. We inferred membership functions from the model for continuous clinical variables, extracted decision rules, and then evaluated their relative importance.

RESULTS

The model was trained and validated using data from 557 heart failure hospitalizations from 300 patients, of whom 193 received advanced therapies. The mean (standard deviation) of AUC, AUPRC, and F1 scores of the proposed model initialized with clinical knowledge was 0.747 (0.080), 0.642 (0.080), and 0.569 (0.067), respectively, showing superior predictive performance or increased interpretability over other machine learning methods. The model learned critical risk factors predicting the need for advanced therapies in the subsequent hospitalization. Furthermore, our model displayed transparent rule sets composed of these critical concepts to justify the prediction.

CONCLUSION

These results demonstrate the ability to successfully predict the need for advanced heart failure therapies by generating transparent and accessible clinical rules although further research is needed to prospectively validate the risk factors identified by the model.

Differences between patients in whom physicians agree versus disagree about the preoperative diagnosis of heart failure

STUDY OBJECTIVE

To quantify preoperative heart failure (HF) diagnostic agreement and identify characteristics of patients in whom physicians agreed versus disagreed about the diagnosis.

DESIGN

Observational cohort study.

SETTING

Patients undergoing major non-cardiac surgery at an academic center between 2015 and 2019.

PATIENTS

40,659 patients undergoing major non-cardiac surgery, among which a stratified subsample of 1018 patients with and without documented HF was reviewed.

INTERVENTIONS

Via a panel of physicians frequently managing patients with HF (cardiologists, cardiac anesthesiologists, intensivists), detailed chart reviews were performed (two per patient; median review time 32 min per reviewer per patient) to render adjudicated HF diagnoses.

MEASUREMENTS

Adjudicated diagnostic agreement measures (percent agreement, Krippendorf's alpha) and univariate comparisons (standardized differences) between patients in whom physicians agreed versus disagreed about the preoperative HF diagnosis.

MAIN RESULTS

Among patients with documented HF, physicians agreed about the diagnosis in 80.0% of cases (consensus positive), disagreed in 13.8% (disagreement), and refuted the diagnosis in 6.3% (consensus negative). Conversely, among patients without documented HF, physicians agreed about the diagnosis in 88.0% (consensus negative), disagreed in 8.4% (disagreement), and refuted the diagnosis in 3.6% (consensus positive). The estimated agreement for the 40,659 cases was 91.1% (95% CI 88.3%-93.9%); Krippendorff's alpha was 0.77 (0.75-0.80). Compared to patients in whom physicians agreed about a HF diagnosis, patients in whom physicians disagreed exhibited fewer guideline-defined HF diagnostic criteria.

CONCLUSIONS

Physicians usually agree about HF diagnoses adjudicated via chart review, although disagreement is not uncommon and may be partly explained by heterogeneous clinical presentations. Our findings inform preoperative screening processes by identifying patients whose characteristics contribute to physician disagreement via chart review. Clinical Trial Number / Registry URL: Not applicable.

Association Between Wearable Device Measured Activity and Patient-Reported Outcomes for Heart Failure

BACKGROUND

Wearable devices are increasingly used in research and clinical care though the relevance of their data in the context of validated outcomes remains unknown.

OBJECTIVES

The purpose of this study was to characterize the relationship between smartwatch activity and patient-centered outcomes in patients with heart failure.

METHODS

CHIEF-HF (Canagliflozin: Impact on Health Status, Quality of Life and Functional Status in Heart Failure) was a randomized-controlled clinical trial that enrolled participants with heart failure and a compatible smartphone. Participants were provided a Fitbit Versa 2 and completed serial Kansas City Cardiomyopathy Questionnaires (KCCQs) through a smartphone application. We evaluated the relationship between daily step count and floors climbed and KCCQ total symptom (TS) and physical limitation (PL) scores at baseline and their respective changes between 2 and 12 weeks using linear regression models, with restricted cubic splines for nonlinear associations.

RESULTS

In total, 425 patients were included: 44.5% women, 40.9% with reduced ejection fraction. Baseline daily step count increased across categories of KCCQ-TS scores (2,437.6 ± 1,419.5 steps/d for scores 0 to 24 vs 4,870.9 ± 3,171.3 steps/d for scores 75 to 100; P < 0.001) with similar results for KCCQ-PL scores. This relationship remained significant for KCCQ-TS and KCCQ-PL scores after multivariable adjustment. Importantly, changes in daily step count were significantly associated with nonlinear changes in KCCQ-TS (P = 0.004) and KCCQ-PL (P = 0.003) scores. Floors climbed was associated with baseline KCCQ scores alone.

CONCLUSIONS

Daily step count was nonlinearly associated with health status at baseline and over time in patients with heart failure. These results may inform interpretation of wearable device data in clinical and research contexts. (A Study on Impact of Canagliflozin on Health Status, Quality of Life, and Functional Status in Heart Failure [CHIEF-HF]; NCT04252287).

Longitudinal Change and Predictors of Myocardial Flow Reserve by Positron Emission Tomography for the Evaluation of Cardiac Allograft Vasculopathy Following Heart Transplantation

BACKGROUND

Positron emission tomography (PET) myocardial flow reserve (MFR) is a noninvasive method of detecting cardiac allograft vasculopathy in recipients of heart transplants (HTs). There are limited data on longitudinal change and predictors of MFR following HT.

METHODS

We conducted a retrospective analysis of HT recipients undergoing PET myocardial perfusion imaging at an academic center. Multivariable linear and Cox regression models were constructed to identify longitudinal trends, predictors and the prognostic value of MFR after HT.

RESULTS

Of HT recipients, 183 underwent 658 PET studies. The average MFR was 2.34 ± 0.70. MFR initially increased during the first 3 years following HT (+ 0.12 per year; P = 0.01) before beginning to decline at an annual rate of -0.06 per year (P < 0.001). MFR declines preceding acute rejection and improves after treatment. Treatment with mammalian target of rapamycin (mTOR) inhibitors (37.2%) slowed the rate of annual MFR decline (P = 0.03). Higher-intensity statin therapy was associated with improved MFR. Longer time post-transplant (P < 0.001), hypertension (P < 0.001), chronic kidney disease (P < 0.001), diabetes mellitus (P = 0.038), antibody-mediated rejection (P = 0.040), and cytomegalovirus infection (P = 0.034) were associated with reduced MFR. Reduced MFR (HR: 7.6, 95% CI: 4.4-13.4; P < 0.001) and PET-defined ischemia (HR: 2.3, 95% CI: 1.4-3.9; P < 0.001) were associated with a higher risk of the composite outcome of mortality, retransplantation, heart failure hospitalization, acute coronary syndrome, or revascularization.

CONCLUSION

MFR declines after the third post-transplant year and is prognostic for cardiovascular events. Cardiometabolic risk-factor modification and treatment with higher-intensity statin therapy and mechanistic target of rapamycin inhibitors are associated with a higher MFR.

Use of Human-Centered Design Methodology to Develop a Digital Toolkit to Optimize Heart Failure Guideline-Directed Medical Therapy

BACKGROUND

Guideline-directed medical therapies (GDMTs) improve quality of life and health outcomes for patients with heart failure (HF). However, GDMT utilization is suboptimal among patients with HF.

OBJECTIVE

The aims of this study were to engage key stakeholders in semistructured, virtual human-centered design sessions to identify challenges in GDMT optimization posthospitalization and inform the development of a digital toolkit aimed at optimizing HF GDMTs.

METHODS

For the human-centered design sessions, we recruited (a) clinicians who care for patients with HF across 3 hospital systems, (b) patients with HF with reduced ejection fraction (ejection fraction ≤ 40%) discharged from the hospital within 30 days of enrollment, and (c) caregivers. All participants were 18 years or older, English speaking, with Internet access.

RESULTS

A total of 10 clinicians (median age, 37 years [interquartile range, 35-41], 12 years [interquartile range, 10-14] of experience caring for patients with HF, 80% women, 50% White, 50% nurse practitioners) and three patients and one caregiver (median age 57 years [IQR: 53-60], 75% men, 50% Black, 75% married) were included. Five themes emerged from the clinician sessions on challenges to GDMT optimization (eg, barriers to patient buy-in). Six themes on challenges (eg, managing medications), 4 themes on motivators (eg, regaining independence), and 3 themes on facilitators (eg, social support) to HF management arose from the patient and caregiver sessions.

CONCLUSIONS

The clinician, patient, and caregiver insights identified through human-centered design will inform a digital toolkit aimed at optimizing HF GDMTs, including a patient-facing smartphone application and clinician dashboard. This digital toolkit will be evaluated in a multicenter, clinical trial.

A randomized trial of a mobile health intervention to augment cardiac rehabilitation

Mobile health (mHealth) interventions may enhance positive health behaviors, but randomized trials evaluating their efficacy are uncommon. Our goal was to determine if a mHealth intervention augmented and extended benefits of center-based cardiac rehabilitation (CR) for physical activity levels at 6-months. We delivered a randomized clinical trial to low and moderate risk patients with a compatible smartphone enrolled in CR at two health systems. All participants received a compatible smartwatch and usual CR care. Intervention participants received a mHealth intervention that included a just-in-time-adaptive intervention (JITAI) as text messages. The primary outcome was change in remote 6-minute walk distance at 6-months stratified by device type. Here we report the results for 220 participants enrolled in the study (mean [SD]: age 59.6 [10.6] years; 67 [30.5%] women). For our primary outcome at 6 months, there is no significant difference in the change in 6 min walk distance across smartwatch types (Intervention versus control: +31.1 meters Apple Watch, -7.4 meters Fitbit; p = 0.28). Secondary outcomes show no difference in mean step counts between the first and final weeks of the study, but a change in 6 min walk distance at 3 months for Fitbit users. Amongst patients enrolled in center-based CR, a mHealth intervention did not improve 6-month outcomes but suggested differences at 3 months in some users.

Use of exception status listing and related outcomes during two heart allocation policy periods

BACKGROUND

The October 2018 update to the heart allocation policy was intended to decrease exception status requests, whereby candidates are listed at a specific status due to perceived need despite not meeting prespecified criteria of illness severity. We assessed the use of exception status and waitlist outcomes before and after the 2018 policy.

METHODS

We used data from the Scientific Registry of Transplant Recipients on adult heart transplant candidates listed from 2015 to 2021. We assessed (1) the use of exception status across patient characteristics between the two periods and (2) transplant rate and waitlist mortality or delisting due to deterioration in each period. Patients listed by exception versus standard criteria were compared with multivariable logistic regression, and waitlist outcomes were assessed using Cox proportional hazard models with medical urgency and exception status as time-dependent covariates.

RESULTS

During the study period (n = 19,213), heart transplants under exception status increased postpolicy from 10.0% to 32.3%, with 20.6% of transplants performed for patients at status 2 exception. Exception status candidates postpolicy were more frequently Black or Hispanic/Latino and less likely to have hypertrophic or restrictive cardiomyopathy and had worse hemodynamics. Exception status listing was associated with higher transplant rates in both periods. Postpolicy, candidates listed status 1 exception had a lower likelihood for waitlist mortality or delisting (hazard ratio, 0.60; 95% CI, 0.37-0.99; and p = 0.05).

CONCLUSIONS

Under the 2018 policy, exception status listings dramatically increased. The policy change shifted the population of patients listed by exception status and affected waitlist mortality, which suggests a need to further evaluate the policy's impact.

Greater geographic sharing and heart transplantation waitlist outcomes following the 2018 heart allocation policy

BACKGROUND

In 2018, a new heart allocation policy was introduced to reduce variability in access to and outcomes after transplantation, in part, through attempts at broader geographic sharing of donor hearts. We evaluated how this policy affected geographic sharing and waitlist outcomes by donation service area (DSA).

METHODS

This retrospective study of the Scientific Registry of Transplant Recipients database included adult patients waitlisted between October 2016 and October 2020, stratified by policy period. Our primary outcomes were mean proportion of imported and exported hearts aggregated by DSA as well as time to transplant.

RESULTS

Following the policy change, there was substantial evidence of sharing across DSAs. The mean proportion of imported hearts transplanted by a DSA increased from 32% (95% CI: 27%-36%) to 74% (95% CI: 71%-78%; p < 0.001), and the mean proportion of exported hearts increased from 37% (95% CI: 33%-42%) to 75% (95% CI: 71%-79%; p < 0.001). The mean sharing ratio, defined as the log-transformed ratio of imported to exported hearts per DSA, shifted from 1.15 (95% CI: 0.88-1.42) to 1.02 (95% CI: 0.96-1.07), with a 76% decline in the variance across DSAs. As sharing increased, time to transplant per DSA declined from 153.9 days (95% CI, 143.4-164.4 days) pre-policy to 89.6 days (95% CI, 83.1-96.1 days) post-policy (p < 0.001). A larger decrease in waitlist time was associated with a higher proportion of exported hearts.

CONCLUSIONS

The 2018 heart allocation policy was associated with more uniform access to heart transplantation and improved waitlist outcomes.

Digital Technologies in Cardiac Rehabilitation: A Science Advisory From the American Heart Association

Cardiac rehabilitation has strong evidence of benefit across many cardiovascular conditions but is underused. Even for those patients who participate in cardiac rehabilitation, there is the potential to better support them in improving behaviors known to promote optimal cardiovascular health and in sustaining those behaviors over time. Digital technology has the potential to address many of the challenges of traditional center-based cardiac rehabilitation and to augment care delivery. This American Heart Association science advisory was assembled to guide the development and implementation of digital cardiac rehabilitation interventions that can be translated effectively into clinical care, improve health outcomes, and promote health equity. This advisory thus describes the individual digital components that can be delivered in isolation or as part of a larger cardiac rehabilitation telehealth program and highlights challenges and future directions for digital technology generally and when used in cardiac rehabilitation specifically. It is also intended to provide guidance to researchers reporting digital interventions and clinicians implementing these interventions in practice and to advance a framework for equity-centered digital health in cardiac rehabilitation.

Recruitment Strategies of a Decentralized Randomized Placebo Controlled Clinical Trial: The Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure (CHIEF-HF) Trial

BACKGROUND

There has been growing Interest in patient-centered clinical trials using mobile technologies to reduce the need for in-person visits. The CHIEF-HF (Canagliflozin Impact on Health Status, Quality of Life and Functional Status in Heart Failure) trial was designed as a double-blind, randomized, fully decentralized clinical trial (DCT) that identified, consented, treated, and followed participants without any in-person visits. Patient-reported questionnaires were the primary outcome, which were collected by a mobile application. To inform future DCTs, we sought to describe the strategies used in successful trial recruitment.

METHODS

This article describes the operational structure and novel strategies employed in a completely DCT by summarizing the recruitment, enrollment, engagement, retention, and follow-up processes used in the execution of the trial at 18 centers.

RESULTS

A total of 18 sites contacted 130,832 potential participants, of which 2572 (2.0%) opened a hyperlink to the study website, completed a brief survey, and agreed to be contacted for potential inclusion. Of these, 1333 were eligible, and 658 consented; there were 182 screen failures, due primarily to baseline Kansas City Cardiomyopathy Questionnaire scores' not meeting inclusion criteria, resulting in 476 participants' being enrolled (18.5%). There was significant site-level variation in the number of patients invited (median = 2976; range 73-46,920) and in those agreeing to be contacted (median = 2.4%; range 0.05%-16.4%). At the site with the highest enrollment, patients contacted by electronic medical record portal messaging were more likely to opt into the study successfully than those contacted by e-mail alone (7.8% vs 4.4%).

CONCLUSIONS

CHIEF-HF used a novel design and operational structure to test the efficacy of a therapeutic treatment, but marked variability across sites and strategies for recruiting participants was observed. This approach may be advantageous for clinical research across a broader range of therapeutic areas, but further optimization of recruitment efforts is warranted.

REGISTRATION

NCT04252287 https://clinicaltrials.gov/ct2/show/NCT04252287.

Abstract P398: Using Human-Centered Design Methodology to Identify Challenges and Inform the Development of a Digital Toolkit to Optimize Heart Failure Guideline-Directed Medical Therapy From Diverse Clinician, Patient, and Patient Health Partner Perspectives

Introduction: Despite overwhelming evidence that guideline-directed medical therapies (GDMT) for heart failure (HF) can reduce mortality and improve quality of life, significant gaps in treatment optimization persist. GDMT initiation and up-titration are especially critical for improving patient outcomes post-hospitalization.

Objective: Identify challenges encountered post-hospitalization in optimizing GDMT for HF management by engaging key stakeholders in human-centered design (HCD) to guide the development of a digital toolkit to increase HF GDMT optimization.

Methods: HCD is used to solve complex problems by soliciting input from stakeholders. We recruited: a) clinicians (physicians and advanced practice providers) who provide care to patients with HF across three health systems, b) patients with HF with Reduced Ejection Fraction (HFrEF, EF < 40%) discharged from the hospital within 30 days of enrollment, and c) patient health partners when available. We conducted separate virtual sessions for clinicians and patients/health partners using semi-structured interview guides to identify challenges, motivators and themes.

Results: We enrolled 10 clinicians, 10 patients, and 2 patient health partners. The clinicians had a median age of 37 years (IQR: 35-41) and 12 years (IQR: 14-9) experience caring for patients with HF; 80% (8/10) were women, and 50% (5/10) were physicians. Patients had a median age of 53 years (IQR: 48-64); 40% (4/10) were women, 60% (6/10) were a racial/ethnic minority, and 50% (5/10) were married. Top challenges to HF GDMT optimization (e.g. number of medications) and digital toolkit features identified during the clinician HCD sessions are reported in Figure 1.

Conclusions: The clinician and patient/health partner HCD findings will inform the development of the digital toolkit, including a patient-facing smartphone application and clinician dashboard, for HF GDMT optimization. We will also conduct HCD sessions in Brazil to further co-design the digital toolkit for low resource settings.

Preoperative clinical diagnostic accuracy of heart failure among patients undergoing major noncardiac surgery: a single-centre prospective observational analysis

Background

Reliable diagnosis of heart failure during preoperative evaluation is important for perioperative management and long-term care. We aimed to quantify preoperative heart failure diagnostic accuracy and explore characteristics of patients with heart failure misdiagnoses.

Methods

We performed an observational cohort study of adults undergoing major noncardiac surgery at an academic hospital between 2015 and 2019. A preoperative clinical diagnosis of heart failure was defined using keywords from the history and clinical examination or administrative documentation. Across stratified subsamples of cases with and without clinically diagnosed heart failure, health records were intensively reviewed by an expert panel to develop an adjudicated heart failure reference standard using diagnostic criteria congruent with consensus guidelines. We calculated agreement among experts, and analysed performance of clinically diagnosed heart failure compared with the adjudicated reference standard.

Results

Across 40 555 major noncardiac procedures, a stratified subsample of 511 patients was reviewed by the expert panel. The prevalence of heart failure was 9.1% based on clinically diagnosed compared with 13.3% (95% confidence interval [CI], 10.3-16.2%) estimated by the expert panel. Overall agreement and inter-rater reliability (kappa) among heart failure experts were 95% and 0.79, respectively. Based upon expert adjudication, heart failure was clinically diagnosed with an accuracy of 92.8% (90.6-95.1%), sensitivity 57.4% (53.1-61.7%), specificity 98.3% (97.1-99.4%), positive predictive value 83.5% (80.3-86.8%), and negative predictive value 93.8% (91.7-95.9%).

Conclusions

Limitations exist to the preoperative clinical diagnosis of heart failure, with nearly half of cases undiagnosed preoperatively. Considering the risks of undiagnosed heart failure, efforts to improve preoperative heart failure diagnoses are warranted.

Virtual AppLication-supported Environment To INcrease Exercise (VALENTINE) during cardiac rehabilitation study: Rationale and design

BACKGROUND

In-person, exercise-based cardiac rehabilitation improves physical activity and reduces morbidity and mortality for patients with cardiovascular disease. However, activity levels may not be optimized and decline over time after patients graduate from cardiac rehabilitation. Scalable interventions through mobile health (mHealth) technologies have the potential to augment activity levels and extend the benefits of cardiac rehabilitation.

METHODS

The VALENTINE Study is a prospective, randomized-controlled, remotely-administered trial designed to evaluate an mHealth intervention to supplement cardiac rehabilitation for low- and moderate-risk patients (ClinicalTrials.gov NCT04587882). Participants are randomized to the control or intervention arms of the study. Both groups receive a compatible smartwatch (Fitbit Versa 2 or Apple Watch 4) and usual care. Participants in the intervention arm of the study additionally receive a just-in-time adaptive intervention (JITAI) delivered as contextually tailored notifications promoting low-level physical activity and exercise throughout the day. In addition, they have access to activity tracking and goal setting through the mobile study application and receive weekly activity summaries via email. The primary outcome is change in 6-minute walk distance at 6-months and, secondarily, change in average daily step count. Exploratory analyses will examine the impact of notifications on immediate short-term smartwatch-measured step counts and exercise minutes.

CONCLUSIONS

The VALENTINE study leverages innovative techniques in behavioral and cardiovascular disease research and will make a significant contribution to our understanding of how to support patients using mHealth technologies to promote and sustain physical activity.

Understanding Baseline Physical Activity in Cardiac Rehabilitation Enrollees Using Mobile Health Technologies

Background: Baseline physical activity in patients when they initiate cardiac rehabilitation is poorly understood. We used mobile health (mHealth) technology to understand baseline physical activity of patients initiating cardiac rehabilitation within a clinical trial to potentially inform personalized care. Methods: The Virtual AppLication-Supported ENvironment To INcrease Exercise During Cardiac Rehabilitation Study (VALENTINE) Study is a prospective, randomized-controlled, remotely administered trial designed to evaluate an mHealth intervention to supplement cardiac rehabilitation for low and moderate risk patients. All participants receive a smartwatch and usual care. Baseline physical activity was assessed remotely after enrollment and included 1) 6-minute walk distance, 2) daily step count, and 3) daily exercise minutes, both over 7 days and for compliant days, defined by ≥8 hours of watch wear time. Multivariable linear regression identified patient-level features associated with these 3 measures of baseline physical activity. Results: From October 2020 to March 2022, 220 participants enrolled in the study. Participants are mostly White [184 (83.6%)]; 67 (30.5%) are female and 84 (38.2%) are ≥ 65 years old. Most participants enrolled in cardiac rehabilitation after percutaneous coronary intervention [105 (47.7%)] or coronary artery bypass surgery [39 (17.7 %)]. Clinical diagnoses include coronary artery disease (78.6%), heart failure (17.3%), and valve repair or replacement (26.4%). Baseline mean 6-minute walk distance was 489.6 (standard deviation [SD], 143.4) meters, daily step count was 6845 (SD, 3353), and exercise minutes was 37.5 (SD, 33.5). In a multivariable model, 6-minute walk distance was significantly associated with age and sex, but not cardiac rehabilitation indication. Sex but not age or cardiac rehabilitation indication was significantly associated with daily step count and exercise minutes. Conclusions: Baseline physical activity varies substantially in low and moderate risk patients enrolled in cardiac rehabilitation. Future studies are warranted to explore whether personalizing cardiac rehabilitation programs using mHealth technologies could optimize recovery. Registration: URL: https://clinicaltrials.gov Unique Identifier: NCT04587882.

Abstract 159: Understanding Baseline Physical Activity In Cardiac Rehabilitation Enrollees Using Mobile Health Technologies

Background: Cardiac rehabilitation (CR) improves physical activity (PA) and reduces morbidity for patients with cardiovascular disease. We understand little of baseline PA as patients initiate CR, particularly when outside of CR. We used mobile health (mHealth) technology to understand baseline PA of patients initiating CR within a clinical trial to potentially inform personalized care.

Methods: The Virtual AppLication-Supported Environment to INcrease Exercise During Cardiac Rehabilitation Study (VALENTINE) Study is a prospective, randomized-controlled, remotely administered trial designed to evaluate an mHealth intervention to supplement CR for low- and moderate-risk patients. Participants are randomized after 2 CR sessions; all receive a smartwatch and usual care. Remotely administered baseline PA outcomes include 6-minute walk distance, step count, and exercise minutes. Baseline PA was assessed for 7-days after study enrollment for compliant days, defined by > 8 hours of watch wear/day. Multivariable linear regression identified features associated with baseline PA.

Results: From October 19, 2020 to January 31, 2022, 180 participants enrolled. Participants are mostly White [156 (86.7%)]; 59 (32.8%) are female and 69 (38.3%) are > 65 years old. Most enrolled in CR after coronary revascularization [114 (64.4%)] or valve intervention [40 (22.2%)]. Comorbidities include hypertension (65.0%), valve disease (35.6%), and heart failure (17.8%). Participants were compliant for 91.4% of days with 15.3 (4.0) hours/compliant day. Baseline PA included 6-minute walk distance of 491.8 (147.8) meters, daily step count of 6818 (3386), and exercise minutes of 36.0 (33.4). Substantial variation in baseline PA assessed by 6-minute walk distance was noted across age and gender but not CR indication.

Conclusions: Understanding baseline PA as participants enroll in CR may be useful in personalizing CR programs at initiation and designing mHealth interventions.

A Physical Activity Just-in-time Adaptive Intervention Designed in Partnership With a Predominantly Black Community: Virtual, Community-Based Participatory Design Approach

Background

Black people are disproportionally impacted by hypertension. New approaches for encouraging healthy lifestyles are needed to reduce hypertension and promote health equity in Black communities.

Objective

In this report, we describe the early-stage, virtual design of a just-in-time adaptive intervention (JITAI) to increase physical activity in partnership with members of a low-income, predominantly Black community.

Methods

The hallmark of JITAIs is highly contextualized mobile app push notifications. Thus, understanding participants' context and determinants of physical activity are critical. During the height of the COVID-19 pandemic, we conducted virtual discovery interviews and analysis guided by the Behavior Change Wheel (which focuses on participants' capacity, opportunity, and motivation to engage in physical activity), as well as empathy mapping. We then formed a community-academic participatory design team that partnered in the design sprint, storyboarding, and paper prototyping.

Results

For this study, 5 community members participated in the discovery interviews, 12 stakeholders participated in the empathy mapping, 3 community members represented the community on the design team, and 10 community members provided storyboard or paper prototyping feedback. Only one community member had used videoconferencing prior to partnering with the academic team, and none had design experience. A set of 5 community-academic partner design principles were created: (1) keep users front and center, (2) tailor to the individual, (3) draw on existing motivation, (4) make physical activity feel approachable, and (5) make data collection transparent yet unobtrusive. To address community-specific barriers, the community-academic design team decided that mobile app push notifications will be tailored to participants’ baseline mobility level and community resources (eg, local parks and events). Push notifications will also be tailored based on the day (weekday versus weekend), time of day, and weather. Motivation will be enhanced via adaptive goal setting with supportive feedback and social support via community-generated notifications.

Conclusions

We completed early-stage virtual design of a JITAI in partnership with community participants and a community design team with limited design and videoconferencing experience. We found that designing JITAIs with the community enables these interventions to address community-specific needs, which may lead to a more meaningful impact on users' health.

A Novel Tropical Geometry-Based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this article, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification.

Wearable device signals and home blood pressure data across age, sex, race, ethnicity, and clinical phenotypes in the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) study: a prospective, community-based observational study

BACKGROUND

Wearable technology has rapidly entered consumer markets and has health-care potential; however, wearable device data for diverse populations are scarce. We therefore aimed to describe and compare key wearable signals (ie, heart rate, step count, and home blood pressure measurements) across age, sex, race, ethnicity, and clinical phenotypes.

METHODS

In the Michigan Predictive Activity & Clinical Trajectories in Health (MIPACT) prospective observational study, we enrolled participants from Michigan Medicine, Ann Abor, MI, USA, and followed them up for at least 90 days. Patients were included if they were aged 18 years or older, were fluent in English, owned an iPhone 6 or newer model with a supported iOS version, and had regular access to the internet throughout the study period. All participants were provided with an Apple Watch Series 3 or 4, an Omron Evolv Wireless Blood Pressure Monitor, and the MyDataHelps study smartphone application. Participants were asked to wear their watch for 12 h per day or longer and to do daily or weekly tasks, including home blood pressure measurements and breathing tasks. Heart rate, blood pressure, step counts, and distance walked were collected. The study was divided into two phases: an intensive 45-day collection phase (phase 1); and a 3-year longitudinal monitoring phase (phase 2). Here we report the first 90 days of data for all participants, which includes all of phase 1 and the first 45 days of phase 2. Participants' electronic health records were used to establish clinical diagnoses for analysis.

FINDINGS

We enrolled 6765 eligible participants between Aug 14, 2018, and Dec 19, 2019, of whom 6454 participants from Michigan Medicine completed the phase 1 study protocol and were included in this analysis (3482 [54%] women and 2972 [46%] men; 3657 [57%] participants were White, with 1094 [17%] Asian and 1090 [17%] Black participants). On days when participants wore their smart watches, median daily watch wear time was 15·5 h (IQR 14-17). Participants contributed a total of 1 107 320 blood pressure and 202 198 347 heart rate measurements over 90 days, with 172 (SD 50) blood pressure and 31 329 (SD 24 620) heart rate measurements per participant. Mean systolic blood pressure was 122 mm Hg (SD 10) and mean diastolic blood pressure was 77 mm Hg (SD 8), with 167 312 (15%) measurements having a systolic blood pressure higher than 140 mm Hg or diastolic blood pressure higher than 90 mm Hg. Mean resting heart rate was 64 beats per min (SD 8). Blood pressure and resting heart rate varied by sex, age, race, and ethnicity, with higher blood pressures in males and lower heart rate in participants aged 65 years or older (p<0·0001). Participants took 7511 steps per day (SD 2805) and walked 6009 metres per day (SD 2608), varying across demographic and clinical subgroups.

INTERPRETATION

These data could inform clinical trial design, interpretation of wearable data in clinical practice, and health-care interventions.

FUNDING

Apple, University of Michigan.

A challenge to equity in transplantation: Increased center-level variation in short-term mechanical circulatory support use in the context of the updated U.S. heart transplant allocation policy

BACKGROUND

The United States National Organ Procurement Transplant Network (OPTN) implemented changes to the adult heart allocation system to reduce waitlist mortality by improving access for those at greater risk of pre-transplant death, including patients on short-term mechanical circulatory support (sMCS). While sMCS increased, it is unknown whether the increase occurred equitably across centers.

METHODS

The OPTN database was used to assess changes in use of sMCS at time of transplant in the 12 months before (pre-change) and after (post-change) implementation of the allocation system in October 2018 among 5,477 heart transplant recipients. An interrupted time series analysis comparing use of bridging therapies pre- and post-change was performed. Variability in the proportion of sMCS use at the center level pre- and post-change was determined.

RESULTS

In the month pre-change, 9.7% of patients were transplanted with sMCS. There was an immediate increase in sMCS transplant the following month to 32.4% - an absolute and relative increase of 22.7% and 312% (p < 0.001). While sMCS use was stable pre-change (monthly change 0.0%, 95% CI [-0.1%,0.1%]), there was a continuous 1.2%/month increase post-change ([0.6%,1.8%], p < 0.001). Center-level variation in sMCS use increased substantially after implementation, from a median (interquartile range) of 3.85% (10%) pre-change to 35.7% (30.6%) post-change (p < 0.001).

CONCLUSIONS

Use of sMCS at time of transplant increased immediately and continued to expand following heart allocation policy changes. Center-level variation in use of sMCS at the time of transplant increased compared to pre-change, which may have negatively impacted equitable access to heart transplantation.

Detection of Low Cardiac Index using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively.

Microrandomized Trial Design for Evaluating Just-in-Time Adaptive Interventions Through Mobile Health Technologies for Cardiovascular Disease

Smartphone and wearable device use is rising broadly and can be leveraged for chronic disease management. Just-in-time adaptive interventions promise to deliver personalized, dynamic interventions directly to patients through use of push notifications from mobile devices. Although just-in-time adaptive interventions are a powerful tool for shaping health behavior, their application to cardiovascular disease management has been limited as they can be challenging to design. Herein, we provide a general overview and conceptual framework for microrandomized trials, a novel experimental study design that can be used to optimize just-in-time adaptive interventions. Microrandomized trials leverage mobile devices to sequentially randomize participants to types or levels of an intervention to determine the effectiveness of an intervention and time-varying moderators of those effects. Microrandomized trials are an efficient study design that can be used to determine which intervention components to include in just-in-time adaptive interventions and to optimize their decision rules while maintaining the strength of causal inference associated with traditional randomized controlled trials.

Virtual echocardiography screening tool to differentiate hemodynamic profiles in pulmonary hypertension

This study validated a novel virtual echocardiography screening tool (VEST), which utilized routinely reported echocardiography parameters to predict hemodynamic profiles in pulmonary hypertension (PH) and identify PH due to pulmonary vascular disease (PHPVD). Direct echocardiography imaging review has been shown to predict hemodynamic profiles in PH; however, routine use often overemphasizes Doppler-estimated pulmonary artery systolic pressure (PASPDE), which lacks discriminatory power among hemodynamically varied PH subgroups. In patients with PH of varying subtypes at a tertiary referral center, reported echocardiographic findings needed for VEST, including left atrial size, E:e' and systolic interventricular septal flattening, were obtained. Receiver operating characteristic analyses assessed the predictive performance of VEST vs. PASPDE in identifying PHPVD, which was later confirmed by right heart catheterization. VEST demonstrated far superior discriminatory power than PASPDE in identifying PHPVD. A positive score was 80.0% sensitive and 75.6% specific for PHPVD with an area under the curve of 0.81. PASPDE exhibited poorer discriminatory power with an area under the curve of 0.56. VEST's strong discriminatory ability remained unchanged when validated in a second cohort from another tertiary center. We demonstrated that this novel VEST using three routine parameters that can be easily extracted from standard echocardiographic reports can successfully capture PH patients with a high likelihood of PHPVD. During the Covid-19 pandemic, when right heart catheterization and timely access to experts at accredited PH centers may have limited widespread availability, this may assist physicians to rapidly and remotely evaluate PH patients to ensure timely and appropriate care.

Statin intensity and risk for cardiovascular events after heart transplantation

AIMS

Statins improve survival and reduce rejection and cardiac allograft vasculopathy after heart transplantation (HT). The impact of different statin intensities on clinical outcomes has never been assessed. We set out to determine the impact of statin exposure on cardiovascular outcomes after HT.

METHODS AND RESULTS

We performed a retrospective study of 346 adult patients who underwent HT from 2006 to 2018. Statin intensity was determined longitudinally after HT based on American College of Cardiology/American Heart Association (ACC/AHA) guidelines. The primary outcome was the time to the first primary event defined as the composite of heart failure hospitalization, myocardial infarction, revascularization, and all-cause mortality. Secondary outcomes included time to significant rejection and time to moderate-severe cardiac allograft vasculopathy. Adverse events were evaluated for subjects on high-intensity statin therapy. A Cox proportional hazards model was used to evaluate the relationship between clinical variables, statin intensity, and outcomes. Most subjects were treated with low-intensity statin therapy although this declined from 89.9% of the population at 1month after HT to 42.8% at 5years after HT. History of ischaemic cardiomyopathy, significant acute rejection, older donor age, and lesser statin intensity (p ≤ 0.001) were associated with reduced time to the primary outcome in a multivariable Cox model. Greater intensity of statin therapy was most beneficial early after HT. There were no statin-related adverse events for the 14 subjects on high-intensity statin therapy.

CONCLUSIONS

Greater statin intensity was associated with a reduction in adverse cardiovascular outcomes after HT.

Utility of routine evaluations for rejection in patients greater than 2 years after heart transplantation

AIMS

Guidelines support routine surveillance testing for rejection for at least 5 years after heart transplant (HT). In patients greater than 2 years post-HT, we examined which clinical characteristics predict continuation of routine surveillance studies, outcomes following discontinuation of routine surveillance, and the cost-effectiveness of different surveillance strategies.

METHODS AND RESULTS

We retrospectively identified subjects older than 18 who underwent a first HT at our centre from 2007 to 2016 and who survived ≥760 days (n = 217) post-HT. The clinical context surrounding all endomyocardial biopsies (EMBs) and gene expression profiles (GEPs) was reviewed to determine if studies were performed routinely or were triggered by a change in clinical status. Subjects were categorized as following a test-based surveillance (n = 159) or a signs/symptoms surveillance (n = 53) strategy based on treating cardiologist intent to continue routine studies after the second post-transplant year. A Markov model was constructed to compare two test-based surveillance strategies to a baseline strategy of discontinuing routine studies. One thousand twenty studies were performed; 835 were routine. Significant rejection was absent in 99.0% of routine EMBs and 99.8% of routine GEPs. The treating cardiologist's practice duration, patient age, and immunosuppressive regimen predicted surveillance strategy. There were no differences in outcomes between groups. Routine surveillance EMBs cost more and were marginally less effective than a strategy of discontinuing routine studies after 2 years; surveillance GEPs had an incremental cost-effectiveness ratio of $1.67 million/quality-adjusted life-year.

CONCLUSIONS

Acute asymptomatic rejection is rare after the second post-transplant year. Obtaining surveillance studies beyond the second post-transplant year is not cost-effective.

Abstract 225: Determinants of Compensation for US Academic Physicians: Does Gender Matter?

Introduction: Academic physician compensation remains opaque, despite its critical importance for negotiating equitable salaries. In particular, prior studies have suggested inequity between male and female physicians. We set out to broadly define determinants of compensation in US academic physicians focusing on gender.

Methods: We performed an online pilot survey using a convenience sample of US academic physicians in clinical practice recruited through social media. Survey questions focused on demographic information, practice environment, job requirements, compensation, and overall satisfaction. Respondents failing to provide information on gender (n=2), salary (n=43) or key compensation questions (n=7) were excluded. Predictors of total salary, defined as the sum of base salary plus bonuses, and satisfaction with position (range 0-100) were explored in a generalized regression model.

Results: 252 respondents met inclusion criteria for analysis of total salary. Of the respondents, 32.9% were female, 59.9% Caucasian, 35.7% were associate professors or professors, 54.8% were employed in an urban setting, 44.9% performed major procedures, and 72.8% practiced in internal medicine or an internal medicine subspecialty. Median total salary was $270,000 (Interquartile range 208,000-348,500). After multivariable adjustment, female physicians made $44,512.55 less in total salary. Performing major procedures (p<0.001), living in a major urban area (p=0.034), and being a professor or associate professor (p<0.001) also predicted greater total salary (Table). Age and years since clinical training ended were significant predictors of total salary in a univariable but not a multivariable model. There was no significant interaction between gender and rank or between gender and performing major procedures. Median job satisfaction in 241 respondents was 77.0 (Interquartile range 64.0-86.0). In a multivariable model, only years since clinical training ended but not total salary or other covariates predicted physician satisfaction.

Conclusions: This pilot online study suggests gender disparities exist with respect to US academic physician compensation. Further studies are needed to better define these disparities and to enhance transparency with respect to physician compensation.

Lower Extremity Function Is Independently Associated With Hospitalization Burden in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Frailty reflects decreased resilience to physiological stressors; its prevalence and prognosis are not fully defined in heart failure with preserved ejection fraction (HFpEF).

METHODS

The Short Physical Performance Battery (SPPB) was prospectively obtained in 114 outpatients with HFpEF. The SPPB tests gait speed, tandem balance, and timed chair rises, each scored from 0 to 4 points. Severe and mild frailty were respectively defined as an SPPB score ≤6 and 7-9 points. We used risk-adjusted logistic, Poisson, and negative binominal regression, respectively, to assess the relationship between SPPB score and risk of death or all-cause hospitalization, number of hospitalizations, and days hospitalized or dead longer than 6 months.

RESULTS

Patients were similar to other HFpEF cohorts (age 68 ± 13 years, 58% female, body mass index 36 ± 8 kg/m², multiple comorbidities). Mean SPPB score was 6.9 ± 3.2, and 80% of patients were at least mildly frail. Over a 6-month period, the SPPB score independently predicted death or all-cause hospitalization (odds ratio 0.81 per point, 95% confidence interval [CI] 0.69-0.94, P = .006), number of hospitalizations (incidence rate ratio 0.92 per point, 95% CI 0.86-0.97, P = .006), and days hospitalized or dead (incidence rate ratio 0.85 per point, 95% CI 0.73-0.99, P = .04).

CONCLUSIONS

Lower extremity function, as measured by the SPPB, independently predicts hospitalization burden in outpatients with HFpEF. Additional studies are warranted to explore shared mechanisms and treatment implications of frailty in HFpEF.