Text Messages to Promote Physical Activity in Patients With Cardiovascular Disease: A Micro-Randomized Trial of a Just-In-Time Adaptive Intervention.

BACKGROUND: Text messages may enhance physical activity levels in patients with cardiovascular disease, including those enrolled in cardiac rehabilitation. However, the independent and long-term effects of text messages remain uncertain. METHODS: The VALENTINE study (Virtual Application-supported Environment to Increase Exercise) was a micro-randomized trial that delivered text messages through a smartwatch (Apple Watch or Fitbit Versa) to participants initiating cardiac rehabilitation. Participants were randomized 4× per day over 6-months to receive no text message or a message encouraging low-level physical activity. Text messages were tailored on contextual factors (eg, weather). Our primary outcome was step count 60 minutes following a text message, and we used a centered and weighted least squares mean method to estimate causal effects. Given potential measurement differences between devices determined a priori, data were assessed separately for Apple Watch and Fitbit Versa users over 3 time periods corresponding to the initiation (0-30 days), maintenance (31-120 days), and completion (121-182 days) of cardiac rehabilitation. RESULTS: One hundred eight participants were included with 70 552 randomizations over 6 months; mean age was 59.5 (SD, 10.7) years with 36 (32.4%) female and 68 (63.0%) Apple Watch participants. For Apple Watch participants, text messages led to a trend in increased step count by 10% in the 60-minutes following a message during days 1 to 30 (95% CI, -1% to +20%), with no effect from days 31 to 120 (+1% [95% CI, -4% to +5%]), and a significant 6% increase during days 121 to 182 (95% CI, +0% to +11%). For Fitbit users, text messages significantly increased step count by 17% (95% CI, +7% to +28%) in the 60-minutes following a message in the first 30 days of the study with no effect subsequently. CONCLUSIONS: In patients undergoing cardiac rehabilitation, contextually tailored text messages may increase physical activity, but this effect varies over time and by device. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

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.

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 (ß = -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.

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.

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.

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).

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.

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.

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.

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.