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Frailty is common among older patients with heart failure (HF). The efficacy of coronary artery bypass grafting (CABG) on the risk of mortality among frail patients with ischemic cardiomyopathy and HF is uncertain, and whether frailty burden modifies the treatment benefits of CABG among these patients is unknown. We performed a post hoc analysis of the STICHES trial, a randomized trial of CABG with medical therapy vs medical therapy alone among participants with ischemic cardiomyopathy with ejection fraction ≤ 35%. Baseline frailty was assessed through a Rockwood Frailty Index (FI), and based on FI cut-offs from prior HF studies, participants with FI ≥ 0.311 were classified as more frail, and those with FI < 0.311 were classified as less frail. A multivariable Cox proportional hazard model with multiplicative interaction terms was constructed to evaluate whether frailty status modified the treatment effect of CABG on mortality in the overall trial cohort and among those < 60 vs ≥ 60 years of age. Of 1187 participants (12.4% female, 2.6% Black, median FI = 0.33 [IQR 0.27-0.39]), 678 were characterized as more frail. Frailty burden did not modify the efficacy of CABG on the risk of all-cause death in the overall cohort (P int CABG × frailty = 0.2). In age stratified analysis, Baseline frailty status did not modify the treatment effect of CABG on the risk of all-cause mortality among younger (< 60 years, P int CABG × frailty = 0.2) as well as older participants (≥60 years, P int CABG × frailty = 0.6). In this post hoc analysis of the STICHES trial, baseline frailty status did not modify the efficacy of CABG in the overall cohort as well as among younger or older participants. Frailty alone should not be used as a criterion to determine the utilization of CABG among patients with ischemic cardiomyopathy., (© 2024. The Author(s), under exclusive licence to American Aging Association.)

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Aims: Abnormalities in specific echocardiographic parameters and cardiac biomarkers have been reported among individuals with diabetes. However, a comprehensive characterization of diabetic cardiomyopathy (DbCM), a subclinical stage of myocardial abnormalities that precede the development of clinical heart failure (HF), is lacking. In this study, we developed and validated a machine learning-based clustering approach to identify the high-risk DbCM phenotype based on echocardiographic and cardiac biomarker parameters., Methods and Results: Among individuals with diabetes from the Atherosclerosis Risk in Communities (ARIC) cohort who were free of cardiovascular disease and other potential aetiologies of cardiomyopathy (training, n = 1199), unsupervised hierarchical clustering was performed using echocardiographic parameters and cardiac biomarkers of neurohormonal stress and chronic myocardial injury (total 25 variables). The high-risk DbCM phenotype was identified based on the incidence of HF on follow-up. A deep neural network (DeepNN) classifier was developed to predict DbCM in the ARIC training cohort and validated in an external community-based cohort (Cardiovascular Health Study [CHS]; n = 802) and an electronic health record (EHR) cohort (n = 5071). Clustering identified three phenogroups in the derivation cohort. Phenogroup-3 (n = 324, 27% of the cohort) had significantly higher 5-year HF incidence than other phenogroups (12.1% vs. 4.6% [phenogroup 2] vs. 3.1% [phenogroup 1]) and was identified as the high-risk DbCM phenotype. The key echocardiographic predictors of high-risk DbCM phenotype were higher NT-proBNP levels, increased left ventricular mass and left atrial size, and worse diastolic function. In the CHS and University of Texas (UT) Southwestern EHR validation cohorts, the DeepNN classifier identified 16% and 29% of participants with DbCM, respectively. Participants with (vs. without) high-risk DbCM phenotype in the external validation cohorts had a significantly higher incidence of HF (hazard ratio [95% confidence interval] 1.61 [1.18-2.19] in CHS and 1.34 [1.08-1.65] in the UT Southwestern EHR cohort)., Conclusion: Machine learning-based techniques may identify 16% to 29% of individuals with diabetes as having a high-risk DbCM phenotype who may benefit from more aggressive implementation of HF preventive strategies., (© 2024 The Author(s). European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Cardiovascular disease is the leading cause of death among women in the United States. Past research has highlighted the importance of the relationship between female-specific demographics and traditional risk factors. The present analysis aimed to identify the prevalence of modifiable risk factors in women attending a community cardiovascular health screening., Methods: Data collected between 2011 and 2019 were obtained from the Houston HeartReach Registry. Participants were classified as having or not having each of 4 traditional cardiometabolic risk factors: hypertension, diabetes, body mass index indicating overweight or obesity, and dyslipidemia. Differences in prevalence were compared using the Pearson χ2 test., Results: Most participants had hypertension, overweight or obesity, and dyslipidemia. Older women (≥65 years) had the highest prevalence of all cardiometabolic risk factors. Black participants had a higher prevalence of hypertension (P = .006) and a lower prevalence of dyslipidemia (P = .009) than non-Black participants. Hispanic participants had a lower prevalence of hypertension (P < .001) and a higher prevalence of overweight or obesity (P = .03) than non-Hispanic participants. Participants in the lowest household income bracket (<$25,000) were more likely to have diabetes (P = .001) and overweight or obesity (P = .004) than participants in the highest income bracket (≥$50,000). Unemployed participants had a higher prevalence of diabetes (P < .001), overweight or obesity (P = .004), and dyslipidemia (P < .001) than employed participants. Comorbidity analysis revealed clustering of multiple cardiometabolic risk factors. Moreover, risk factor hot spots were identified by zip code, which could help select future sites for targeted screening., Conclusion: The analysis found that cardiometabolic risk factor prevalence varies with demographic and socioeconomic status. Geographic areas where cardiometabolic risk factor prevalence was highest were also identified. Further participant recruitment and analysis are required to create predictive models of cardiovascular disease risk in women., (© 2024 The Authors. Published by The Texas Heart Institute®.)

Background: Sodium glucose cotransporter 2 inhibitors (SGLT2i) prevent heart failure (HF) in patients with type 2 diabetes mellitus (T2DM) but prescription rates are low. The effect of an electronic health record (EHR) alert notifying providers of patients' estimated risk of developing HF on SGTL2i prescriptions is unknown., Methods: This was a pragmatic, randomized clinical trial that compared an EHR alert and usual care among patients with T2DM and no history of HF or SGLT2i use at a single center. The EHR alert notified providers of their patient's HF risk and recommended HF prevention strategies. Randomization was performed at the provider level across general and subspecialty internal medicine as well as family medicine outpatient clinics. The primary outcome was proportion of SGLT2i prescriptions within 30 days. Proportion of natriuretic peptide (NP) tests within 90 days was also assessed., Results: A total of 1524 patients (median age 75 years, 45% women, 23% Black) were enrolled between September 28, 2021, and April 29, 2022 from 189 outpatient clinics. SGLT2i were prescribed to 1.2% (9/780) of patients in the EHR alert group and 0% (0/744) of those in the usual care group ( P value = 0.009). Natriuretic peptide testing was performed within 90 days among 10.8% (84/780) of patients in the EHR alert group and 7.3% (54/744) of patients in the usual care group ( P value = 0.02)., Conclusions: In a single-center trial with low overall SGLT2i use, an EHR alert incorporating HF risk information significantly increased SGLT2i prescriptions and NP testing although the absolute rates were low., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MWS has received personal fees from Merck and serves on the advisory board at descendants DNA. KVP has received consultant fees from NovoNordisk. NK has received consultant fees from HeartSciences and Tricog Health. AP received grant funding outside this study from National Institute of Health, Gilead Sciences, Ultromics, Roche Diagnostics, and Applied Therapeutics; has received honoraria outside of this study as an advisor/consultant for Tricog Health Inc, Lilly, USA, Rivus, Roche Diagnostics, Axon Therapies, Semler Scientific, Medtronic, Edwards Lifesciences, Science37, Novo Nordisk, Bayer, Merck, Sarfez Pharamcuticals, Emmi Solutions, Palomarin Inc (Stocks compensation) and has received nonfinancial support from Pfizer and Merck. All other authors declare no competing interests.

Background: Left ventricular assist device (LVAD) recipients have a higher incidence of ventricular tachycardia (VT). However, the role of VT ablation in this population is not well-established., Objectives: This single-center retrospective cohort study sought to examine the impact of post-LVAD implant VT ablation on survival., Methods: This retrospective study examined a cohort of patients that underwent LVAD implantation at Baylor St. Luke's Medical Center and Texas Heart Institute between January 2011 and January 2021. All-cause estimated mortality was compared across LVAD recipients based on the incidence of VT, timing of VT onset, and the occurrence and timing of VT ablation utilizing Kaplan-Meier survival analysis and Cox proportional hazards models., Results: Post-implant VT occurred in 53% of 575 LVAD recipients. Higher mortality was seen among patients with post-implant VT within a year of implantation (HR: 1.62 [95% CI: 1.15-2.27]). Among this cohort, patients who were treated with a catheter ablation had superior survival compared with patients treated with medical therapy alone for the 45 months following VT onset (HR: 0.48 [95% CI: 0.26-0.89]). Moreover, performance of an ablation in this population aligned mortality rates with those who did not experience post-implant VT (HR: 1.18 [95% CI: 0.71-1.98])., Conclusions: VT occurrence within 1 year of LVAD implantation was associated with worse survival. However, performance of VT ablation in this population was correlated with improved survival compared with medical management alone. Among patients with refractory VT, catheter ablation aligned survival with other LVAD participants without post-implant VT. Catheter ablation of VT is associated with improved survival in LVAD recipients, but further prospective randomized studies are needed to compare VT ablation to medical management in LVAD recipients., Competing Interests: Funding Support and Author Disclosures Dr Chelu has received funding from Patient-Centered Outcomes Research Institute (PLACER grant 2021C3-24160). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: High-density lipoprotein (HDL) is commonly characterized by its cholesterol concentration (HDL-C) and inverse association with atherosclerotic cardiovascular disease., Objectives: The authors sought to evaluate the association of HDL particle concentration (HDL-P), HDL particle size (HDL-size), HDL-C, and cholesterol content per particle (HDL-C/HDL-P) with risk of overall heart failure (HF) and subtypes., Methods: Participants from the Atherosclerosis Risk In Communities Study, Dallas Heart Study, Multi-Ethnic Study of Atherosclerosis, and Prevention of Renal and Vascular End-stage Disease studies without HF history were included. Associations of HDL-P, HDL-size, HDL-C, and HDL-C/HDL-P with risk of overall HF, HF with reduced and preserved ejection fraction were assessed using adjusted Cox models., Results: Among 16,925 participants (53.5% women; 21.8% Black), there were 612 incident HF events (3.6%) (HF with reduced ejection fraction, 309 [50.5%]; HF preserved ejection fraction, 303 [49.5%]) over median follow-up of 11.4 years. In adjusted models, higher HDL-P was significantly associated with lower HF risk (HR of highest vs lowest tertile of HDL-P: 0.76 [95% CI: 0.62-0.93]). Larger HDL-size was significantly associated with higher overall HF risk (HR of largest vs smallest tertile of HDL-size: 1.27 [95% CI: 1.03-1.58]). HF risk associated with HDL-P and HDL-size was similar for HF subtypes. In adjusted analyses, there was no significant association between HDL-C and HF risk. Higher HDL-C/HDL-P was significantly associated with higher overall HF risk (HR of highest vs lowest tertile of HDL-C/HDL-P: 1.29 [95% CI: 1.04-1.60])., Conclusions: Higher HDL-P was associated with a lower risk of HF. In contrast, larger HDL-size was associated with higher risk of HF and there was no significant association observed between HDL-C and HF risk after accounting for cardiovascular risk factors., Competing Interests: Funding Support and Author Disclosures The ARIC study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contracts HHSN268201700001I, HHSN268201700002I, HHSN268201700003I, HHSN268201700005I, and HHSN268201700004I. The Dallas Heart Study was funded by a grant from the Donald W. Reynolds Foundation. The Multi-Ethnic Study of Atherosclerosis was supported by the National Heart, Lung, and Blood Institute (R01 HL071739 and contracts N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, and N01-C-95169). The PREVEND cohort study was supported by The Dutch Kidney Foundation which supported the infrastructure of the PREVEND program from 1997 to 2003 (Grant E.033). The University Medical Center Groningen supported the infrastructure from 2003 to 2006. Dr Pandey is supported by the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, the National Institute of Aging GEMSSTAR Grant (1R03AG067960-01), and Applied Therapeutics; has served on the advisory board for Roche Diagnostics; and has received nonfinancial support from Pfizer and Merck. Dr Patel has served as a consultant to Novo Nordisk. Dr Segar has received speaker fees from Merck and is on the advisory board of descendantsDNA. Dr Shapiro has received grants from Amgen, Boehringer Ingelheim, 89Bio, Esperion, Novartis, Ionis, Merck, and New Amsterdam; has served on scientific advisory boards for Amgen, Agepha, Ionis, Novartis, Precision BioScience, New Amsterdam, and Merck; and has served as a consultant to Ionis, Novartis, Regeneron, Aidoc, Shanghai Pharma Biotherapeutics, Kaneka, and Novo Nordisk. Dr Connelly is an employee of Labcorp. Dr Rohatgi has received a research grant from CSL Behring; has served as a collaborator to Quest; has served as a consultant to HDL Diagnostics, JP Morgan, Johnson and Johnson, and Raydel. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Background: Excess muscle fat is observed in obesity and associated with greater burden of cardiovascular risk factors and higher risk of mortality. Liraglutide reduces total body weight and visceral fat but its effect on muscle fat and adverse muscle composition is unknown., Methods: This is a pre-specified secondary analysis of a randomized, double-blind, placebo-controlled trial that examined the effects of liraglutide plus a lifestyle intervention on visceral adipose tissue and ectopic fat among adults without diabetes with body mass index ≥30 kg/m 2 or ≥27 kg/m 2 and metabolic syndrome. Participants were randomly assigned to a once-daily subcutaneous injection of liraglutide (target dose 3.0 mg) or matching placebo for 40 weeks. Body fat distribution and muscle composition was assessed by magnetic resonance imaging at baseline and 40-week follow-up. Muscle composition was described by the combination of thigh muscle fat and muscle volume. Treatment difference (95% confidence intervals [CI]) was calculated by least-square means adjusted for baseline thigh muscle fat. The association between changes in thigh muscle fat and changes in body weight were assessed using Spearman correlation coefficients. The effect of liraglutide versus placebo on adverse muscle composition, denoted by high thigh muscle fat and low thigh muscle volume, was explored., Results: Among the 128 participants with follow-up imaging (92.2% women, 36.7% Black), median muscle fat at baseline was 7.8%. The mean percent change in thigh muscle fat over median follow-up of 36 weeks was -2.87% among participants randomized to liraglutide (n = 73) and 0.05% in the placebo group (absolute change: -0.23% vs. 0.01%). The estimated treatment difference adjusted for baseline thigh muscle fat was -0.24% (95% CI, -0.41 to -0.06, P-value 0.009). Longitudinal change in thigh muscle fat was significantly associated with change in body weight in the placebo group but not the liraglutide group. The proportion of participants with adverse muscle composition decreased from 11.0% to 8.2% over follow-up with liraglutide, but there was no change with placebo., Conclusions: In a cohort of predominantly women with overweight or obesity in the absence of diabetes, once-daily subcutaneous liraglutide was associated with a reduction in thigh muscle fat and adverse muscle composition compared with placebo. The contribution of muscle fat improvement to the cardiometabolic benefits of liraglutide requires further study., (© 2024 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)

Patients with type 2 diabetes face an elevated risk of cardiovascular disease. This review centers on sodium-glucose cotransporter-2 (SGLT2) inhibitors, a class of drugs that, according to a growing body of evidence, may have major potential for managing cardiovascular disease in patients with type 2 diabetes. This review presents findings from multiple clinical trials suggesting that SGLT2 inhibitors can not only serve as preventive therapeutic agents but also play a role in the active management of heart failure. The discussion includes the mechanism of action of SGLT2 inhibitors, emphasizing that they enhance urinary glucose excretion, which could lead to improved glycemic control and contribute to metabolic shifts beneficial to cardiac function. Alongside these cardiometabolic effects, safety concerns and practical considerations for prescribing these agents are addressed, taking into account potential adverse effects such as genitourinary infections and diabetic ketoacidosis as well as the financial implications for patients. Despite these drawbacks, therapeutic indications for SGLT2 inhibitors continue to expand, including for kidney protection, although further research is necessary to fully understand the mechanisms driving the cardioprotective and kidney-protective effects of SGLT2 inhibitors. By synthesizing current knowledge, this review intends to inform and guide clinical decision-making, thereby enhancing cardiovascular disease outcomes in patients with type 2 diabetes., (© 2024 The Authors. Published by The Texas Heart Institute®.)

Patients with heart failure with preserved ejection fraction (HFpEF) often receive β-blocker (BB) therapy for management of co-morbidities. However, the association of BB therapy with exercise capacity and health-related quality of life (HRQL) in HFpEF is not well-studied. In this post hoc analysis of the Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in HFpEF (RELAX) trial, which included patients with chronic stable HFpEF with peak exercise capacity assessment at baseline and at 12 and 24 weeks of follow-up, we evaluated the association of BB use with the measures of exercise capacity (peak exercise oxygen uptake), anaerobic threshold, and HRQL (Minnesota living with heart failure questionnaire). Separate linear mixed-effect models were constructed for each outcome with adjustment for treatment arm, demographics, medical history, left ventricular ejection fraction, and duration of heart failure. Of the 216 study participants (median age 69 years, 48.2% women), 76% reported BB use at baseline. Participants with (vs without) BB therapy were older (70 vs 63.5 years, p = 0.001) and had a higher prevalence of ischemic heart disease (44% vs 23%, p = 0.01). In the adjusted linear mixed model, BB use over time was not associated with peak exercise oxygen uptake (β 95% confidence interval [CI] 0.2 (-0.31 to 0.7), p = 0.5) and 6-minute walk distance (β 95% CI 14.69 [-14.25 to 43.63], p = 0.3). However, BB use was associated with a higher anaerobic threshold (β 95% CI 0.32 (0.02 to 0.62), p = 0.036) and better HRQL (lower quality of life as assessed by Minnesota living with heart failure questionnaire score) (β 95% CI -6.68 [-10.96 to -2.4], p = 0.002). Future trials are needed to better evaluate the effects of BB on exercise capacity in patients with chronic stable HFpEF., Competing Interests: Declaration of competing interest Dr. Chandra has received grant funding outside the present study from August Health. Dr. Pandey has received grant funding outside the present study from Applied Therapeutics and Gilead Sciences; has received honoraria outside the present study as an advisor/consultant for Tricog Health Inc and Lilly, Rivus, and Roche Diagnostics; and has received nonfinancial support from Pfizer and Merck. Dr. Segar reports speaker fees from Merck & Co and grant support from the American Heart Association outside the present study. The remaining authors have no competing interest to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Implantable cardioverter-defibrillator (ICD) therapy is recommended to reduce mortality risk in patients with heart failure with reduced ejection fraction (HFrEF). Frailty is common among patients with HFrEF and is associated with increased mortality risk. Whether the therapeutic efficacy of ICD is consistent among frail and nonfrail patients with HFrEF remains unclear., Objectives: The aim of this study was to evaluate the effect modification of baseline frailty burden on ICD efficacy for primary prevention among participants of the SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial)., Methods: Participants in SCD-HeFT with HFrEF randomized to ICD vs placebo were included. Baseline frailty was estimated using the Rockwood Frailty Index (FI), and participants were stratified into high (FI > median) vs low (FI ≤ median) frailty burden groups. Multivariable Cox models with multiplicative interaction terms (frailty × treatment arm) were constructed to evaluate whether baseline frailty status modified the treatment effect of ICD for all-cause mortality., Results: The study included 1,676 participants (mean age: 59 ± 12 years, 23% women) with a median FI of 0.30 (IQR: 0.23-0.37) in the low frailty group and 0.54 (IQR: 0.47-0.60) in the high frailty group. In adjusted Cox models, baseline frailty status significantly modified the treatment effect of ICD therapy (P interaction  = 0.047). In separate stratified analysis by frailty status, ICD therapy was associated with a lower risk of all-cause mortality among participants with low frailty burden (HR: 0.56; 95% CI: 0.40-0.78) but not among those with high frailty burden (HR: 0.86; 95% CI: 0.68-1.09)., Conclusions: Baseline frailty modified the efficacy of ICD therapy with a significant mortality benefit observed among participants with HFrEF and a low frailty burden but not among those with a high frailty burden., Competing Interests: Funding Support and Author Disclosures Dr Pandey is supported in part by the National Institute on Aging GEMSSTAR Grant (1R03AG067960-01) and the National Institute on Minority Health and Disparities (R01MD017529). Dr Kitzman has received honoraria outside the present study as a consultant for Boehringer-Ingelheim, NovoNordisk, AstraZeneca, Rivus, Keyto, Pfizer, and Novartis; has received grant funding outside the present study from Novartis, Bayer, NovoNordisk, Pfizer, and AstraZeneca; and has stock ownership in Gilead Sciences. Dr Pandey has received grant funding outside the present study from Applied Therapeutics and Gilead Sciences; has received honoraria outside the present study as an advisor/consultant for Tricog Health Inc and Lilly, USA, Rivus, and Roche Diagnostics; and has received nonfinancial support from Pfizer and Merck. The other authors report no conflicts. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Background: Individuals with acute decompensated heart failure (ADHF) have a varying response to diuretic therapy. Strategies for the early identification of low diuretic efficiency to inform decongestion therapies are lacking., Objectives: The authors sought to develop and externally validate a machine learning-based phenomapping approach and integer-based diuresis score to identify patients with low diuretic efficiency., Methods: Participants with ADHF from ROSE-AHF, CARRESS-HF, and ATHENA-HF were pooled in the derivation cohort (n = 794). Multivariable finite-mixture model-based phenomapping was performed to identify phenogroups based on diuretic efficiency (urine output over the first 72 hours per total intravenous furosemide equivalent loop diuretic dose). Phenogroups were externally validated in other pooled ADHF trials (DOSE/ESCAPE). An integer-based diuresis score (BAN-ADHF score: blood urea nitrogen, creatinine, natriuretic peptide levels, atrial fibrillation, diastolic blood pressure, hypertension and home diuretic, and heart failure hospitalization) was developed and validated based on predictors of the diuretic efficiency phenogroups to estimate the probability of low diuretic efficiency using the pooled ADHF trials described earlier. The associations of the BAN-ADHF score with markers and symptoms of congestion, length of stay, in-hospital mortality, and global well-being were assessed using adjusted regression models., Results: Clustering identified 3 phenogroups based on diuretic efficiency: phenogroup 1 (n = 370; 47%) had lower diuretic efficiency (median: 13.1 mL/mg; Q1-Q3: 7.7-19.4 mL/mg) than phenogroups 2 (n = 290; 37%) and 3 (n = 134; 17%) (median: 17.8 mL/mg; Q1-Q3: 10.8-26.1 mL/mg and median: 35.3 mL/mg; Q1-Q3: 17.5-49.0 mL/mg, respectively) (P < 0.001). The median urine output difference in response to 80 mg intravenous twice-daily furosemide between the lowest and highest diuretic efficiency group (phenogroup 1 vs 3) was 3,520 mL/d. The BAN-ADHF score demonstrated good model performance for predicting the lowest diuretic efficiency phenogroup membership (C-index: 0.92 in DOSE/ESCAPE validation cohort) that was superior to measures of kidney function (creatinine or blood urea nitrogen), natriuretic peptide levels, or home diuretic dose (DeLong P < 0.001 for all). Net urine output in response to 80 mg intravenous twice-daily furosemide among patients with a low vs high (5 vs 20) BAN-ADHF score was 2,650 vs 660 mL per 24 hours, respectively. Participants with higher BAN-ADHF scores had significantly lower global well-being, higher natriuretic peptide levels on discharge, a longer in-hospital stay, and a higher risk of in-hospital mortality in both derivation and validation cohorts., Conclusions: The authors developed and validated a phenomapping strategy and diuresis score for individuals with ADHF and differential response to diuretic therapy, which was associated with length of stay and mortality., Competing Interests: Funding Support and Author Disclosures Dr Pandey has received research support from the National Institute of Health (5R01MD017529, R21HL169708) and grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Roche Diagnostics, Axon Therapies, Medtronic, Edward Lifesciences, Science37, Novo Nordisk, Bayer, Merck, Sarfez Pharmaceuticals, and Emmi Solutions; has received nonfinancial support from Pfizer and Merck; and is also a consultant for Palomarin Inc with stock compensation. Dr Segar has received honoraria from Merck. Dr Patel has served as a consultant to Novo Nordisk. Dr Fonarow has done consulting for Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Janssen, Medtronic, Merck, and Novartis. Dr Mentz has received research support and honoraria from Abbott, American Regent, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim/Eli Lilly, Boston Scientific, Cytokinetics, Fast BioMedical, Gilead, Innolife, Medtronic, Merck, Novartis, Relypsa, Respicardia, Roche, Sanofi, Vifor, Windtree Therapeutics, and Zoll. Dr Allen reports grant funding from American Heart Association, National Institutes of Health, and PCORI; and consulting fees from Amgen, Boston Scientific, Cytokinetics, Novartis, and WCG ACI Clinical. Dr Pandey has received grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Bayer, Edwards Lifesciences, Medtronic, Sarfez Pharmacuticals, Novo Nordisk, and Roche Diagnostics; has received support from Pfizer and Merck; and is a consultant for Palomarin Inc with stock compensation. Dr Khan serves as an advisory board member for Bayer. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Postoperative atrial fibrillation (POAF) after cardiac surgery is associated with elevated morbidity and mortality. Although current prediction models have limited efficacy, several perioperative interventions can reduce patients' risk of POAF. These begin with preoperative medications, including beta-blockers and amiodarone. Moreover, patients should be screened for preexisting atrial fibrillation (AF) so that concomitant surgical ablation and left atrial appendage occlusion can be performed in appropriate candidates. Intraoperative interventions such as posterior pericardiectomy can reduce mediastinal fluid accumulation, which is a trigger for POAF. Furthermore, many preventive strategies for POAF are implemented in the immediate postoperative period. Initiating beta-blockers, amiodarone, or both is reasonable for most patients. Overdrive atrial pacing, colchicine, and steroids have been used by some, although the evidence base is less robust. For patients with POAF, rate-control and rhythm-control strategies have comparable outcomes. Decision-making regarding anticoagulation should recognize that the stroke risk associated with POAF appears to be lower than that for general nonvalvular AF. The evidence that oral anticoagulation reduces stroke risk is less clear for POAF patients than for patients with general nonvalvular AF. Given that POAF tends to be shorter-lived and is associated with greater bleeding risks in the perioperative period, decisions regarding anticoagulation should be individualized. Finally, wearable technology and machine learning algorithms for better predicting and managing POAF appear to be coming soon. These technologies and a comprehensive clinical program could meaningfully reduce the incidence of this common complication., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-23-1626/coif). L.R.B. has received honoraria for lectures from Abiomed. M.W.S. has received speaker fees from Merck. M.R.M. is a consultant/advisory board member for Medtronic and Edwards Lifesciences. S.C. has served on advisory boards for Edwards Lifesciences, La Jolla Pharmaceutical Company, Eagle Pharmaceuticals, and Baxter Pharmaceuticals. S.C. serves as an unpaid editorial board member of Journal of Thoracic Disease. The other authors have no conflicts of interest to declare., (2024 Journal of Thoracic Disease. All rights reserved.)

Aim: Left ventricular (LV) global longitudinal strain (GLS) may detect subtle abnormalities in myocardial contractility among individuals with normal LV ejection fraction (LVEF). However, the prognostic implications of GLS among healthy, community-dwelling adults is not well-established., Methods and Results: Overall, 2234 community-dwelling adults (56% women, 47% Black) with LVEF ≥50% without a history of cardiovascular disease (CVD) from the Dallas Heart Study who underwent cardiac magnetic resonance (CMR) with GLS assessed by feature tracking CMR (FT-CMR) were included. The association of GLS with the risk of incident major adverse cardiovascular events (MACE; composite of incident myocardial infarction, incident heart failure [HF], hospitalization for atrial fibrillation, coronary revascularization, and all-cause death), and incident HF or death were assessed with adjusted Cox proportional hazards models. A total of 309 participants (13.8%) had MACE during a median follow-up duration of 17 years. Participants with the worst GLS (Q4) were more likely male and of the Black race with a history of tobacco use and diabetes with lower LVEF, higher LV end-diastolic volume, and higher LV mass index. Cumulative incidence of MACE was higher among participants with worse (Q4 vs. Q1) GLS (20.4% vs. 9.0%). In multivariable-adjusted Cox models that included clinical characteristics, cardiac biomarkers and baseline LVEF, worse GLS (Q4 vs. Q1) was associated with a significantly higher risk of MACE (hazard ratio [HR] 1.55, 95% confidence interval [CI] 1.07-2.24, p = 0.02) and incident HF or death (HR 1.57, 95% CI 1.03-2.38, p = 0.04)., Conclusions: Impaired LV GLS assessed by FT-CMR among adults free of cardiovascular disease is associated with a higher risk of incident MACE and incident HF or death independent of cardiovascular risk factors, cardiac biomarkers and LVEF., (© 2024 European Society of Cardiology.)

Background: The optimal approach to identify individuals with diabetes who are at a high risk for developing heart failure (HF) to inform implementation of preventive therapies is unknown, especially in those without atherosclerotic cardiovascular disease (ASCVD)., Methods: Adults with diabetes and no HF at baseline from 7 community-based cohorts were included. Participants without ASCVD who were at high risk for developing HF were identified using 1-step screening strategies: risk score (WATCH-DM [Weight, Age, Hypertension, Creatinine, HDL-C, Diabetes Control, QRS Duration, MI, and CABG] ≥12), NT-proBNP (N-terminal pro-B-type natriuretic peptide ≥125 pg/mL), hs-cTn (high-sensitivity cardiac troponin T ≥14 ng/L; hs-cTnI ≥31 ng/L), and echocardiography-based diabetic cardiomyopathy (echo-DbCM; left atrial enlargement, left ventricular hypertrophy, or diastolic dysfunction). High-risk participants were also identified using 2-step screening strategies with a second test to identify residual risk among those deemed low risk by the first test: WATCH-DM/NT-proBNP, NT-proBNP/hs-cTn, NT-proBNP/echo-DbCM. Across screening strategies, the proportion of HF events identified, 5-year number needed to treat and number needed to screen to prevent 1 HF event with an SGLT2i (sodium-glucose cotransporter 2 inhibitor) among high-risk participants, and cost of screening were estimated., Results: The initial study cohort included 6293 participants (48.2% women), of whom 77.7% without prevalent ASCVD were evaluated with different HF screening strategies. At 5-year follow-up, 6.2% of participants without ASCVD developed incident HF. The 5-year number needed to treat to prevent 1 HF event with an SGLT2i among participants without ASCVD was 43 (95% CI, 29-72). In the cohort without ASCVD, high-risk participants identified using 1-step screening strategies had a low 5-year number needed to treat (22 for NT-proBNP to 37 for echo-DbCM). However, a substantial proportion of HF events occurred among participants identified as low risk using 1-step screening approaches (29% for echo-DbCM to 47% for hs-cTn). Two-step screening strategies captured most HF events (75-89%) in the high-risk subgroup with a comparable 5-year number needed to treat as the 1-step screening approaches (30-32). The 5-year number needed to screen to prevent 1 HF event was similar across 2-step screening strategies (45-61). However, the number of tests and associated costs were lowest for WATCH-DM/NT-proBNP ($1061) compared with other 2-step screening strategies (NT-proBNP/hs-cTn: $2894; NT-proBNP/echo-DbCM: $16 358)., Conclusions: Selective NT-proBNP testing based on the WATCH-DM score efficiently identified a high-risk primary prevention population with diabetes expected to derive marked absolute benefits from SGLT2i to prevent HF., Competing Interests: Disclosures Dr Patel has served as a consultant to Novo Nordisk. Dr Segar has received honoraria from Merck. Dr Klonoff has served as a consultant for Afon, Atropos Health, Better Therapeutics, Glucotrack, Lifecare, Novo, Samsung, and Thirdwayv. Dr Khan has received personal fees from Merck. Dr Lam has received research support from AstraZeneca, Bayer, Boston Scientific, and Roche Diagnostics; has served as a consultant or on advisory boards/steering committees/executive committees for Actelion, Alleviant Medical, Allysta, Amgen, ANaCardio AB, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Cytokinetics, Darma, EchoNous, Impulse Dynamics, Ionis Pharmaceutical, Janssen Research and Development, Medscape, Merck, Novartis, Novo Nordisk, Radcliffe Group, Roche Diagnostics, Sanofi, Siemens Healthcare Diagnostics, Us2.ai, and WebMD Global; and has served as cofounder and nonexecutive director of Us2.ai. Dr Verma holds a Tier 1 Canada Research Chair in Cardiovascular Surgery; has received research grants and honoraria from Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, HLS Therapeutics, Janssen, Novartis, Novo Nordisk, PhaseBio, and Pfizer; has received honoraria from Sanofi, Sun Pharmaceuticals, and the Toronto Knowledge Translation Working Group; is a member of the scientific excellence committee of the EMPEROR-Reduced trial (Empagliflozin Outcome Trial in Patients with Chronic Heart Failure With Reduced Ejection Fraction); has served as a national lead investigator of the DAPA-HF (Dapagliflozin and Prevention of Adverse Outcomes in Heart Failure) and EMPEROR-Reduced trials; and is the president of the Canadian Medical and Surgical Knowledge Translation Research Group, a federally incorporated not-for-profit physician organization. Dr Nasir is on the advisory board of Amgen, Novartis, and Novo Nordisk, and his research is partly supported by the Jerold B. Katz Academy of Translational Research. Dr Butler has served as a consultant for Abbott, American Regent, Amgen, Applied Therapeutic, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiac Dimension, Cardior, CVRx, Cytokinetics, Daxor Edwards, Element Science, Innolife, Impulse Dynamics, Imbria, Inventiva, Lexicon, Lilly, LivaNova, Janssen, Medtronics, Merck, Occlutech, Owkin, Novartis, Novo Nordisk, Pfizer, Pharmacosmos, Pharmain, Prolaio, Roche, Secretome, Sequana, SQ Innovation, Tenex, and Vifor. Dr Vaduganathan is supported by the KL2/Catalyst Medical Research Investigator Training award from Harvard Catalyst (National Institutes of Health/National Center for Advancing Translational Sciences Award UL 1TR002541) and has served on advisory boards or has received research grant support from American Regent, Amgen, AstraZeneca, Baxter Healthcare, Bayer AG, Boehringer Ingelheim, Cytokinetics, and Relypsa. Dr Pandey has received research support from the National Institute on Aging GEMSSTAR Grant (1R03AG067960-01) and the National Institute on Minority Health and Disparities (R01MD017529). Dr Pandey has received grant funding (to the institution) from Applied Therapeutics, Gilead Sciences, Ultromics, Myovista, and Roche; has served as a consultant for and/or has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Roche Diagnostics, Sarfez Therapeutics, Edwards Lifesciences, Merck, Bayer, Novo Nordisk, Alleviant, Axon Therapies; and has received nonfinancial support from Pfizer and Merck. Dr Pandey is also a consultant for Palomarin Inc with stocks compensation. The other authors report no conflicts.

The oblique random survival forest (RSF) is an ensemble supervised learning method for right-censored outcomes. Trees in the oblique RSF are grown using linear combinations of predictors, whereas in the standard RSF, a single predictor is used. Oblique RSF ensembles have high prediction accuracy, but assessing many linear combinations of predictors induces high computational overhead. In addition, few methods have been developed for estimation of variable importance (VI) with oblique RSFs. We introduce a method to increase computational efficiency of the oblique RSF and a method to estimate VI with the oblique RSF. Our computational approach uses Newton-Raphson scoring in each non-leaf node, We estimate VI by negating each coefficient used for a given predictor in linear combinations, and then computing the reduction in out-of-bag accuracy. In benchmarking experiments, we find our implementation of the oblique RSF is hundreds of times faster, with equivalent prediction accuracy, compared to existing software for oblique RSFs. We find in simulation studies that "negation VI" discriminates between relevant and irrelevant numeric predictors more accurately than permutation VI, Shapley VI, and a technique to measure VI using analysis of variance. All oblique RSF methods in the current study are available in the aorsf R package, and additional supplemental materials are available online., Competing Interests: Disclosure Statement No potential conflict of interest was reported by the authors.

Introduction: Diabetic cardiomyopathy (DbCM) is characterized by subclinical abnormalities in cardiac structure/function and is associated with a higher risk of overt heart failure (HF). However, there are limited data on optimal strategies to identify individuals with DbCM in contemporary health systems. The aim of this study was to evaluate the prevalence of DbCM in a health system using existing data from the electronic health record (EHR)., Methods: Adult patients with type 2 diabetes mellitus free of cardiovascular disease (CVD) with available data on HF risk in a single-center EHR were included. The presence of DbCM was defined using different definitions: (1) least restrictive: ≥1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); (2) intermediate restrictive: ≥2 echocardiographic abnormalities; (3) most restrictive: 3 echocardiographic abnormalities. DbCM prevalence was compared across age, sex, race, and ethnicity-based subgroups, with differences assessed using the chi-squared test. Adjusted logistic regression models were constructed to evaluate significant predictors of DbCM., Results: Among 1921 individuals with type 2 diabetes mellitus, the prevalence of DbCM in the overall cohort was 8.7% and 64.4% in the most and least restrictive definitions, respectively. Across all definitions, older age and Hispanic ethnicity were associated with a higher proportion of DbCM. Females had a higher prevalence than males only in the most restrictive definition. In multivariable-adjusted logistic regression, higher systolic blood pressure, higher creatinine, and longer QRS duration were associated with a higher risk of DbCM across all definitions., Conclusions: In this single-center, EHR cohort, the prevalence of DbCM varies from 9% to 64%, with a higher prevalence with older age and Hispanic ethnicity., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MWS has received personal fees from Merck & Co. NK has received consultant fees from HeartSciences and Tricog Health. KP has served as a consultant to Novo Nordisk. AP has received research support from the National Institute of Health (5R01MD017529, R21HL169708), grant funding from Applied Therapeutics and Gilead Sciences; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, Roche Diagnostics, Axon therapies, Medtronic, Edward Lifesciences, Science37, Novo Nordisk, Bayer, Merck, Sarfez Pharmaceuticals, Emmi Solutions; and has received nonfinancial support from Pfizer and Merck. Dr. Pandey is also a consultant for Palomarin Inc. with stock compensation. All other authors declare no competing interests.

Background: Polypharmacy is common among patients with heart failure with reduced ejection fraction (HFrEF). However, its impact on the use of optimal guideline-directed medical therapy (GDMT) is not well established., Objectives: This study sought to evaluate the association between polypharmacy and odds of receiving optimal GDMT over time among patients with HFrEF., Methods: The authors conducted a post hoc analysis of the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment) trial. Polypharmacy was defined as receiving ≥5 medications (excluding HFrEF GDMT) at baseline. The outcome of interest was optimal triple therapy GDMT (concurrent administration of a renin-angiotensin-aldosterone blocker and beta-blocker at 50% of the target dose and a mineralocorticoid receptor antagonist at any dose) achieved over the 12-month follow-up. Multivariable adjusted mixed-effect logistic regression models with multiplicative interaction terms (time × polypharmacy) were constructed to evaluate how polypharmacy at baseline modified the odds of achieving optimal GDMT on follow-up., Results: The study included 891 participants with HFrEF. The median number of non-GDMT medications at baseline was 4 (IQR: 3-6), with 414 (46.5%) prescribed ≥5 and identified as being on polypharmacy. The proportion of participants who achieved optimal GDMT at the end of the 12-month follow-up was lower with vs without polypharmacy at baseline (15% vs 19%, respectively). In adjusted mixed models, the odds of achieving optimal GDMT over time were modified by baseline polypharmacy status (P for interaction < 0.001). Patients without polypharmacy at baseline had increased odds of achieving GDMT (OR: 1.16 [95% CI: 1.12-1.21] per 1-month increase; P < 0.001) but not patients with polypharmacy (OR: 1.01 [95% CI: 0.96-1.06)] per 1-month increase)., Conclusions: Patients with HFrEF who are on non-GDMT polypharmacy have lower odds of achieving optimal GDMT on follow-up., Competing Interests: Funding Support and Author Disclosures Dr Pandey has received research support from the National Institute on Aging GEMSSTAR grant (1R03AG067960-01) and the National Institute on Minority Health and Disparities (R01MD017529); grant funding from Applied Therapeutics and Gilead Sciences; honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly USA, Rivus, Cytokinetics, and Roche Diagnostics; nonfinancial support from Pfizer and Merck; and is a consultant for Palomarin Inc with stock compensation. Dr Segar has received speaker fees from Merck and Co outside the present study. Dr Butler is a consultant for Abbott, American Regent, Amgen, Applied Therapeutic, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiac Dimension, Cardior, CVRx, Cytokinetics, Edwards, Element Science, Innolife, Impulse Dynamics, Imbria, Inventiva, Lexicon, Lilly, LivaNova, Janssen, Medtronics, Merck, Occlutech, Novartis, Novo Nordisk, Pfizer, Pharmacosmos, Pharmain, Roche, Sequana, SQ Innovation, 3live, and Vifor. Dr Fonarow has done consulting for Abbott, Amgen, AstraZeneca, Bayer, Cytokinetics, Eli Lilly, Johnson & Johnson, Medtronic, Merck, Novartis, and Pfizer. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023. Published by Elsevier Inc.)

Background: Postoperative atrial fibrillation (POAF) frequently complicates cardiac surgery. Predicting POAF can guide interventions to prevent its onset. This study assessed the incidence, risk factors, and related adverse outcomes of POAF after cardiac surgery., Methods: A cohort of 1,606 patients undergoing cardiac surgery at a tertiary referral center was analyzed. Postoperative AF was defined based on the Society of Thoracic Surgeons' criteria: AF/atrial flutter after operating room exit that either lasted longer than 1 hour or required medical or procedural intervention. Risk factors for POAF were evaluated, and the performance of established risk scores (POAF, HATCH, COM-AF, CHA2DS2-VASc, and Society of Thoracic Surgeons risk scores) in predicting POAF was assessed using discrimination (area under the receiver operator characteristics curve) analysis. The association of POAF with secondary outcomes, including length of hospital stay, ventilator time, and discharge to rehabilitation facilities, was evaluated using adjusted linear and logistic regression models., Results: The incidence of POAF was 32.2% (n = 517). Patients who developed POAF were older, had traditional cardiovascular risk factors and higher Society of Thoracic Surgeons risk scores, and often underwent valve surgery. The POAF risk score demonstrated the highest area under the receiver operator characteristics curve (0.65), but risk scores generally underperformed. Postoperative AF was associated with extended hospital stays, longer ventilator use, and higher likelihood of discharge to rehabilitation facilities (odds ratio, 2.30; 95% CI, 1.73-3.08)., Conclusion: This study observed a high incidence of POAF following cardiac surgery and its association with increased morbidity and resource utilization. Accurate POAF prediction remains elusive, emphasizing the need for better risk-prediction methods and tailored interventions to diminish the effect of POAF on patient outcomes., (© 2023 The Author(s). Published by The Texas Heart Institute®.)

Left atrial appendage occlusion (LAAO) is effective in preventing thromboembolism. Risk stratification tools could help identify patients at risk for early mortality after LAAO. In this study, we validated and recalibrated a clinical risk score (CRS) to predict risk of all-cause mortality after LAAO. This study used data from patients who underwent LAAO in a single-center, tertiary hospital. A previously developed CRS using 5 variables (age, body mass index [BMI], diabetes, heart failure, and estimated glomerular filtration rate) was applied to each patient to assess risk of all-cause mortality at 1 and 2 years. The CRS was recalibrated to the present study cohort and compared with established atrial fibrillation-specific (CHA 2 DS 2 -VASc and HAS-BLED) and generalized (Walter index) risk scores. Cox proportional hazard models were used to assess the risk of mortality and discrimination was assessed by Harrel C-index. Among 223 patients, the 1- and 2-year mortality rates were 6.7% and 11.2%, respectively. With the original CRS, only low BMI (<23 kg/m 2 ) was a significant predictor of all-cause mortality (hazard ratio [HR] [95% CI] 2.76 [1.03 to 7.35]; p = 0.04). With recalibration, BMI <29 kg/m 2 and estimated glomerular filtration rate <60 ml/min/1.73 m 2 were significantly associated with an increased risk of death (HR [95% CI] 3.24 [1.29 to 8.13] and 2.48 [1.07 to 5.74], respectively), with a trend toward significance noted for history of heart failure (HR [95% CI] 2.13 [0.97 to 4.67], p = 0.06). Recalibration improved the discriminative ability of the CRS from 0.65 to 0.70 and significantly outperformed established risk scores (CHA 2 DS 2 -VASc = 0.58, HAS-BLED = 0.55, Walter index = 0.62). In this single-center, observational study, the recalibrated CRS accurately risk stratified patients who underwent LAAO and significantly outperformed established atrial fibrillation-specific and generalized risk scores. In conclusion, clinical risk scores should be considered as an adjunct to standard of care when evaluating a patient's candidacy for LAAO., Competing Interests: Declaration of Competing Interest Dr. Segar has reported speaker fees from Merck & Co and grant support from the American Heart Association outside the present study. The remaining authors have no conflicts of interest to declare., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: In the CANVAS (Canagliflozin Cardiovascular Assessment Study) program, canagliflozin reduced the risk of heart failure (HF) hospitalization among individuals with type 2 diabetes mellitus (T2DM)., Objectives: The purpose of this study was to evaluate heterogeneity in absolute and relative treatment effects of canagliflozin on HF hospitalization according to baseline HF risk as assessed by diabetes-specific HF risk scores (WATCH-DM [Weight (body mass index), Age, hyperTension, Creatinine, HDL-C, Diabetes control (fasting plasma glucose) and QRS Duration, MI and CABG] and TRS-HF DM [TIMI Risk Score for HF in Diabetes])., Methods: Participants in the CANVAS trial were categorized into low, medium, and high risk for HF using the WATCH-DM score (for participants without prevalent HF) and the TRS-HF DM score (for all participants). The outcome of interest was time to first HF hospitalization. The treatment effect of canagliflozin vs placebo for HF hospitalization was compared across risk strata., Results: Among 10,137 participants with available HF data, 1,446 (14.3%) had HF at baseline. Among participants without baseline HF, WATCH-DM risk category did not modify the treatment effect of canagliflozin (vs placebo) on HF hospitalization (P interaction = 0.56). However, the absolute and relative risk reduction with canagliflozin was numerically greater in the high-risk group (cumulative incidence, canagliflozin vs placebo: 8.1% vs 12.7%; HR: 0.62 [95% CI: 0.37-0.93]; P = 0.03; number needed to treat: 22) than in the low- and intermediate-risk groups. When overall study participants were categorized according to the TRS-HF DM score, a statistically significant difference in the treatment effect of canagliflozin across risk strata was observed (P interaction = 0.04). Canagliflozin significantly reduced the risk of HF hospitalization by 39% in the high-risk group (HR: 0.61 [95% CI: 0.48-0.78]; P < 0.001; number needed to treat: 20) but not in the intermediate- or low-risk groups., Conclusions: Among participants with T2DM, the WATCH-DM and TRS-HF DM can reliably identify those at high risk for HF hospitalization and most likely to benefit from canagliflozin., Competing Interests: Funding Support and Author Disclosures This study, carried out under YODA Project # 2020-4213, used data obtained from the Yale University Open Data Access Project, which has an agreement with Janssen Research and Development, LLC. The interpretation and reporting of research using this data are solely the responsibility of the authors and does not necessarily represent the official views of the Yale University Open Data Access Project or Janssen Research and Development, LLC. Dr Khan has received personal fees from Merck. Dr Patel has served as a consultant to Novo Nordisk. Dr DeVore has received research funding through his institution from the American Heart Association, Biofourmis, Bodyport, Cytokinetics, American Regent Inc, the National Heart, Lung, and Blood Institute, Novartis, and Story Health; has provided consulting services for and/or received honoraria from Abiomed, AstraZeneca, Cardionomic, InnaMed, LivaNova, Natera, Novartis, Procyrion, Story Health, Vifor, and Zoll; and has received nonfinancial support from Abbott for educational and research activities. Dr Vaduganathan is supported by the KL2/Catalyst Medical Research Investigator Training award from Harvard Catalyst (National Institutes of Health/National Center for Advancing Translational Sciences Award UL 1TR002541); and has served on advisory boards or has received research grant support from American Regent, Amgen, AstraZeneca, Baxter Healthcare, Bayer AG, Boehringer Ingelheim, Cytokinetics, and Relypsa. Dr Lam has received research support from AstraZeneca, Bayer, Boston Scientific, and Roche Diagnostics; has served as a consultant or on advisory boards/steering committees/executive committees for Actelion, Alleviant Medical, Allysta, Amgen, ANaCardio AB, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, Cytokinetics, Darma, EchoNous, Impulse Dynamics, Ionis Pharmaceutical, Janssen Research and Development, Medscape, Merck, Novartis, Novo Nordisk, Radcliffe Group, Roche Diagnostics, Sanofi, Siemens Healthcare Diagnostics, Us2.ai and WebMD Global; and has served as cofounder and non-executive director of Us2.ai. Dr Zannad has received personal fees from Boehringer Ingelheim during the conduct of the study; has received personal fees from Janssen, Novartis, Boston Scientific, Amgen, CVRx, AstraZeneca, Vifor Fresenius, Cardior, Cereno Pharmaceutical, Applied Therapeutics, Merck, Bayer, and Cellprothera outside of the submitted work; and has received other support from cardiovascular clinical trialists and Cardiorenal outside of the submitted work. Dr Verma holds a Tier 1 Canada Research Chair in Cardiovascular Surgery; has received research grants and honoraria from Amarin, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, HLS Therapeutics, Janssen, Novartis, Novo Nordisk, PhaseBio, and Pfizer; has received honoraria from Sanofi, Sun Pharmaceuticals, and the Toronto Knowledge Translation Working Group; is a member of the scientific excellence committee of the EMPEROR-Reduced trial (Empagliflozin Outcome Trial in Patients with Chronic Heart Failure With Reduced Ejection Fraction); has served as a national lead investigator of the DAPA-HF and EMPEROR-Reduced trials; and is the president of the Canadian Medical and Surgical Knowledge Translation Research Group, a federally incorporated not-for-profit physician organization. Dr Butler has received consulting fees from Boehringer Ingelheim, Cardior, CVRx, Foundry, G3 Pharma, Imbria, Impulse Dynamics, Innolife, Janssen, LivaNova, Luitpold, Medtronic, Merck, Novartis, Novo Nordisk, Relypsa, Roche, Sanofi, Sequana Medical, V-Wave Ltd, and Vifor. Dr Pandey has received grant funding outside the present study from Applied Therapeutics; has received honoraria outside of the present study as an advisor/consultant for Tricog Health Inc, Lilly, USA, Rivus, and Roche Diagnostics; has received nonfinancial support from Pfizer and Merck; is supported by the Texas Health Resources Clinical Scholarship, Gilead Sciences Research Scholar Program, and the National Institute of Aging GEMSSTAR Grant (1R03AG067960-01); and has received grant support from Applied Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Aims: To evaluate the contribution of baseline and longitudinal changes in cardiometabolic health (CMH) towards heart failure (HF) risk among adults with type 2 diabetes (T2D)., Methods and Results: Participants of the Look AHEAD trial with T2D and without prevalent HF were included. Adjusted Cox models were used to create a CMH score incorporating target levels of parameters weighted based on relative risk for HF. The associations of baseline and changes in the CMH score with risk of overall HF, HF with preserved (HFpEF) and reduced ejection fraction (HFrEF) were assessed using Cox models. Among the 5080 participants, 257 incident HF events occurred over 12.4 years of follow-up. The CMH score included 2 points each for target levels of waist circumference, glomerular filtration rate, urine albumin-to-creatinine ratio, and 1 point each for blood pressure and glycated haemoglobin at target. High baseline CMH score (6-8) was significantly associated with lower overall HF risk (adjusted hazard ratio [HR], ref = low score (0-3): 0.31, 95% confidence interval [CI] 0.21-0.47) with similar associations observed for HFpEF and HFrEF. Improvement in CMH was significantly associated with lower risk of overall HF (adjusted HR per 1-unit increase in score at 4 years: 0.80, 95% CI 0.70-0.91). In the ACCORD validation cohort, the baseline CMH score performed well for predicting HF risk with adequate discrimination (C-index 0.70), calibration (chi-square 5.53, p = 0.70), and risk stratification (adjusted HR [high (6-8) vs. low score (0-3)]: 0.35, 95% CI 0.26-0.46). In the Look AHEAD subgroup with available biomarker data, incorporating N-terminal pro-B-type natriuretic peptide to the baseline CMH score improved model discrimination (C-index 0.79) and risk stratification (adjusted HR [high (8-10) vs. low score (0-4)]: 0.18, 95% CI 0.09-0.35)., Conclusions: Achieving target levels of more CMH parameters at baseline and sustained improvements were associated with lower HF risk in T2D., (© 2022 European Society of Cardiology.)

Background: Racial disparities in heart failure hospitalization and mortality are well established; however, the association between different social determinants of health (SDOH) and length of stay (LOS) and the extent to which this association may differ across racial groups is not well established., Methods: We utilized data from the Get With The Guidelines-Heart Failure registry to evaluate the association between SDOH, as determined by patients' residential ZIP Code and LOS among patients hospitalized with heart failure. We also assessed the race-specific contribution of the ZIP Code-level SDOH to LOS in patients of Black and non-Black races. Finally, we evaluated SDOH predictors of racial differences in LOS at the hospital level., Results: Among 301 500 patients (20.2% Black race), the median LOS was 4 days. In adjusted analysis accounting for patient-level and hospital-level factors, SDOH parameters of education, income, housing instability, and foreign-born were significantly associated with LOS after adjusting for clinical status and hospital-level factors. SDOH parameters accounted for 25.8% of the total attributable risk for prolonged LOS among Black patients compared with 10.1% in patients of non-Black race. Finally, hospitals with disproportionately longer LOS for Black versus non-Black patients were more likely to care for disadvantaged patients living in ZIP Codes with a higher percentage of foreign-born and non-English speaking areas., Conclusions: ZIP Code-level SDOH markers can identify patients at risk for prolonged LOS, and the effects of SDOH parameters are significantly greater among Black adults with heart failure as compared with non-Black adults.

Aims: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have been shown to reduce the risk of hospitalization for heart failure (HHF) and composite kidney outcomes, but the mediators underlying these benefits are unknown., Materials and Methods: Among participants from VERTIS CV, a trial of patients with type 2 diabetes mellitus and atherosclerotic cardiovascular disease randomized to ertugliflozin versus placebo, Cox proportional hazards regression models were used to evaluate the percentage mediation of ertugliflozin efficacy on the first HHF and kidney composite outcome in 26 potential mediators. Time-dependent approaches were used to evaluate associations between early (change from baseline to the first post-baseline measurement) and average (weighted average of change from baseline using all post-baseline measurements) changes in covariates with clinical outcomes., Results: For the HHF analyses, early changes in four biomarkers (haemoglobin, haematocrit, serum albumin and urate) and average changes in seven biomarkers (early biomarkers + weight, chloride and serum protein) were identified as fulfilling the criteria as mediators of ertugliflozin effects on the risk of HHF. Similar results were observed for the composite kidney outcome, with early changes in four biomarkers (glycated haemoglobin, haemoglobin, haematocrit and urate), and average changes in five biomarkers [early biomarkers (not glycated haemoglobin) + weight, serum albumin] mediating the effects of ertugliflozin on the kidney outcome., Conclusions: In these analyses from the VERTIS CV trial, markers of volume status and haemoconcentration and/or haematopoiesis were the strongest mediators of the effect of ertugliflozin on reducing risk of HHF and composite kidney outcomes in the early and average change periods., Gov Identifier: NCT01986881., (© 2022 Pfizer Inc. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Background: Among individuals with atherosclerotic cardiovascular disease (ASCVD), type 2 diabetes mellitus (T2DM) is common and confers increased risk for morbidity and mortality. Differentiating risk is key to optimize efficiency of treatment selection. Our objective was to develop and validate a model to predict risk of major adverse cardiovascular events (MACE) comprising the first event of cardiovascular death, myocardial infarction (MI), or stroke for individuals with both T2DM and ASCVD., Methods: Using data from the Trial Evaluating Cardiovascular Outcomes with Sitagliptin (TECOS), we used Cox proportional hazards models to predict MACE among participants with T2DM and ASCVD. All baseline covariates collected in the trial were considered for inclusion, although some were excluded immediately because of large missingness or collinearity. A full model was developed using stepwise selection in each of 25 imputed datasets, and comprised candidate variables selected in 20 of the 25 datasets. A parsimonious model with a maximum of 10 degrees of freedom was created using Cox models with least absolute shrinkage and selection operator (LASSO), where the adjusted R-square was used as criterion for selection. The model was then externally validated among a cohort of participants with similar criteria in the ACCORD (Action to Control Cardiovascular Risk in Diabetes) trial. Discrimination of both models was assessed using Harrell's C-index and model calibration by the Greenwood-Nam-D'Agostino statistic based on 4-year event rates., Results: Overall, 1491 (10.2%) of 14,671 participants in TECOS and 130 (9.3%) in the ACCORD validation cohort (n = 1404) had MACE over 3 years' median follow-up. The final model included 9 characteristics (prior stroke, age, chronic kidney disease, prior MI, sex, heart failure, insulin use, atrial fibrillation, and microvascular complications). The model had moderate discrimination in both the internal and external validation samples (C-index = 0.65 and 0.61, respectively). The model was well calibrated across the risk spectrum-from a cumulative MACE rate of 6% at 4 years in the lowest risk quintile to 26% in the highest risk quintile., Conclusion: Among patients with T2DM and prevalent ASCVD, this 9-factor risk model can quantify the risk of future ASCVD complications and inform decision making for treatments and intensity., (© 2022. The Author(s).)

Background: Supranormal ejection fraction by echocardiography in clinically referred patient populations has been associated with an increased risk of cardiovascular disease (CVD). The prognostic implication of supranormal left ventricular ejection fraction (LVEF)-assessed by cardiac magnetic resonance (CMR)-in healthy, community-dwelling individuals is unknown., Objectives: The purpose of this study is to investigate the prognostic implication of supranormal LVEF as assessed by CMR and its inter-relationship with stroke volume among community-dwelling adults without CVD., Methods: Participants from the MESA (Multi-Ethnic Study of Atherosclerosis) and DHS (Dallas Heart Study) cohorts free of CVD who underwent CMR with LVEF above the normal CMR cutoff (≥57%) were included. The association between cohort-specific LVEF categories and risk of clinically adjudicated major adverse cardiovascular events (MACE) was assessed using adjusted Cox models. Subgroup analysis was also performed to evaluate the association of LVEF and risk of MACE among individuals stratified by left ventricular stroke volume index., Results: The study included 4,703 participants from MESA and 2,287 from DHS with 727 and 151 MACE events, respectively. In adjusted Cox models, the risk of MACE was highest among individuals in LVEF Q4 (vs Q1) in both cohorts after accounting for potential confounders (MESA: HR = 1.27 [95% CI: 1.01-1.60], P = 0.04; DHS: HR = 1.72 [95% CI: 1.05-2.79], P = 0.03). A significant interaction was found between the continuous measures of LVEF and left ventricular stroke volume index (P interaction = 0.02) such that higher LVEF was significantly associated with an increased risk of MACE among individuals with low but not high stroke volume., Conclusions: Among community-dwelling adults without CVD, LVEF in the supranormal range is associated with a higher risk of adverse cardiovascular outcomes, particularly in those with lower stroke volume., Competing Interests: Funding Support and Author Disclosures The MESA study was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute; and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). The Dallas Heart Study was supported by a grant from the Donald W. Reynolds Foundation and the National Center for Advancing Translational Sciences (UL1TR001105). Dr Segar has received nonfinancial support from Pfizer and Merck. Dr Pandey has received research support from the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, the National Institute of Aging GEMSSTAR Grant (1R03AG067960-01), and Applied Therapeutics; has served on the advisory board of Roche Diagnostics; serves as a consultant to Tricog Health, Rivus, and Lilly USA; and has received nonfinancial support from Pfizer and Merck. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Importance: Traditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH)., Objective: To develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH., Design, Setting, and Participants: This retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014., Main Outcomes and Measures: Random forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility., Results: The training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients., Conclusions and Relevance: ML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

Background: Supervised aerobic exercise training (ET) is recommended for stable outpatients with heart failure (HF) with reduced ejection fraction (HFrEF). Frailty, a syndrome characterized by increased vulnerability and decreased physiologic reserve, is common in patients with HFrEF and associated with a higher risk of adverse outcomes. The effect modification of baseline frailty on the efficacy of aerobic ET in HFrEF is not known., Methods: Stable outpatients with HFrEF randomized to aerobic ET versus usual care in the HF-ACTION (Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training) trial were included. Baseline frailty was estimated using the Rockwood frailty index (FI), a deficit accumulation-based model of frailty assessment; participants with FI scores >0.21 were identified as frail. Multivariable Cox proportional hazard models with multiplicative interaction terms (frailty × treatment arm) were constructed to evaluate whether frailty modified the treatment effect of aerobic ET on the primary composite end point (all-cause hospitalization or mortality), secondary end points (composite of cardiovascular death or cardiovascular hospitalization, and cardiovascular death or HF hospitalization), and Kansas City Cardiomyopathy Questionnaire score. Separate models were constructed for continuous (FI) and categorical (frail versus not frail) measures of frailty., Results: Among 2130 study participants (age, 59±13 years; 28% women), 1266 (59%) were characterized as frail (FI>0.21). Baseline frailty burden significantly modified the treatment effect of aerobic ET ( P interaction: FI × treatment arm=0.02; frail status [frail versus nonfrail] × treatment arm=0.04) with a lower risk of primary end point in frail (hazard ratio [HR], 0.83 [95% CI, 0.72-0.95]) but not nonfrail (HR, 1.04 [95% CI, 0.87-1.25]) participants. The favorable effect of aerobic ET among frail participants was driven by a significant reduction in the risk of all-cause hospitalization (HR, 0.84 [95% CI, 0.72-0.99]). The treatment effect of aerobic ET on all-cause mortality and other secondary endpoints was not different between frail and nonfrail patients ( P interaction>0.1 for each). Aerobic ET was associated with a nominally greater improvement in Kansas City Cardiomyopathy Questionnaire scores at 3 months among frail versus nonfrail participants without a significant treatment interaction by frailty status ( P interaction>0.2)., Conclusions: Among patients with chronic stable HFrEF, baseline frailty modified the treatment effect of aerobic ET with a greater reduction in the risk of all-cause hospitalization but not mortality.

Background The WATCH-DM (weight [body mass index], age, hypertension, creatinine, high-density lipoprotein cholesterol, diabetes control [fasting plasma glucose], ECG QRS duration, myocardial infarction, and coronary artery bypass grafting) and TRS-HF DM (Thrombolysis in Myocardial Infarction [TIMI] risk score for heart failure in diabetes) risk scores were developed to predict risk of heart failure (HF) among individuals with type 2 diabetes. WATCH-DM was developed to predict incident HF, whereas TRS-HF DM predicts HF hospitalization among patients with and without a prior HF history. We evaluated the model performance of both scores to predict incident HF events among patients with type 2 diabetes and no history of HF hospitalization across different cohorts and clinical settings with varying baseline risk. Methods and Results Incident HF risk was estimated by the integer-based WATCH-DM and TRS-HF DM scores in participants with type 2 diabetes free of baseline HF from 2 randomized clinical trials (TECOS [Trial Evaluating Cardiovascular Outcomes With Sitagliptin], N=12 028; and Look AHEAD [Look Action for Health in Diabetes] trial, N=4867). The integer-based WATCH-DM score was also validated in electronic health record data from a single large health care system (N=7475). Model discrimination was assessed by the Harrell concordance index and calibration by the Greenwood-Nam-D'Agostino statistic. HF incidence rate was 7.5, 3.9, and 4.1 per 1000 person-years in the TECOS, Look AHEAD trial, and electronic health record cohorts, respectively. Integer-based WATCH-DM and TRS-HF DM scores had similar discrimination and calibration for predicting 5-year HF risk in the Look AHEAD trial cohort (concordance indexes=0.70; Greenwood-Nam-D'Agostino P >0.30 for both). Both scores had lower discrimination and underpredicted HF risk in the TECOS cohort (concordance indexes=0.65 and 0.66, respectively; Greenwood-Nam-D'Agostino P <0.001 for both). In the electronic health record cohort, the integer-based WATCH-DM score demonstrated a concordance index of 0.73 with adequate calibration (Greenwood-Nam-D'Agostino P =0.96). TRS-HF DM score could not be validated in the electronic health record because of unavailability of data on urine albumin/creatinine ratio in most patients in the contemporary clinical practice. Conclusions The WATCH-DM and TRS-HF DM risk scores can discriminate risk of HF among intermediate-risk populations with type 2 diabetes.

Objectives: In this study, we sought to evaluate the association of frailty with the use of optimal guideline-directed medical therapy (GDMT) and outcomes in heart failure with reduced ejection fraction (HFrEF)., Background: The burden of frailty in HFrEF is high, and the patterns of GDMT use according to frailty status have not been studied previously., Methods: A post hoc analysis of patients with HFrEF enrolled in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment in Heart Failure) trial was conducted. Frailty was assessed with the use of a frailty index (FI) using a 38-variable deficit model, and participants were categorized into 3 groups: class 1: nonfrail, FI <0.21); class 2: intermediate frailty, FI 0.21-0.31), and class 3: high frailty, FI >0.31). Multivariate-adjusted Cox models were used to study the association of frailty status with clinical outcomes. Use of optimal GDMT over time (beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and mineralocorticoid receptor antagonists) across frailty strata was assessed with the use of adjusted linear and logistic mixed-effect models., Results: The study included 879 participants, of which 56.3% had high frailty burden (class 3 FI). A higher frailty burden was associated with a significantly higher risk of HF hospitalization or death in adjusted Cox models: high frailty vs nonfrail HR: 1.76, 95% CI: 1.20-2.58. On follow-up, participants with high frailty burden also had a significantly lower likelihood of achieving optimal GDMT: high frailty vs non-frail GDMT triple therapy use at study end: 17.7% vs 28.4%; P interaction, frailty class × time <0.001., Conclusions: Patients with HFrEF with a high burden of frailty have a significantly higher risk for adverse clinical outcomes and are less likely to be initiated and up-titrated on an optimal GDMT regimen., Competing Interests: Funding Support and Author Disclosures The GUIDE-IT study was funded by grants HL105448, HL105451, and HL105457 from the National Institutes of Health, and Roche Diagnostics provided support for NT-proBNP testing. Dr Greene has received research support from the Duke University Department of Medicine Chair’s Research Award, American Heart Association, Amgen, AstraZeneca, Bristol Myers Squibb, Cytokinetics, Merck, Novartis, Pfizer, and Sanofi; has served on advisory boards for Amgen, AstraZeneca, Bristol Myers Squibb, Cytokinetics, and Sanofi; and serves as a consultant for Amgen, Bayer, Bristol Myers Squibb, Merck, and Vifor. Dr Fonarow is a consultant for Abbott, Amgen, AstraZeneca, Bayer, Janssen, Medtronic, Merck, and Novartis, and Associate Section Editor for the JAMA Cardiology. Dr Anker has received research support from Vifor International and Abbott Vascular; and fees for consultancy and/or speaking from AstraZeneca, Bayer, Boehringer Ingelheim, Respicardia, Impulse Dynamics, Janssen, Novartis, Servier, and Vifor International. Dr Felker has received research grants from the National Heart, Lung, and Blood Institute, American Heart Association, Amgen, Bayer Merck, Cytokinetics, Myokardia; has acted as a consultant to Novartis, Amgen, Bristol Myers Squibb, Cytokinetics, Medtronic, Cardionomic, Boehringer-Ingelheim, American Regent, Abbott, AstraZeneca, Reprieve, and Sequana; and has served on clinical end point committees/data safety monitoring boards for Amgen, Merck, Medtronic, EBR Systems, V-Wave, LivaNova, Siemens, and Rocket Pharma. Dr Januzzi has received grant support from Roche Diagnostics, Abbott Diagnostics, Singulex, Prevencio, Novartis, and Cleveland Heart Labs; has received consulting income from Roche Diagnostics, Abbott, Prevencio, and Critical Diagnostics; and participates in clinical end point committees/data safety monitoring boards for Siemens Diagnostics, Novartis, Bayer, AbbVie, and Amgen. Dr Butler has acted as a consultant to Abbott, Adrenomed, Amgen, Applied Therapeutics, Array, AstraZeneca, Bayer, Boehringer Ingelheim, CVRx, G3 Pharma, Impulse Dynamics, Innolife, Janssen, LivaNova, Luitpold, Medtronic, Merck, Novartis, NovoNordisk, Relypsa, Sequana Medical, V-Wave Limited, and Vifor; and has served on speaker bureaus for Novartis, Boehringer Ingelheim–Lilly, AstraZeneca, and Janssen. Dr Pandey is supported by the Texas Health Resources Clinical Scholars Program; and has served on the advisory board of Roche Diagnostics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Patients with continuous flow destination therapy (DT) left ventricular assist devices (LVAD) comprise a heterogeneous population. We hypothesized that phenotypic clustering of individuals with DT LVADs by their implantation characteristics will be associated with different long-term risk profiles. We analyzed 5,999 patients with continuous flow DT LVADs in Interagency Registry for Mechanically Assisted Circulatory Support using 18 continuous variable baseline characteristics. We Z-transformed the variables and applied a Gaussian finite mixture model to perform unsupervised clustering resulting in identification of 4 phenogroups. Survival analyses considered the competing risk for cumulative incidence of transplant or the composite end point of death or heart transplant where appropriate. Phenogroup 1 (n = 1,163, 19%) was older (71 years) and primarily white (81%). Phenogroups 2 (n = 648, 11%) and 3 (n = 3,671, 61%) were of intermediate age (70 and 62 years), weight (85 and 87 kg), and ventricular size. Phenogroup 4 (n = 517, 9%) was younger (40 years), heavier (108 kg), and more racially diverse. The cumulative incidence of death, heart transplant, bleeding, LVAD malfunction, and LVAD thrombosis differed among phenogroups. The highest incidence of death and the lowest rate of heart transplant was seen in phenogroup 1 (p <0.001). For adverse outcomes, phenogroup 4 had the lowest incidence of bleeding, whereas LVAD device thrombosis and malfunction were lowest in phenogroup 1 (p <0.001 for all). Finally, the incidence of stroke, infection, and renal dysfunction were not statistically different. In conclusion, the present unsupervised machine learning analysis identified 4 phenogroups with different rates of adverse outcomes and these findings underscore the influence of phenotypic heterogeneity on post-LVAD implantation outcomes., Competing Interests: Disclosures Dr. Grodin receives grant support from Texas Health Resources. Dr. Grodin reports relations with Pfizer Inc that includes consulting or advisory. Dr. Grodin reports relations with Alnylam Pharmaceuticals Inc that includes consulting or advisory. Dr. Grodin reports relations with Eidos Therapeutics that includes consulting or advisory and funding grants. Dr. Grodin reports relations with Sarepta Therapeutics Inc that includes consulting or advisory. Dr. Pandey receives a grant support from Texas Health Resources. Dr. Drazner receives grant support from James M. Wooten Chair in Cardiology. Dr. Grodin reports consulting fees from Pfizer, Eidos, Alnylam, and Sarepta. The remaining authors have no conflicts of interest to declare., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Objectives: This study evaluated the association of transthyretin (TTR) gene variant, in which isoleucine substitutes for valine at position 122 (V142I), with cardiac structure, function, and heart failure (HF) risk among middle-aged Black adults., Background: The valine-to-isoleucine substitution in the TTR protein is prevalent in Black individuals and causes cardiac amyloidosis., Methods: Jackson Heart Study participants without HF at baseline who had available data on the TTR V142I variant were included. The association of the TTR V142I variant with baseline echocardiographic parameters and repeated measures of high-sensitivity cardiac troponin-I (hs-cTnI) was assessed using adjusted linear regression models and linear mixed models, respectively. Adjusted Cox models, restricted mean survival time analysis, and Anderson-Gill models were constructed to determine the association of TTR V142I variant with the risk of incident HF, survival free of HF, and total HF hospitalizations., Results: A total of 119 of 2,960 participants (4%) were heterozygous carriers of the TTR V142I variant. The TTR V142I variant was not associated with measures of cardiac parameters at baseline but was associated with a greater increase in high-sensitivity troponin I (hs-TnI) levels over time. In adjusted Cox models, TTR V142I variant carriers had significantly higher risk of incident HF (HR: 1.80; 95% CI: 1.07-3.05; P = 0.03), lower survival free of HF (mean difference: 4.0 year; 95% CI: 0.6-6.2 years); P = 0.02), and higher risk of overall HF hospitalizations (HR: 2.12; 95% CI: 1.23-3.63; P = 0.007)., Conclusions: The TTR V142I variant in middle-aged Black adults is not associated with adverse cardiac remodeling but was associated with a significantly higher burden of chronic myocardial injury, and greater risk of incident HF and overall HF hospitalizations., Competing Interests: Funding Support and Author Disclosures The Jackson Heart Study was supported and conducted in collaboration with Jackson State University (HHSN268201800013I), Tougaloo College (HHSN268201800014I), the Mississippi State Department of Health (HHSN268201800015I), and the University of Mississippi Medical Center (HHSN268201800010I, HHSN268201800011I and HHSN268201800012I) contracts from the National Heart, Lung, and Blood Institute and the National Institute for Minority Health and Health Disparities. Dr Hall has received support from the National Institutes of Health (NIH)/National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) grant 1K08DK099415- 01A1, NIH/National Institute of General Medical Sciences grant P20GM104357, and NIH/National Institute of General Medical Sciences grant 5U54GM115428. This study was supported by research support from the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, National Institute of Aging GEMSSTAR grant (1R03AG067960-01), and Applied Therapeutics to Dr Pandey. Disclosures: Dr Grodin is a consultant for Pfizer, Eidos Therapeutics, and Alynlam Pharmaceuticals; and has received research funding from the Texas Health Resources Clinical Scholars fund. Dr De Lemos has received financial support from Roche Diagnostics and Abbott Diagnostics; and is a consultant for Ortho Clinical Diagnostics, Quidel, and Regeneron. Dr Berry has received financial support from Roche Diagnostics, Abbott Diagnostics, and the National Institutes of Health; is a consultant for Abbott and the Cooper Institute. Dr Butler is a consultant for Abbott, Adrenomed, Arena Pharma, Array, Amgen, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Cardior, CVRx, Eli Lilly, G3 Pharma, Imbria, Impulse Dynamics, Innolife, Janssen, LivaNova, Luitpold, Medtronic, Merck, Novartis, Novo Nordisk, Relypsa, Roche, Sequana Medical, V-Wave Limited, and Vifor. Dr Mentz has received financial support and honoraria from Abbott, American Regent, Amgen, AstraZeneca, Bayer, Boehringer Ingelheim/Eli Lilly, Boston Scientific, Cytokinetics, Fast BioMedical, Gilead, Medtronic, Merck, Novartis, Roche, Sanofi, and Vifor. Dr Sanjiv Shah has received financial support from Actelion, AstraZeneca, Corvia, Novartis, and Pfizer; and is a consultant for Abbott, Actelion, AstraZeneca, Amgen, Aria, Axon Therapeutics, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiora, CVRx, Cyclerion, Cytokinetics, Eisai, GlaxoSmithKline, Imara, Ionis, Ironwood, Keyto, Lilly, Merck, MyoKardia, Novartis, Novo Nordisk, Pfizer, Regeneron, Sanofi, Shifamed, Tenax, and United Therapeutics. Dr Amil Shah has received financial support from Novartis through Brigham and Women’s Hospital, and Philips Ultrasound through Brigham and Women’s Hospital, and personal fees from Philips Ultrasound Advisory Board outside the submitted work. Dr Pandey has served on the advisory board of Roche Diagnostics; and has received nonfinancial support from Pfizer and Merck. The views expressed in this paper are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services., (Copyright © 2022 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Obesity and diabetes are associated with a higher risk of heart failure (HF). The interrelationships between different measures of adiposity-overall obesity, central obesity, fat mass (FM)-and diabetes status for HF risk are not well-established., Methods: Participant-level data from the ARIC study (Atherosclerosis Risk in Communities; visit 5) and the CHS (Cardiovascular Health Study; visit 1) cohorts were obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center, harmonized, and pooled for the present analysis, excluding individuals with prevalent HF. FM was estimated in all participants using established anthropometric prediction equations additionally validated using the bioelectrical impedance-based FM in the ARIC subgroup. Incident HF events on follow-up were captured across both cohorts using similar adjudication methods. Multivariable-adjusted Fine-Gray models were created to evaluate the associations of body mass index (BMI), waist circumference (WC), and FM with risk of HF in the overall cohort as well as among those with versus without diabetes at baseline. The population attributable risk of overall obesity (BMI≥30 kg/m 2 ), abdominal obesity (WC>88 and 102 cm in women and men, respectively), and high FM (above sex-specific median) for incident HF was evaluated among participants with and without diabetes., Results: The study included 10 387 participants (52.9% ARIC; 25.1% diabetes; median age, 74 years). The correlation between predicted and bioelectrical impedance-based FM was high ( R 2 =0.90; n=5038). During a 5-year follow-up, 447 participants developed HF (4.3%). Higher levels of each adiposity measure were significantly associated with higher HF risk (hazard ratio [95% CI] per 1 SD higher BMI=1.15 [1.05, 1.27], WC=1.22 [1.10, 1.36]; FM=1.13 [1.02, 1.25]). A significant interaction was noted between diabetes status and measures of BMI ( P interaction=0.04) and WC ( P interaction=0.004) for the risk of HF. In stratified analysis, higher measures of each adiposity parameter were significantly associated with higher HF risk in individuals with diabetes (hazard ratio [95% CI] per 1 SD higher BMI=1.29 [1.14-1.47]; WC=1.48 [1.29-1.70]; FM=1.25 [1.09-1.43]) but not those without diabetes, including participants with prediabetes and euglycemia. The population attributable risk percentage of overall obesity, abdominal obesity, and high FM for incident HF was higher among participants with diabetes (12.8%, 29.9%, and 13.7%, respectively) versus those without diabetes (≤1% for each)., Conclusions: Higher BMI, WC, and FM are strongly associated with greater risk of HF among older adults, particularly among those with prevalent diabetes.

Aims: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs)., Methods and Results: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrell's concordance index (C-index) and calibration by the Greenwood-Nam-D'Agostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P ≥0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (<13) vs. high (≥13) WATCH-DM(i) scores [0.71 (95% confidence interval 0.68-0.74) vs. 0.64 (95% confidence interval 0.61-0.66)]. When NP levels were combined with the WATCH-DM(i) score, HF risk discrimination improvement and NRI varied across the spectrum of risk with greater improvement observed at low/intermediate risk [WATCH-DM(i) <13] vs. high risk [WATCH-DM(i) ≥13] (C-index = 0.73 vs. 0.71; NRI = 0.45 vs. 0.17)., Conclusion: The WATCH-DM risk score can accurately predict incident HF risk in community-based individuals with dysglycaemia. The addition of NP levels is associated with greater improvement in the HF risk prediction performance among individuals with low/intermediate risk than those with high risk., (© 2021 European Society of Cardiology.)

Importance: Cardiovascular (CV) mortality has declined for more than 3 decades in the US. However, differences in declines among residents at a US county level are not well characterized., Objective: To identify unique county-level trajectories of CV mortality in the US during a 35-year study period and explore county-level factors that are associated with CV mortality trajectories., Design, Setting, and Participants: This longitudinal cross-sectional analysis of CV mortality trends used data from 3133 US counties from 1980 to 2014. County-level demographic, socioeconomic, environmental, and health-related risk factors were compiled. Data were analyzed from December 2019 to September 2021., Exposures: County-level characteristics, collected from 5 county-level data sets., Main Outcomes and Measures: Cardiovascular mortality data were obtained for 3133 US counties from 1980 to 2014 using age-standardized county-level mortality rates from the Global Burden of Disease study. The longitudinal K-means approach was used to identify 3 distinct clusters based on underlying mortality trajectory. Multinomial logistic regression models were constructed to evaluate associations between county characteristics and cluster membership., Results: Among 3133 US counties (median, 49.5% [IQR, 48.9%-50.5%] men; 30.7% [IQR, 27.1%-34.4%] older than 55 years; 9.9% [IQR, 4.5%-22.7%] racial minority group [individuals self-identifying as Black or African American, American Indian or Alaska Native, Asian, Native Hawaiian, Pacific Islander, other, or multiple races/ethnicities]), CV mortality declined by 45.5% overall and by 38.4% in high-mortality strata (694 counties), by 45.0% in intermediate-mortality strata (1382 counties), and by 48.3% in low-mortality strata (1057 counties). Counties with the highest mortality in 1980 continued to demonstrate the highest mortality in 2014. Trajectory groups were regionally distributed, with high-mortality trajectory counties focused in the South and in portions of Appalachia. Low- vs high-mortality groups varied significantly in demographic (racial minority group proportion, 7.6% [IQR, 4.1%-14.5%]) vs 23.9% [IQR, 6.5%-40.8%]) and socioeconomic characteristics such as high-school education (9.4% [IQR, 7.3%-12.6%] vs 20.1% [IQR, 16.1%-23.2%]), poverty rates (11.4% [IQR, 8.8%-14.6%] vs 20.6% [IQR, 17.1%-24.4%]), and violent crime rates (161.5 [IQR, 89.0-262.4] vs 272.8 [IQR, 155.3-431.3] per 100 000 population). In multinomial logistic regression, a model incorporating demographic, socioeconomic, environmental, and health characteristics accounted for 60% of the variance in the CV mortality trajectory (R2 = 0.60). Sociodemographic factors such as racial minority group proportion (odds ratio [OR], 1.70 [95% CI, 1.35-2.14]) and educational attainment (OR, 6.17 [95% CI, 4.55-8.36]) and health behaviors such as smoking (OR for high vs low, 2.04 [95% CI, 1.58-2.64]) and physical inactivity (OR, 3.74 [95% CI, 2.83-4.93]) were associated with the high-mortality trajectory., Conclusions and Relevance: Cardiovascular mortality declined in all subgroups during the 35-year study period; however, disparities remained unchanged during that time. Disparate trajectories were associated with social and behavioral risks. Health policy efforts across multiple domains, including structural and public health targets, may be needed to reduce existing county-level cardiovascular mortality disparities.

Background: Diabetes is associated with abnormalities in cardiac remodeling and high risk of heart failure (HF)., Objectives: The purpose of this study was to evaluate the prevalence and prognostic implications of diabetes with cardiomyopathy (DbCM) among community-dwelling individuals., Methods: Adults without prevalent cardiovascular disease or HF were pooled from 3 cohort studies (ARIC [Atherosclerosis Risk In Communities], CHS [Cardiovascular Health Study], CRIC [Chronic Renal Insufficiency Cohort]). Among participants with diabetes, DbCM was defined using different definitions: 1) least restrictive: ≥1 echocardiographic abnormality (left atrial enlargement, left ventricle hypertrophy, diastolic dysfunction); 2) intermediate restrictive: ≥2 echocardiographic abnormalities; and 3) most restrictive: elevated N-terminal pro-B-type natriuretic peptide levels (>125 in normal/overweight or >100 pg/mL in obese) plus ≥2 echocardiographic abnormalities. Adjusted Fine-Gray models were used to evaluate the risk of HF., Results: Among individuals with diabetes (2,900 of 10,208 included), the prevalence of DbCM ranged from 67.0% to 11.7% in the least and most restrictive criteria, respectively. Higher fasting glucose, body mass index, and age as well as worse kidney function were associated with higher risk of DbCM. The 5-year incidence of HF among participants with DbCM ranged from 8.4%-12.8% in the least and most restrictive definitions, respectively. Compared with euglycemia, DbCM was significantly associated with higher risk of incident HF with the highest risk observed for the most restrictive definition of DbCM (HR: 2.55 [95% CI: 1.69-3.86]; least restrictive criteria HR: 1.99 [95% CI: 1.50-2.65]). A similar pattern of results was observed across cohort studies, across sex and race subgroups, and among participants without hypertension or obesity., Conclusions: Regardless of the criteria used to define cardiomyopathy, DbCM identifies a high-risk subgroup for developing HF., Competing Interests: Funding Support and Author Disclosures This study was supported an investigator-initiated research grant by Applied Therapeutics to UT Southwestern Medical Center, Dallas, Texas (principal investigator Dr. Pandey). The sponsors had no role in the study design, conduct, or manuscript preparation. Dr Segar has received nonfinancial support from Pfizer and Merck. Dr Butler is a consultant to Abbott, Adrenomed, Amgen, Applied Therapeutics, Array, AstraZeneca, Bayer, Boehringer Ingelheim, Cardior, CVRx, G3 Pharma, Impulse Dynamics, Innolife, Janssen, LivaNova, Luitpold, Medtronic, Merck, Novartis, NovoNordisk, Relypsa, Roche, Sanofi, Sequana Medical, and Vifor. Dr Tang has served as a consultant for Sequana Medical AG, Relypsa, PreCardia, Cardiol Therapeutics Inc, Genomics Plc, and Owkin Inc; and has served as an exam writing committee member for American Board of Internal Medicine. Dr Vaduganathan has received research grant support or served on advisory boards for American Regent, Amgen, AstraZeneca, Bayer AG, Baxter Healthcare, Boehringer Ingelheim, Cytokinetics, Lexicon Pharmaceuticals, and Relypsa; and has participated on clinical endpoint committees for studies sponsored by Galmed, Novartis, and the National Institutes of Health. Dr Lam has received grant support and advisory board fees from Boston Scientific and Roche Diagnostics; has received grant support, advisory board fees, and fees for serving on a steering committee from AstraZeneca; has received grant support from Medtronic; has received grant support and fees for serving on a steering committee from Vifor Pharma; has received advisory board fees and fees for serving on a steering committee from Novartis; has received advisory board fees from Amgen, Boehringer Ingelheim, Abbott Diagnostics, Novo Nordisk, Biofourmis, and MyoKardia; has received consulting fees from Stealth BioTherapeutics, Jana Care, Darma, Cytokinetics, WebMD Global, Radcliffe Group, and Corpus; has received fees for serving on a steering committee from Janssen Research and Development, Corvia Medical, and Applied Therapeutics; has received lecture fees and consulting fees from Menarini Group; holds a pending patent PCT/SG2016/050217 on a method for the diagnosis and prognosis of chronic heart failure; holds a pending patent 16/216,929 on an automatic clinical workflow that recognizes and analyzes 2-dimensional and Doppler modality echocardiography; and has received fees for serving as cofounder and nonexecutive director of eko.ai. Dr McGuire has had leadership roles in clinical trials for AstraZeneca, Boehringer Ingelheim, Eisai, Esperion, GlaxoSmithKline, Janssen, Lexicon, Merck and Co Inc, Novo Nordisk, CSL Behring, Lilly, USA and Sanofi USA; and has received consultancy fees from AstraZeneca, Boehringer Ingelheim, Lilly USA, Merck and Co Inc, Pfizer, Novo Nordisk, Metavant, Afimmune, Bayer, and Sanofi. Dr Pandey has served on the advisory board of Roche Diagnostics; has received nonfinancial support from Pfizer and Merck; and has received research support from the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, the National Institute on Aging GEMSSTAR Grant (1R03AG067960-01), Myovista, and Applied Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Objectives: The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF)., Background: The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise., Methods: The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346)., Results: The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001)., Conclusions: This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone., Competing Interests: Funding Support and Author Disclosures This study was supported partly by funds from the National Science Foundation (1920920) and an interinstitutional Smart Health Initiative. This study was judged as the winner of the 2020 Aurther Weyman Young Investigator Award presented at the annual Scientific Sessions of the ASE. This project was also the winner of the National Heart, Lung, and Blood Institute Big-Data Challenge: Creating New Paradigms for Heart Failure Research Award. Dr. Kagiyama was supported by a research grant from Hitachi Healthcare. Dr. Sengupta has served as a consultant to Ultromics and Kencor Health. Dr. Pandey has served on the advisory board of Roche Diagnostics; and is supported by research grants from the Texas Health Resources Clinical Scholarship, the Gilead Sciences Research Scholar Program, the National Institute on Aging GEMSSTAR Grant (1R03AG067960-01), and Applied Therapeutics. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: Evaluate the association between prefrailty and the risk of heart failure (HF) among older adults., Design, Setting, and Participants: This prospective, community-based cohort study included participants from the Atherosclerotic Risk in Communities study who underwent detailed frailty assessment using Fried Criteria and physical function assessment using the Short Performance Physical Battery (SPPB) score. Individuals with prevalent HF and frailty were excluded., Main Outcomes and Measures: Adjusted association between prefrailty (vs robust), physical function measures (SPPB score, grip strength, and gait speed), and incident HF (overall and HF subtypes, HF with reduced [HFrEF, EF < 50%] and preserved ejection fraction [HFpEF]) were assessed using Cox proportional hazards models., Results: Among 5210 participants (mean age 75 years, 58% women), 2565 (49.2%) were identified as prefrail. In cross-sectional analysis, prefrail individuals had a higher burden of chronic myocardial injury (troponin, Std β = 0.08 [0.05-0.10]) and neurohormonal stress (NT-ProBNP, Std β = 0.03 [0.02-0.05]) after adjustment for potential confounders. Over a median follow-up of 4.6 years, there were 232 (4.5%) HF events (HFrEF: 102; HFpEF: 97). Prefrailty was associated with an increased risk of HF after adjusting for potential clinical confounders and cardiac biomarkers (aHR [95% CI] = 1.65 [1.24-2.20]). Among HF subtypes, prefrailty was associated with an increased risk of HFpEF but not HFrEF (aHR [95% CI] = 1.73 [1.11-2.70] and 1.38 [0.90-2.10], respectively). A lower SPPB score was also associated with an increased risk of overall HF and HFpEF, but not HFrEF. Among individual components, increased gait speed were associated with a lower risk of HFpEF, but not HFrEF., Conclusions and Relevance: Subtle abnormalities in physiological reserve (prefrailty) and impairment in physical function (SPPB) were both significantly associated with a higher risk of incident HF, particularly HFpEF. These findings highlight the potential role of routine assessment of geriatric syndromes for early identification of HF risk., (© 2021 The American Geriatrics Society.)

Aims/hypothesis: Type 2 diabetes is a heterogeneous disease process with variable trajectories of CVD risk. We aimed to evaluate four phenomapping strategies and their ability to stratify CVD risk in individuals with type 2 diabetes and to identify subgroups who may benefit from specific therapies., Methods: Participants with type 2 diabetes and free of baseline CVD in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial were included in this study (N = 6466). Clustering using Gaussian mixture models, latent class analysis, finite mixture models (FMMs) and principal component analysis was compared. Clustering variables included demographics, medical and social history, laboratory values and diabetes complications. The interaction between the phenogroup and intensive glycaemic, combination lipid and intensive BP therapy for the risk of the primary outcome (composite of fatal myocardial infarction, non-fatal myocardial infarction or unstable angina) was evaluated using adjusted Cox models. The phenomapping strategies were independently assessed in an external validation cohort (Look Action for Health in Diabetes [Look AHEAD] trial: n = 4211; and Bypass Angioplasty Revascularisation Investigation 2 Diabetes [BARI 2D] trial: n = 1495)., Results: Over 9.1 years of follow-up, 789 (12.2%) participants had a primary outcome event. FMM phenomapping with three phenogroups was the best-performing clustering strategy in both the derivation and validation cohorts as determined by Bayesian information criterion, Dunn index and improvement in model discrimination. Phenogroup 1 (n = 663, 10.3%) had the highest burden of comorbidities and diabetes complications, phenogroup 2 (n = 2388, 36.9%) had an intermediate comorbidity burden and lowest diabetes complications, and phenogroup 3 (n = 3415, 52.8%) had the fewest comorbidities and intermediate burden of diabetes complications. Significant interactions were observed between phenogroups and treatment interventions including intensive glycaemic control (p-interaction = 0.042) and combination lipid therapy (p-interaction < 0.001) in the ACCORD, intensive lifestyle intervention (p-interaction = 0.002) in the Look AHEAD and early coronary revascularisation (p-interaction = 0.003) in the BARI 2D trial cohorts for the risk of the primary composite outcome. Favourable reduction in the risk of the primary composite outcome with these interventions was noted in low-risk participants of phenogroup 3 but not in other phenogroups. Compared with phenogroup 3, phenogroup 1 participants were more likely to have severe/symptomatic hypoglycaemic events and medication non-adherence on follow-up in the ACCORD and Look AHEAD trial cohorts., Conclusions/interpretation: Clustering using FMMs was the optimal phenomapping strategy to identify replicable subgroups of patients with type 2 diabetes with distinct clinical characteristics, CVD risk and response to therapies.