The management of heart failure cardiogenic shock: an international RAND appropriateness panel.

BACKGROUND: Observational data suggest that the subset of patients with heart failure related CS (HF-CS) now predominate critical care admissions for CS. There are no dedicated HF-CS randomised control trials completed to date which reliably inform clinical practice or clinical guidelines. We sought to identify aspects of HF-CS care where both consensus and uncertainty may exist to guide clinical practice and future clinical trial design, with a specific focus on HF-CS due to acute decompensated chronic HF. METHODS: A 16-person multi-disciplinary panel comprising of international experts was assembled. A modified RAND/University of California, Los Angeles, appropriateness methodology was used. A survey comprising of 34 statements was completed. Participants anonymously rated the appropriateness of each statement on a scale of 1 to 9 (1-3 as inappropriate, 4-6 as uncertain and as 7-9 appropriate). RESULTS: Of the 34 statements, 20 were rated as appropriate and 14 were rated as inappropriate. Uncertainty existed across all three domains: the initial assessment and management of HF-CS; escalation to temporary Mechanical Circulatory Support (tMCS); and weaning from tMCS in HF-CS. Significant disagreement between experts (deemed present when the disagreement index exceeded 1) was only identified when deliberating the utility of thoracic ultrasound in the immediate management of HF-CS. CONCLUSION: This study has highlighted several areas of practice where large-scale prospective registries and clinical trials in the HF-CS population are urgently needed to reliably inform clinical practice and the synthesis of future societal HF-CS guidelines.

Association of cardiovascular diseases with cognitive performance in older adults.

BACKGROUND: Cognitive function and cardiovascular disease (CVD) have a bidirectional relationship, but studies on the impact of CVD subtypes and aging spectrum have been scarce. METHODS: We assessed older adults aged ≥60 years from the 2011 to 2012 and 2013 to 2014 cycles of the National Health and Nutrition Examination Survey who had coronary heart disease, angina, prior myocardial infarction, congestive heart failure, or prior stroke. We compared CERAD-IR, CERAD-DR, Animal Fluency test, and DSST scores to assess cognitive performance in older adults with and without CVD. RESULTS: We included 3,131 older adults, representing 55,479,673 older adults at the national level. Older adults with CVD had lower CERAD-IR (mean difference 1.8, 95% CI 1.4-2.1, P < .001), CERAD-DR (mean difference 0.8, 95% CI 0.6-1.0, P < .001), Animal Fluency test (mean difference 2.1, 95% CI 1.6-2.6, P < .001), and DSST (mean difference 9.5, 95% CI 8.0-10.9, P < .001) scores compared with those without CVD. After adjustment, no difference in CERAD-IR, CERAD-DR, and Animal Fluency test scores was observed, but DSST scores were lower in older adults with CVD (adjusted mean difference 2.9, 95% CI 1.1-4.7, P = .001). Across CVD subtypes, individuals with congestive heart failure had lower performance on the DSST score. The oldest-old cohort of patients ≥80 years old with CVD had lower performance than those without CVD on both the DSST and Animal Fluency test. CONCLUSION: Older adults with CVD had lower cognitive performance as measured than those free of CVD, driven by pronounced differences among those with CHF and those ≥80 years old with CVD.

Study Protocol for the Artificial Intelligence-Driven Evaluation of Structural Heart Diseases Using Wearable Electrocardiogram (ID-SHD).

Introduction: Portable devices capable of electrocardiogram (ECG) acquisition have the potential to enhance structural heart disease (SHD) management by enabling early detection through artificial intelligence-ECG (AI-ECG) algorithms. However, the performance of these AI algorithms for identifying SHD in a real-world screening setting is unknown. To address this gap, we aim to evaluate the validity of our wearable-adapted AI algorithm, which has been previously developed and validated for detecting SHD from single-lead portable ECGs in patients undergoing routine echocardiograms in the Yale New Haven Hospital (YNHH). Research Methods and Analysis: This is the protocol for a cross-sectional study in the echocardiographic laboratories of YNHH. The study will enroll 585 patients referred for outpatient transthoracic echocardiogram (TTE) as part of their routine clinical care. Patients expressing interest in participating in the study will undergo a screening interview, followed by enrollment upon meeting eligibility criteria and providing informed consent. During their routine visit, patients will undergo a 1-lead ECG with two devices - one with an Apple Watch and the second with another portable 1-lead ECG device. With participant consent, these 1-lead ECG data will be linked to participant demographic and clinical data recorded in the YNHH electronic health records (EHR). The study will assess the performance of the AI-ECG algorithm in identifying SHD, including left ventricular systolic dysfunction (LVSD), valvular disease and severe left ventricular hypertrophy (LVH), by comparing the algorithm's results with data obtained from TTE, which is the established gold standard for diagnosing SHD. Ethics and Dissemination: All patient EHR data required for assessing eligibility and conducting the AI-ECG will be accessed through secure servers approved for protected health information. Data will be maintained on secure, encrypted servers for a minimum of five years after the publication of our findings in a peer-reviewed journal, and any unanticipated adverse events or risks will be reported by the principal investigator to the Yale Institutional Review Board, which has reviewed and approved this protocol (Protocol Number: 2000035532).

Circulating T cell specific extracellular vesicle profiles in cardiac allograft acute cellular rejection.

There is a critical need for biomarkers of acute cellular rejection (ACR) in organ transplantation. We hypothesized that ACR leads to changes in donor-reactive T cell small extracellular vesicle (sEV) profiles in transplant recipient circulation that match the kinetics of alloreactive T cell activation. In rodent heart transplantation, circulating T cell sEV quantities (P < .0001) and their protein and mRNA cargoes showed time-specific expression of alloreactive and regulatory markers heralding early ACR in allogeneic transplant recipients but not in syngeneic transplant recipients. Next generation sequencing of their microRNA cargoes identified novel candidate biomarkers of ACR, which were validated by stem loop quantitative reverse transcription polymerase chain reaction (n = 10). Circulating T cell sEVs enriched from allogeneic transplant recipients mediated targeted cytotoxicity of donor cardiomyocytes by apoptosis assay (P < .0001). Translation of the concept and EV methodologies to clinical heart transplantation demonstrated similar upregulation of circulating T cell sEV profiles at time points of grade 2 ACR (n = 3 patients). Furthermore, T cell receptor sequencing of T cell sEV mRNA cargo demonstrated expression of T cell clones with intact complementarity determining region 3 signals. These data support the diagnostic potential of T cell sEVs as noninvasive biomarker of ACR and suggest their potential functional roles.

A Multicenter Evaluation of the Impact of Therapies on Deep Learning-based Electrocardiographic Hypertrophic Cardiomyopathy Markers.

Background: Artificial intelligence-enhanced electrocardiography (AI-ECG) can identify hypertrophic cardiomyopathy (HCM) on 12-lead ECGs and offers a novel way to monitor treatment response. While the surgical or percutaneous reduction of the interventricular septum (SRT) represented initial HCM therapies, mavacamten offers an oral alternative. Objective: To evaluate biological response to SRT and mavacamten. Methods: We applied an AI-ECG model for HCM detection to ECG images from patients who underwent SRT across three sites: Yale New Haven Health System (YNHHS), Cleveland Clinic Foundation (CCF), and Atlantic Health System (AHS); and to ECG images from patients receiving mavacamten at YNHHS. Results: A total of 70 patients underwent SRT at YNHHS, 100 at CCF, and 145 at AHS. At YNHHS, there was no significant change in the AI-ECG HCM score before versus after SRT (pre-SRT: median 0.55 [IQR 0.24-0.77] vs post-SRT: 0.59 [0.40-0.75]). The AI-ECG HCM scores also did not improve post SRT at CCF (0.61 [0.32-0.79] vs 0.69 [0.52-0.79]) and AHS (0.52 [0.35-0.69] vs 0.61 [0.49-0.70]). Among 36 YNHHS patients on mavacamten therapy, the median AI-ECG score before starting mavacamten was 0.41 (0.22-0.77), which decreased significantly to 0.28 (0.11-0.50, p <0.001 by Wilcoxon signed-rank test) at the end of a median follow-up period of 237 days. Conclusions: The lack of improvement in AI-based HCM score with SRT, in contrast to a significant decrease with mavacamten, suggests the potential role of AI-ECG for serial monitoring of pathophysiological improvement in HCM at the point-of-care using ECG images.

Prognostic Impact of Repeated NT-proBNP Measurements in Patients With Heart Failure With Reduced Ejection Fraction.

BACKGROUND: Although clinical studies have demonstrated the association between a single N-terminal pro-B-type natriuretic peptide (NT-proBNP) measurement and clinical outcomes in chronic heart failure, the biomarker is frequently measured serially in clinical practice. OBJECTIVES: The aim of this study was to determine the added prognostic value of repeated NT-proBNP measurements compared with single measurements alone for chronic heart failure patients. METHODS: In the GUIDE-IT (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure) study, 894 study participants with chronic heart failure with reduced ejection fraction were enrolled at 45 outpatient sites in the United States and Canada. Repeated NT-proBNP levels were measured over a 2-year study period. Associations between repeated NT-proBNP measurements and trial endpoints were assessed using a joint longitudinal and survival model. RESULTS: After adjustment for baseline covariates, each doubling of the baseline NT-proBNP level was associated with a HR of 1.17 (95% CI: 1.08-1.28; P = 0.0003) for the primary trial endpoint of cardiovascular death or heart failure hospitalization. Serial measurements increased the adjusted HR for the primary trial endpoint to 1.66 (95% CI: 1.50-1.84; P < 0.0001), and a similar increased risk was observed across secondary trial endpoints. In joint modeling, an increase in NT-proBNP occurred weeks before the onset of adjudicated events. CONCLUSIONS: Repeated NT-proBNP measurements are a strong predictor of outcomes in heart failure with reduced ejection fraction with an increase in concentration occurring well before event onset. These results may support routine NT-proBNP monitoring to assist in clinical decision making. (Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure [GUIDE-IT]; NCT01685840).

Heart failure with reduced ejection fraction: clinical and economic burden and insights into current and emerging treatments.

Heart failure (HF) imposes a large and growing burden on the population, with a prevalence that is projected to increase to more than 8 million adults by 2030. The high risk of morbidity and mortality associated with HF is further exacerbated by the frequent presence of comorbidities. The coexistence of HF and comorbid conditions can result in emergency department visits and hospitalizations that not only affect patients and their families but also pose a growing economic burden on health care systems. The largest costs arise from hospitalization for HF, with outpatient care and associated medication costs comprising the second largest component. For patients with HF with reduced ejection fraction (HFrEF), defined as left ventricular ejection fraction of 40% or less, remarkable improvements in outcomes have been observed in recent decades due to the availability of disease-modifying therapies. However, the management of HFrEF remains suboptimal, with many patients either not receiving guideline-directed medical therapy (GDMT) or experiencing delays in receiving target doses. Since this can result in preventable hospitalizations and deaths, action is needed to ensure rapid initiation of GDMT. Optimal treatment can be hindered by such patient factors as the presence of comorbidities and socioeconomic barriers that include the cost of multiple treatments. Furthermore, poor treatment adherence is common among patients with HF. Measures aimed at tailoring therapies to individual patients and reducing medical costs are important to increase the uptake of and adherence to therapy and therefore improve clinical outcomes.

Automated Identification of Heart Failure with Reduced Ejection Fraction using Deep Learning-based Natural Language Processing.

Background: The lack of automated tools for measuring care quality has limited the implementation of a national program to assess and improve guideline-directed care in heart failure with reduced ejection fraction (HFrEF). A key challenge for constructing such a tool has been an accurate, accessible approach for identifying patients with HFrEF at hospital discharge, an opportunity to evaluate and improve the quality of care. Methods: We developed a novel deep learning-based language model for identifying patients with HFrEF from discharge summaries using a semi-supervised learning framework. For this purpose, hospitalizations with heart failure at Yale New Haven Hospital (YNHH) between 2015 to 2019 were labeled as HFrEF if the left ventricular ejection fraction was under 40% on antecedent echocardiography. The model was internally validated with model-based net reclassification improvement (NRI) assessed against chart-based diagnosis codes. We externally validated the model on discharge summaries from hospitalizations with heart failure at Northwestern Medicine, community hospitals of Yale New Haven Health in Connecticut and Rhode Island, and the publicly accessible MIMIC-III database, confirmed with chart abstraction. Results: A total of 13,251 notes from 5,392 unique individuals (mean age 73 ± 14 years, 48% female), including 2,487 patients with HFrEF (46.1%), were used for model development (train/held-out test: 70/30%). The deep learning model achieved an area under receiving operating characteristic (AUROC) of 0.97 and an area under precision-recall curve (AUPRC) of 0.97 in detecting HFrEF on the held-out set. In external validation, the model had high performance in identifying HFrEF from discharge summaries with AUROC 0.94 and AUPRC 0.91 on 19,242 notes from Northwestern Medicine, AUROC 0.95 and AUPRC 0.96 on 139 manually abstracted notes from Yale community hospitals, and AUROC 0.91 and AUPRC 0.92 on 146 manually reviewed notes at MIMIC-III. Model-based prediction of HFrEF corresponded to an overall NRI of 60.2 ± 1.9% compared with the chart diagnosis codes (p-value < 0.001) and an increase in AUROC from 0.61 [95% CI: 060-0.63] to 0.91 [95% CI 0.90-0.92]. Conclusions: We developed and externally validated a deep learning language model that automatically identifies HFrEF from clinical notes with high precision and accuracy, representing a key element in automating quality assessment and improvement for individuals with HFrEF.

Electronic health record alerts for management of heart failure with reduced ejection fraction in hospitalized patients: the PROMPT-AHF trial.

BACKGROUND AND AIMS: Patients hospitalized for acute heart failure (AHF) continue to be discharged on an inadequate number of guideline-directed medical therapies (GDMT) despite evidence that inpatient initiation is beneficial. This study aimed to examine whether a tailored electronic health record (EHR) alert increased rates of GDMT prescription at discharge in eligible patients hospitalized for AHF. METHODS: Pragmatic trial of messaging to providers about treatment of acute heart failure (PROMPT-AHF) was a pragmatic, multicenter, EHR-based, and randomized clinical trial. Patients were automatically enrolled 48 h after admission if they met pre-specified criteria for an AHF hospitalization. Providers of patients in the intervention arm received an alert during order entry with relevant patient characteristics along with individualized GDMT recommendations with links to an order set. The primary outcome was an increase in the number of GDMT prescriptions at discharge. RESULTS: Thousand and twelve patients were enrolled between May 2021 and November 2022. The median age was 74 years; 26% were female, and 24% were Black. At the time of the alert, 85% of patients were on ß-blockers, 55% on angiotensin-converting enzyme inhibitor/angiotensin receptor blocker/angiotensin receptor-neprilysin inhibitor, 20% on mineralocorticoid receptor antagonist (MRA) and 17% on sodium-glucose cotransporter 2 inhibitor. The primary outcome occurred in 34% of both the alert and no alert groups [adjusted risk ratio (RR): 0.95 (0.81, 1.12), P = .99]. Patients randomized to the alert arm were more likely to have an increase in MRA [adjusted RR: 1.54 (1.10, 2.16), P = .01]. At the time of discharge, 11.2% of patients were on all four pillars of GDMT. CONCLUSIONS: A real-time, targeted, and tailored EHR-based alert system for AHF did not lead to a higher number of overall GDMT prescriptions at discharge. Further refinement and improvement of such alerts and changes to clinician incentives are needed to overcome barriers to the implementation of GDMT during hospitalizations for AHF. GDMT remains suboptimal in this setting, with only one in nine patients being discharged on a comprehensive evidence-based regimen for heart failure.

Multicenter study of universal prophylaxis versus pre-emptive therapy for patients at intermediate risk (R+) for CMV following heart transplantation.

INTRODUCTION: Heart transplant (HT) recipients with prior exposure to cytomegalovirus (CMV R+) are considered intermediate risk for CMV-related complications. Consensus guidelines allow for either universal prophylaxis (UP) or preemptive therapy (PET) (serial CMV testing) approaches to CMV prevention in such patients. Whether an optimal approach to mitigate CMV related risks exists in this setting remains uncertain. We therefore assessed the utility of PET as compared to UP in CMV R+ HT recipients. METHODS: Retrospective analysis of all CMV R+ HT recipients from 6 U.S. centers between 2010 and 2018 was performed. The primary outcome was the development of CMV DNAemia or end-organ disease resulting in the initiation/escalation of anti-CMV therapy. The secondary outcome was CMV-related hospitalization. Additional outcomes included incidence of acute cellular rejection (ACR) ≥ grade 2R, death, cardiac allograft vasculopathy (CAV), and leukopenia. RESULTS: Of 563 CMV R+ HT recipients, 344 (61.1%) received UP. PET was associated with increased risk for the primary (adjusted HR 3.95, 95% CI: 2.65-5.88, p < .001) and secondary (adjusted HR 3.19, 95% CI: 1.47-6.94, p = .004) outcomes, and with increased ACR ≥ grade 2R (PET 59.4% vs. UP 34.4%, p < .001). Incidence of detectable CAV was similar at 1 year (PET 8.2% vs. UP 9.5%, p = .698). UP was associated with increased incidence of leukopenia within 6 months post-HT (PET 34.7% vs. UP 43.6%, p = .036). CONCLUSION: The use of a PET CMV prophylaxis strategy in intermediate risk HT recipients associated with increased risk of CMV infection and CMV-related hospitalization, and may associate with worse post-HT graft outcomes.

Trends and Outcomes in Cardiac Arrest Among Heart Failure Admissions.

There is limited large, national data investigating the prevalence, characteristics, and outcomes of cardiac arrest (CA) in patients hospitalized for heart failure (HF). The goal of this study was to examine the characteristics, trends, and outcomes of HF hospitalizations complicated by in-hospital CA. We used the National Inpatient Sample to identify all primary HF admissions from 2016 to 2019. Cohorts were built based on the presence of a codiagnosis of CA. Diagnoses were identified using International Classification of Diseases, Tenth Revision, Clinical Modification codes. Associations with CA were then analyzed using multivariate logistic regression. We identified a total of 4,905,564 HF admissions, 56,170 (1.1%) of which had CA. Hospitalizations complicated by CA were significantly more likely to be male, to have coronary artery disease, renal disease, and less likely to be White (p <0.001, all). Age <65 (odds ratio [OR] 1.18, p <0.001), renal disease (OR 2.41, p <0.001), and coronary artery disease (OR 1.26, p <0.001) had higher odds of CA while female gender (OR 0.84, confidence interval [CI] 0.83 to 0.86, p <0.001) or HFpEF (OR 0.49, CI 0.48 to 0.50, p <0.001) had lower odds of CA. Patients with CA had higher inpatient mortality (CA 54.2% vs no CA 2.1%, p <0.001), which persisted after multivariate adjustment (OR 64.8, CI 63.5 to 66.0, p <0.001). CA occurs in >1 in 1,000 HF hospitalizations and remains a prominent and serious event associated with a high mortality. Further research is needed to examine long-term outcomes and mechanical circulatory support utilization with more granularity in HF patients with in-hospital CA.

Network Meta-Analysis Comparing Angiotensin Receptor-Neprilysin Inhibitors, Angiotensin Receptor Blockers, and Angiotensin-Converting Enzyme Inhibitors in Heart Failure With Reduced Ejection Fraction.

The superiority of angiotensin receptor-neprilysin inhibitor (ARNI) over angiotensin-converting enzyme inhibitor (ACE-I) and angiotensin receptor blocker (ARB) has not been reassessed after the publication of recent trials that did not find clinical benefits. Therefore, we performed an updated network meta-analysis comparing the efficacy and safety of ARNI, ACE-I, ARB, and placebo in heart failure with reduced ejection fraction. We included randomized clinical trials that compared ARNI, ARB, ACE-I, and placebo in heart failure with reduced ejection fraction. We extracted prespecified efficacy end points and produced network estimates, p scores, and surface under the cumulative ranking curve scores using frequentist and Bayesian network meta-analysis approaches. A total of 28 randomized controlled trials including 47,407 patients were included. ARNI was associated with lower risk of all-cause mortality (relative risk [RR] 0.81, 95% confidence interval [CI] 0.68 to 0.96), cardiac death (RR 0.79, 95% CI 0.64 to 0.99), and major adverse cardiac events (MACEs; RR 0.83, 95% CI 0.72 to 0.97) but higher risk of hypotension (RR 1.46, 95% CI 1.02 to 2.10) than ARB. ARNI was associated with lower risk of MACE (RR 0.85, 95% CI 0.74 to 0.97), but higher risk of hypotension (RR 1.69, 95% CI 1.27 to 2.24) compared with ACE-I. P scores and surface under the cumulative ranking curve scores demonstrated superiority of ARNI over ARB and ACE-I in all-cause mortality, cardiac death, MACE, and hospitalization for heart failure. In conclusion, ARNI was associated with improved clinical outcomes, except for higher risk of hypotension, compared with ARB and ACE-I.

Pragmatic trial of messaging to providers about treatment of acute heart failure: The PROMPT-AHF trial.

Acute Heart failure (AHF) is among the most frequent causes of hospitalization in the United States, contributing to substantial health care costs, morbidity, and mortality. Inpatient initiation of guideline-directed medical therapy (GDMT) is recommended for patients with heart failure with reduced ejection fraction (HFrEF) to reduce the risk of cardiovascular death or HF hospitalization. However, underutilization of GDMT prior to discharge is pervasive, representing a valuable missed opportunity to optimize evidence-based care. The PRagmatic Trial Of Messaging to Providers about Treatment of Acute Heart Failure tests the effectiveness of an electronic health record embedded clinical decision support system that informs providers during hospital management about indicated but not yet prescribed GDMT for eligible AHF patients with HFrEF. PRagmatic Trial Of Messaging to Providers about Treatment of Acute Heart Failureis an open-label, multicenter, pragmatic randomized controlled trial of 1,012 patients hospitalized with HFrEF. Eligible patients randomized to the intervention group are exposed to a tailored best practice advisory embedded within the electronic health record that alerts providers to prescribe omitted GDMT. The primary outcome is an increase in the proportion of additional GDMT medication classes prescribed at the time of discharge compared to those in the usual care arm.

Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning.

Background: Hypertrophic cardiomyopathy (HCM) affects 1 in every 200 individuals and is the leading cause of sudden cardiac death in young adults. HCM can be identified using an electrocardiogram (ECG) raw voltage data and deep learning approaches, but their point-of-care application is limited by the inaccessibility of these signal data. We developed a deep learning-based approach that overcomes this limitation and detects HCM from images of 12-lead ECGs across layouts. Methods: We identified ECGs from patients with HCM features present on cardiac magnetic resonance imaging (CMR) or those within 30 days of an echocardiogram documenting thickened interventricular septum (end-diastolic interventricular septum thickness > 15mm). Patients with CMR-confirmed HCM were considered as cases during the final model evaluation. The model was validated within clinical settings at YNHH and externally on ECG images from the prospective, population-based UK Biobank cohort. We localized class-discriminating signals in ECG images using gradient-weighted class activation mapping. Results: Overall, 124,553 ECGs from 66,987 individuals (HCM cases and controls) were used for model development. The model demonstrated high discrimination for HCM across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROC] 0.96) and external sets of ECG images from UK Biobank (AUROC 0.94). A positive screen for HCM was associated with a 100-fold higher odds of CMR-confirmed HCM (OR 102.4, 95% Confidence Interval, 57.4 - 182.6) in the held-out set. Class-discriminative patterns localized to the anterior and lateral leads (V4-V5). Conclusions: We developed and externally validated a deep learning model that identifies HCM from ECG images with excellent discrimination. This approach represents an automated, efficient, and accessible screening strategy for HCM.

Clinical implications of Type 2 diabetes on outcomes after cardiac transplantation.

BACKGROUND: T2D is an increasingly common disease that is associated with worse outcomes in patients with heart failure. Despite this, no contemporary study has assessed its impact on heart transplantation outcomes. This paper examines the demographics and outcomes of patients with type 2 diabetes (T2D) undergoing heart transplantation. METHODS: Using the United Network for Organ Sharing (UNOS) database, patients listed for transplant were separated into cohorts based on history of T2D. Demographics and comorbidities were compared, and cox regressions were used to examine outcomes. RESULTS: Between January 1st, 2011 and June 12th, 2020, we identified 9,086 patients with T2D and 23,676 without T2D listed for transplant. The proportion of patients with T2D increased from 25.2% to 27.9% between 2011 and 2020. Patients with T2D were older, more likely to be male, less likely to be White, and more likely to pay with public insurance (p<0.001, all). After adjustment, T2D patients had a lower likelihood of transplantation (Hazard Ratio [HR]: 0.93, CI: 0.90-0.96, p<0.001) and a higher likelihood of post-transplant mortality (HR: 1.30, CI: 1.20-1.40, p<0.001). Patients with T2D were more likely to be transplanted in the new allocation system compared to the old allocation system (all, p<0.001). CONCLUSIONS: Over the last ten years, the proportion of heart transplant recipients with T2D has increased. These patients are more likely to be from traditionally underserved populations. Patients with T2D have a lower likelihood of transplantation and a higher likelihood of post-transplant mortality. After the allocation system change, likelihood of transplantation has improved for patients with T2D.

Impact of Obesity on Heart Transplantation Outcomes.

Background Patients with obesity and advanced heart failure face unique challenges on the path to heart transplantation. There are limited data on waitlist and transplantation outcomes in this population. We aimed to evaluate the impact of obesity on heart transplantation outcomes, and to investigate the effects of the new organ procurement and transplantation network allocation system in this population. Methods and Results This cohort study of adult patients listed for heart transplant used the United Network for Organ Sharing database from January 2006 to June 2020. Patients were stratified by body mass index (BMI) (18.5-24.9, 25-29.9, 30-34.9, 35-39.9, and 40-55 kg/m2). Recipient characteristics and donor characteristics were analyzed. Outcomes analyzed included transplantation, waitlist death, and posttransplant death. BMI 18.5 to 24.9 kg/m2 was used as the reference compared with progressive BMI categories. There were 46 645 patients listed for transplantation. Patients in higher BMI categories were less likely to be transplanted. The lowest likelihood of transplantation was in the highest BMI category, 40 to 55 kg/m2 (hazard ratio [HR], 0.19 [0.05-0.76]; P=0.02). Patients within the 2 highest BMI categories had higher risk of posttransplantation death (HR, 1.29; P<0.001 and HR, 1.65; P<0.001, respectively). Left ventricular assist devices among patients in obese BMI categories decreased after the allocation system change (P<0.001, all). After the change, patients with obesity were more likely to undergo transplantation (BMI 30-35 kg/m2: HR, 1.31 [1.18-1.46], P<0.001; BMI 35-55 kg/m2: HR, 1.29 [1.06-1.58]; P=0.01). Conclusions There was an inverse relationship between BMI and likelihood of heart transplantation. Higher BMI was associated with increased risk of posttransplant mortality. Patients with obesity were more likely to undergo transplantation under the revised allocation system.