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Heart failure (HF) poses a major global health challenge with rising prevalence, significant morbidity and mortality, and substantial associated healthcare costs. With aging of the population and an increasing burden of comorbidities, the complex interplay between cardiovascular, kidney, and metabolic risk factors have been thrust into the spotlight and have broadened the traditional focus from HF treatment to an increased emphasis on prevention. In recognition of the evolving HF landscape, the American Heart Association released the PREVENT models which are comprehensive risk assessment tools that estimate 10- and 30-year risk of incident cardiovascular disease and its subtypes, including atherosclerotic cardiovascular disease (ASCVD) and, for the first time, HF. While it is an accurate risk estimation tool and represents a step forward in improving risk stratification for primary prevention of HF, there remain several limitations and unknowns like model performance across disaggregated racial and ethnic groups, the role of traditional ASCVD vs. HF-specific risk factors, HF prediction among those with known ASCVD, and the use of traditional regression techniques in lieu of potentially more powerful machine learning-based modeling approaches. Furthermore, it remains unclear how to optimize risk estimation in clinical care. The emergence of multiple novel pharmacological therapies that prevent incident HF, including sodium-glucose co-transporter 2 (SGLT2) inhibitors, glucagon-like peptide 1 (GLP1) receptor agonists, and nonsteroidal mineralocorticoid receptor antagonists (MRAs), highlights the importance of accurate HF risk prediction. To provide HF prevention with these effective but costly therapies, we must understand the optimal strategy in sequencing and combining these therapies and prioritize patients at highest risk. Such implementation requires both accurate risk stratification and a better understanding of how to communicate risk to patients and providers. This state-of-the-art review aims to provide a comprehensive overview of recent trends in HF prevention, including risk assessment, care management strategies, and emerging and novel treatments. [ABSTRACT FROM AUTHOR]

The clinical and economic impact of heart failure (HF) is immense and will continue to rise due to the increasing prevalence of the disease. Despite the availability of guideline-recommended medications that improve mortality, reduce hospitalizations, and enhance quality of life, there are major gaps in the implementation of such care. Quality improvement interventions have generally focused on clinicians. While certain interventions have had modest success in improving the use of heart failure medications, they remain insufficient in optimizing HF care. Here, we discuss how patient-facing interventions can add value and supplement clinician-centered interventions. We discuss how digital health can be leveraged to create patient activation tools that create a larger, sustainable impact. Small studies have suggested the promise of digital tools for patient engagement and self-care, but there are also important barriers to the adoption of such interventions that we describe. We share key principles and strategies around the design and implementation of digital health innovations to maximize patient participation and engagement. By uniquely activating patients in their own care, digital health can unlock the full potential of both existing and new quality improvement initiatives to drive forward high-quality and equitable heart failure care. [ABSTRACT FROM AUTHOR]

Aims: Worsening heart failure (WHF) events occurring in non‐inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)‐based approach compared with traditional diagnostic coding for non‐inpatient clinical encounters and left ventricular ejection fraction (LVEF). Methods and results: We compared characteristics for encounters that did vs. did not meet WHF criteria, stratified by care setting [i.e. emergency department (ED) and observation stay]. Overall, 8407 (22%) encounters met NLP‐based criteria for WHF (3909 ED visits and 4498 observation stays). The use of an NLP‐derived definition adjudicated 3983 (12%) of non‐primary HF diagnoses as meeting consensus definitions for WHF. The most common diagnosis indicated in these encounters was dyspnoea. Results were primarily driven by observation stays, in which 2205 (23%) encounters with a secondary HF diagnosis met the WHF definition by NLP. Conclusions: The use of standard claims‐based adjudication for primary diagnosis in the non‐inpatient setting may lead to misclassification of WHF events in the ED and overestimate observation stays. Primary diagnoses alone may underestimate the burden of WHF in non‐hospitalized settings. [ABSTRACT FROM AUTHOR]

Clinical risk scores based on traditional risk factors of atherosclerosis correlate imprecisely to an individual's complex pathophysiological predisposition to atherosclerosis and provide limited accuracy for predicting major adverse cardiovascular events (MACE). Over the past two decades, computed tomography scanners and techniques for coronary computed tomography angiography (CCTA) analysis have substantially improved, enabling more precise atherosclerotic plaque quantification and characterization. The accuracy of CCTA for quantifying stenosis and atherosclerosis has been validated in numerous multicentre studies and has shown consistent incremental prognostic value for MACE over the clinical risk spectrum in different populations. Serial CCTA studies have advanced our understanding of vascular biology and atherosclerotic disease progression. The direct disease visualization of CCTA has the potential to be used synergistically with indirect markers of risk to significantly improve prevention of MACE, pending large-scale randomized evaluation. [ABSTRACT FROM AUTHOR]

Background and Aims Guideline-directed medical therapy (GDMT) is recommended before mitral valve transcatheter edge-to-edge repair (MTEER) in patients with heart failure (HF) and severe functional mitral regurgitation (FMR). Whether MTEER is being performed on the background of optimal GDMT in clinical practice is unknown. Methods Patients with left ventricular ejection fraction (LVEF) < 50% who underwent MTEER for FMR from 23 July 2019 to 31 March 2022 in the Society of Thoracic Surgeons/American College of Cardiology Transcatheter Valve Therapy Registry were identified. Pre-procedure GDMT utilization was assessed. Cox proportional hazards models were constructed to evaluate associations between pre-MTEER therapy (no/single, double, or triple therapy) and risk of 1-year mortality or HF hospitalization (HFH). Results Among 4199 patients across 449 sites, beta-blockers, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, mineralocorticoid receptor antagonists, and angiotensin receptor–neprilysin inhibitors were used in 85.1%, 44.4%, 28.6%, and 19.9% before MTEER, respectively. Triple therapy was prescribed for 19.2%, double therapy for 38.2%, single therapy for 36.0%, and 6.5% were on no GDMT. Significant centre-level variation in the proportion of patients on pre-intervention triple therapy was observed (0%–61%; adjusted median odds ratio 1.48 [95% confidence interval (CI) 1.25–3.88]; P <.001). In patients eligible for 1-year follow-up (n = 2014; 341 sites), the composite rate of 1-year mortality or HFH was lowest in patients prescribed triple therapy (23.0%) compared with double (24.8%), single (35.7%), and no (41.1%) therapy (P <.01 comparing across groups). Associations persisted after accounting for relevant clinical characteristics, with lower risk in patients prescribed triple therapy [adjusted hazard ratio (aHR) 0.73, 95% CI.55–.97] and double therapy (aHR 0.69, 95% CI.56–.86) before MTEER compared with no/single therapy. Conclusions Under one-fifth of patients with LVEF <50% who underwent MTEER for FMR in this US nationwide registry were prescribed comprehensive GDMT, with substantial variation across sites. Compared with no/single therapy, triple and double therapy before MTEER were independently associated with reduced risk of mortality or HFH 1 year after intervention. [ABSTRACT FROM AUTHOR]

The American College of Cardiology (ACC) Foundation, along with key specialty and subspecialty societies, conducted an appropriate use review of stress testing and anatomic diagnostic procedures for risk assessment and evaluation of known or suspected chronic coronary disease (CCD), formerly referred to as stable ischemic heart disease (SIHD). This document reflects an updating of the prior Appropriate Use Criteria (AUC) published for radionuclide imaging, stress echocardiography (echo), calcium scoring, coronary computed tomography angiography (CCTA), stress cardiac magnetic resonance (CMR), and invasive coronary angiography for SIHD. This is in keeping with the commitment to revise and refine the AUC on a frequent basis. As with the prior version of this document, rating of test modalities is provided side-by-side for a given clinical scenario. These ratings are explicitly not considered competitive rankings due to the limited availability of comparative evidence, patient variability, and the range of capabilities available in any given local setting1–4. This version of the AUC for CCD is a focused update of the prior version of the AUC for SIHD4. Key changes beyond the updated ratings based on new evidence include the following: Clinical scenarios related to preoperative testing were removed and will be incorporated into another AUC document under development. Some clinical scenarios and tables were removed in an effort to simplify the selection of clinical scenarios. Additionally, the flowchart of tables has been reorganized, and all clinical scenario tables can now be reached by answering a limited number of clinical questions about the patient, starting with the patient's symptom status. Several clinical scenarios have been revised to incorporate changes in other documents such as pretest probability assessment, atherosclerotic cardiovascular disease (ASCVD) risk assessment, syncope, and others. ASCVD risk factors that are not accounted for in contemporary risk calculators have been added as modifiers to certain clinical scenarios. The 64 clinical scenarios rated in this document are limited to the detection and risk assessment of CCD and were drawn from common applications or anticipated uses, as well as from current clinical practice guidelines.5 These clinical scenarios do not specifically address patients having acute chest pain episodes. They may, however, be applicable in the inpatient setting if the patient is not having an acute coronary syndrome and warrants evaluation for CCD. Using standardized methodology, clinical scenarios were developed to describe common patient encounters in clinical practice focused on common applications and anticipated uses of testing for CCD. Where appropriate, the scenarios were developed on the basis of the most current ACC/American Heart Association guidelines. A separate, independent rating panel scored the clinical scenarios in this document on a scale of 1 to 9, following a modified Delphi process consistent with the recently updated AUC development methodology. Scores of 7 to 9 indicate that a modality is considered appropriate for the clinical scenario presented, midrange scores of 4 to 6 indicate that a modality may be appropriate for the clinical scenario, and scores of 1 to 3 indicate that a modality is rarely appropriate. [ABSTRACT FROM AUTHOR]

Aims: The long‐term outcome in patients with heart failure (HF) after hospitalization may vary substantially depending on their age and left ventricular ejection fraction (LVEF). We aimed to assess the relative rates of cardiovascular death (CVD) and non‐CVD based on the age and how the rates differ under the updated LVEF classification system. Methods and results: Consecutively registered hospitalized patients with HF (N = 3558; 39.7% women with a mean age of 73.9 ± 13.3 years) were followed for a median of 2 (interquartile range, 0.8–3.1) years. The CVDs and non‐CVDs were evaluated based on age [young (<65 years), older (65–84 years), and very old (≥85 years)] and LVEF classification [HF with preserved EF (HFpEF; LVEF ≥50%) and non‐HFpEF (LVEF <50%)]. The adverse clinical events were adjudicated independently by a central committee. Overall, 1505 (42.3%) had HFpEF [young: n = 182 (12.1%), older: n = 894 (59.4%), very old: n = 429 (28.5%)], and 2053 (57.7%) had non‐HFpEF [young: n = 575 (28.0%), older: n = 1159 (56.5%), very old: n = 319 (15.5%)]. During the follow‐up, the crude incidence of all‐cause death was higher in non‐HFpEF than in HFpEF across all age groups (non‐HFpEF vs. HFpEF, young: 10.4% vs. 5.5%, log‐rank P = 0.10; older: 26.6% vs. 20.9%, log‐rank P = 0.002; very old: 36.7% vs. 31.7%, log‐rank P = 0.043). CVDs accounted for more than half of all deaths in non‐HFpEF (young 65.0%, older 64.2%, and very old 55.6%), whereas the proportion of CVDs remained less than half in HFpEF (young 50.0%, older 41.2%, very old 38.2%). HF readmission was associated with subsequent all‐cause death in non‐HFpEF [hazard ratio (HR): 1.72, 95% confidence interval (CI): 1.41–2.09, P < 0.001], but not in HFpEF (HR: 1.12, 95% CI: 0.87–1.43, P = 0.39). Conclusions: The probability of a non‐CVD increases in both LVEF categories with advancing age, but that it is greater in the HFpEF category. The findings indicate that mitigating CV‐related outcomes alone may be insufficient for treating HF in older population, particularly in the HFpEF category. [ABSTRACT FROM AUTHOR]

Objectives To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes. Study Design and Setting A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014–18. Participants Plan enrollees aged ≥50 years. Methods We applied the two algorithms to the cohort and assessed the scores' distributions alongside enrollees' 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine–Gray models, we evaluated the association between frailty scores and outcomes as well as the models' discriminatory ability. Results Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98–2.21) for mortality and 2.45 (2.28–2.63) for LTC for eFI; those for HFRS were 3.79 (3.56–4.03) and 3.31 (2.87–3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48 months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS. Conclusions The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use. [ABSTRACT FROM AUTHOR]

Background: There are theoretical concerns that angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) could increase the risk of severe Covid-19. Objective: To determine if ACEIs and ARBs are associated with an increased risk of Covid-19 hospitalization overall, or hospitalization involving intensive care unit (ICU) admission, invasive mechanical ventilation, or death. Design: Observational case-control study. Participants: Medicare beneficiaries aged ≥ 66 years with hypertension, treated with ACEIs, ARBs, calcium channel blockers (CCBs), or thiazide diuretics. Main Measures: Adjusted odds ratios (OR) and 95% confidence intervals (CI) for the outcomes of Covid-19 hospitalization, or hospitalization involving ICU admission, invasive mechanical ventilation, or death. Results: A total of 35,300 cases of hospitalized Covid-19 were matched to 228,228 controls on calendar date and neighborhood of residence. The median age of cases was 79 years, 57.4% were female, and the median duration of hospitalization was 8 days (interquartile range 5–12). ACEIs and ARBs were associated with a slight reduction in Covid-19 hospitalization risk compared with treatment with other first-line antihypertensives (OR for ACEIs 0.95, 95% CI 0.92–0.98; OR for ARBs 0.94, 95% CI 0.90–0.97). Similar results were obtained for hospitalizations involving ICU admission, invasive mechanical ventilation, or death. There were no meaningful differences in risk for ACEIs compared with ARBs. In an analysis restricted to monotherapy with a first-line agent, CCBs were associated with a small increased risk of Covid-19 hospitalization compared with ACEIs (OR 1.09, 95% CI 1.04–1.14), ARBs (OR 1.10, 95% CI 1.05–1.15), or thiazide diuretics (OR 1.11, 95% CI 1.03–1.19). Conclusions: ACEIs and ARBs were not associated with an increased risk of Covid-19 hospitalization or with hospitalization involving ICU admission, invasive mechanical ventilation, or death. The finding of a small increased risk of Covid-19 hospitalization with CCBs was unexpected and could be due to residual confounding. [ABSTRACT FROM AUTHOR]

Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = −2.86; Cohen's Kappa = 0.89, P < 0.0001) with current conventional manual scoring on a retrospective dataset of 79 patients and was found to perform the task faster (average time for automated CAC scoring using a graphics processing unit (GPU) was 3.5 ± 2.1 s vs. 261 s for manual scoring) in a prospective trial of 55 patients with little difference in scores compared to three technologists (mean difference in scores = 3.24, 5.12, and 5.48, respectively). Then using CAC scores from paired gated coronary CT as a reference standard, we trained a deep learning model on our internal data and a cohort from the Multi-Ethnic Study of Atherosclerosis (MESA) study (total training n = 341, Stanford test n = 42, MESA test n = 46) to perform CAC scoring on routine non-gated chest CT exams with validation on external datasets (total n = 303) obtained from four geographically disparate health systems. On identifying patients with any CAC (i.e., CAC ≥ 1), sensitivity and PPV was high across all datasets (ranges: 80–100% and 87–100%, respectively). For CAC ≥ 100 on routine non-gated chest CTs, which is the latest recommended threshold to initiate statin therapy, our model showed sensitivities of 71–94% and positive predictive values in the range of 88–100% across all the sites. Adoption of this model could allow more patients to be screened with CAC scoring, potentially allowing opportunistic early preventive interventions. [ABSTRACT FROM AUTHOR]

The author reflects on the survival benefits of patients who underwent heart transplants in transplant centers in the U.S. from 2005 to 2017. Topics include the mortality rate of patients on the transplant waiting list, the effectiveness of centrifugal flow left ventricular assist device (LVAD) implantation in reducing risk of stroke or reoperation, and the difference between waiting list mortality and posttransplant mortality among the patients.

Background: Sacubitril-valsartan therapy reduces cardiovascular mortality compared with enalapril therapy in patients with heart failure with reduced ejection fraction.Objective: To evaluate the cost-effectiveness of sacubitril-valsartan versus angiotensin-converting enzyme inhibitor therapy in patients with chronic heart failure.Design: Markov decision model.Data Sources: Clinical trials, observational analyses, reimbursement data from the Centers for Medicare & Medicaid Services, drug pricing databases, and Centers for Disease Control and Prevention life tables.Target Population: Patients at an average age of 64 years, New York Heart Association (NYHA) class II to IV heart failure, and left ventricular ejection fraction of 0.40 or less.Time Horizon: Lifetime.Perspective: Societal.Intervention: Treatment with sacubitril-valsartan or lisinopril.Outcome Measures: Life-years, quality-adjusted life-years (QALYs), costs, heart failure hospitalizations, and incremental cost-effectiveness ratios.Results of Base-case Analysis: The sacubitril-valsartan group experienced 0.08 fewer heart failure hospitalization, 0.69 additional life-year, 0.62 additional QALY, and $29 203 in incremental costs, equating to a cost per QALY gained of $47 053. The cost per QALY gained was $44 531 in patients with NYHA class II heart failure and $58 194 in those with class III or IV heart failure.Results of Sensitivity Analysis: Sacubitril-valsartan treatment was most sensitive to the duration of improved outcomes, with a cost per QALY gained of $120 623 if the duration was limited to the length of the trial (median, 27 months). No variations in other parameters caused the cost to exceed $100 000 per QALY gained.Limitation: The benefit of sacubitril-valsartan is based on a single clinical trial.Conclusion: Treatment with sacubitril-valsartan provides reasonable value in reducing cardiovascular mortality and morbidity in patients with NYHA class II to IV heart failure.Primary Funding Source: U.S. Department of Veterans Affairs and Institute for Clinical and Economic Review. [ABSTRACT FROM AUTHOR]

Background: In 2014, the American Board of Internal Medicine (ABIM) substantially increased the requirements and fees for its maintenance-of-certification (MOC) program. Faced with mounting criticism, the ABIM suspended certain content requirements in February 2015 but retained the increased fees and number of modules. An objective appraisal of the cost of MOC would help inform upcoming consultations about MOC reform.Objective: To estimate the total cost of the 2015 version of the MOC program ("2015 MOC") and the incremental cost relative to the 2013 version ("2013 MOC").Design: Decision analytic model.Data Sources: Published literature.Target Population: All ABIM-certified U.S. physicians.Time Horizon: 10 years (2015 to 2024).Perspective: Societal.Intervention: 2015 MOC.Outcome Measures: Testing costs (ABIM fees) and time costs (monetary value of physician time).Results Of Base-case Analysis: Internists will incur an average of $23 607 (95% CI, $5380 to $66 383) in MOC costs over 10 years, ranging from $16 725 for general internists to $40 495 for hematologists-oncologists. Time costs account for 90% of MOC costs. Cumulatively, 2015 MOC will cost $5.7 billion over 10 years, $1.2 billion more than 2013 MOC. This includes $5.1 billion in time costs (resulting from 32.7 million physician-hours spent on MOC) and $561 million in testing costs.Results Of Sensitivity Analysis: Costs are sensitive to time spent on MOC and MOC credits obtainable from current continuing education activities.Limitation: Precise estimates of time required for MOC are not available.Conclusion: The ABIM MOC program will generate considerable costs, predominantly due to demands on physician time. A rigorous evaluation of its effect on clinical and economic outcomes is warranted to balance potential gains in health care quality and efficiency against the high costs identified in this study.Primary Funding Source: University of California, San Francisco, and the U.S. Department of Veterans Affairs. [ABSTRACT FROM AUTHOR]