Retraction: The utility of biomarker risk prediction score in patients with chronic heart failure

Calling for Targeted Trials in Cardiorenal Syndromes

Stroke prevention in heart failure and sinus rhythm: where do we go from here?

Background In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) developed a score for assessment of cardiovascular risk. Due to between study variability in ascertainment and adjudication of heart failure (HF), incident HF was not included as an endpoint in the ACC/AHA risk score. Purpose To assess the performance of the ACC/AHA risk score for HF risk prediction in a large population-based cohort and to compare its performance with the existing HF risk prediction models including the Atherosclerosis Risk in Communities (ARIC) model and the Health Aging and Body Composition (Health ABC) model. Methods The study included 2743 men and 3646 women from a prospective population-based cohort study. Cox proportional hazards models were fitted using risk factors applied by the ACC/AHA model for cardiovascular risk, the ARIC model and the Health ABC model. Independent relationship of each predictor with 10-year HF incidence was estimated in men and women. Next, N-terminal pro-b-type natriuretic peptide (NT-pro-BNP) was added to the ACC/AHA model. The performance of all fitted models was evaluated and compared in terms of discrimination, calibration and the Akaike Information Criterion (AIC). In addition, area under the receiver operator characteristic curve (AUC), sensitivity and specificity of each model in predicting 10-year incident of HF was assessed. The incremental value of NT-pro-BNP to the ACC/AHA model, was assessed using the continuous net reclassification improvement index (NRI). Results During a median follow-up of 13 years (63127 person-years), 387 HF events in women and 259 in men were recorded. The Optimism-corrected c-statistic for ACC/AHA model was 0.76 (95% confidence interval (CI): 0.73–0.79) for men and 0.76 (95% CI: 0.74–0.79) for women. The ARIC model provided the largest c-statistic for both men [0.82 (95% CI: 0.80–0.84)] and women [95% CI: 0.81 (0.79–0.83)] among the three models. Calibration of the models was reasonable. Addition of NT-pro-BNP to the ACC/AHA model considerably improved model fitness for men and for women. The AIC improved from 3104.62 to 2976.28 among men and from 5161.63 to 4921.51 among women. The c-statistic also improved to 0.81 (0.78–0.84) in men and 0.79 (0.77–0.81) in women. The continuous NRI for the addition of NT-pro-BNP to the base model was 5.3% (95% CI: −12.3–28.6%) for men and 15.9% (95% CI: 2.7–24.7%) for women. Conclusions Compared to HF-specific models, the ACC/AHA model, containing routine clinically available risk factors, had a reasonable performance in prediction of HF risk. Inclusion of NT-pro-BNP in the ACC/AHA model strongly increased the model performance. To achieve a better model performance for 10-year prediction of incident HF, updating the simple ACC/AHA risk score with the addition of NT-pro-BNP is recommended.

Recurrent orthostatic syncope due to left atrial and left ventricular collapse after a continuous-flow left ventricular assist device implantation

Early Identification of Asymptomatic Subjects at Increased Risk of Heart Failure and Cardiovascular Events: Progress and Future Directions

Hospitalizations for New Heart Failure Among Subjects With Diabetes Mellitus in the RENAAL and LIFE Studies

Use of Polygenic Risk Score for Prediction of Heart Failure in Cancer Survivors

Assessment of quality of care in heart failure (HF) has focused on the development and use of process of care-based performance measures. While it has been presumed that these process measures when applied in actual clinical practice are associated with improved clinical outcomes, this link has not been well-established. A recent analysis of the Organized Program to Initiate Lifesaving Treatment In Hospitalized Patients With Heart Failure (OPTIMIZE-HF) 1 registry/performance improvement program examined the relationship between current performance measures for patients hospitalized with HF and relevant patient clinical outcomes. This study found that none of the current HF performance measures were significantly associated with reduced early mortality risk and only angiotensin-converting enzyme inhibitor/angiotensin receptor blocker use at discharge was associated with 60- to 90-day postdischarge mortality or rehospitalization. b-Blocker therapy at the time of hospital discharge, currently not an HF performance measure, was strongly associated with reduced risk of mortality and mortality/rehospitalization postdischarge. To accurately identify health care providers and hospitals providing care that is associated with more optimal clinical outcomes, additional HF performance measures as well as better methodology for identifying and validating performance measures is needed. HF is the leading cause of hospitalization in persons older than 65 years, with almost 3.6 million hospitalizations attributed to HF as the primary or a secondary discharge diagnosis each year. 2 HF patients are at substantial risk for recurrent exacerbations of symptoms requiring intervention, with up to 50% of discharged patients being rehospitalized within 6 months. An estimated 11.6% of HF patients die within 30 days and 33.1% of patients die within 1 year after their first hospitalization for HF. 3 Uniform highquality health care might reasonably be expected to reduce this burden of morbidity and mortality associated with HF. Evidence-based guidelines for the diagnosis and treatment of patients with HF have been developed. 2,4 To facilitate the measurement of and improvement in quality of care in HF, components of these guidelines have been adapted by the various organizations as performance measures. 5,6 These performance measures are based on clinical practice guidelines but are intended to be confined to those structural aspects or processes of care for which the evidence is so strong that the failure to perform them reduces the likelihood of optimal patient outcomes. 5 It is impor

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BackgroundThe Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) mortality risk score, derived from a large sample of patients with heart failure (HF) across the spectrum of ejection fraction (EF), has not yet been externally validated in a well‐characterized HF with preserved EF cohort with adjudicated morbidity outcomes.Methods and ResultsWe evaluated the MAGGIC risk score (composed of 13 clinical variables) in 407 patients with HF with preserved EF enrolled in a prospective registry and used Cox regression to evaluate its association with morbidity/mortality. We used receiver‐operating characteristic analysis to compare the predictive ability of the MAGGIC risk score with the more complex Seattle Heart Failure Model, and we determined the value of adding B‐type natriuretic peptide to the MAGGIC risk score for risk prediction. During a mean follow‐up time of 3.6±1.8 years, 28% died, 32% were hospitalized for HF, and 55% had a cardiovascular hospitalization and/or death. The MAGGIC score, a mean±SD of 18±7, was significantly associated with mortality (P<0.0001), HF hospitalizations (P<0.0001), and the combined end point of cardiovascular‐related hospitalizations or death (hazard ratio, 1.8 [95% confidence interval, 1.6–2.1], per 1‐SD increase in the MAGGIC score; P<0.0001). Receiver‐operating characteristic analyses showed that MAGGIC and Seattle Heart Failure Model performed similarly in predicting HF with preserved EF outcomes, but the MAGGIC score demonstrated better calibration for hospitalization outcomes. Further analyses showed that B‐type natriuretic peptide was additive to the MAGGIC risk score for predicting outcomes (P<0.01 by likelihood ratio test).ConclusionsThe MAGGIC risk score is a simple, yet powerful method of risk stratification for both morbidity and mortality in HF with preserved EF.Clinical Trial RegistrationURL: http://www.clinicaltrials.gov. Unique identifier: NCT01030991.

Background: The AI-CVD initiative aims to extract opportunistic screening information from coronary artery calcium (CAC) scans to maximize cardiovascular disease prediction beyond the traditional risk factors and the Agatston CAC score. Hypothesis: In 2024, the American Heart Association introduced the PREVENT heart failure (HF) risk score based on age, sex, systolic blood pressure, body mass index, glomerular filtration rate (GFR), diabetes, smoking, and anti-hypertensive medication consumption. We sought to compare PREVENT HF vs. AI-CVD risk scores for predicting HF in the Multi-Ethnic Study of Atherosclerosis (MESA). Method: AI-CVD platform is a collection of deep learning models targeting various componenets of a CAC scan (see figure 1). We applied AI-CVD to 4,554 CAC scans of asymptomatic MESA participants aged 45–84 years (46.9% male). We used selected AI-CVD outputs included cardiac chamber volumes, thoracic skeletal muscle volume and density, epicardial fat volume, percentage of lung emphysema (&lt;950 HU), and percentage of liver fat (&lt;40 HU). Clinical data comprised demographic and anthropometric characteristics, laboratory results, lifestyle factors, and electrocardiogram parameters. Embedded feature selection methods were applied to identify the most important predictors of HF. The AI-CVD risk score for incident HF was developed using FasterRisk, an interpretable machine learning technique. We then compared the performance of PREVENT HF vs. AI-CVD using the area under the receiver operating curve (AUC) and DeLong’s test for predicting HF. Results: After a median follow-up of 17.7 (IQR: 13.0-18.5) years, 265 (5.8%) cases were diagnosed with HF. Age, GFR, hypertension, anti-hypertensive medication consumption, smoking, microalbuminuria, diabetes, left atrial volume, ratio of left ventricle to right ventricle volume, left ventricular mass, CAC score, epicardial fat volume, and emphysema were selected features for predicting HF. The AUC for AI-CVD (AUC: 0.84 [95% CI:0.82-0.87]) was significantly (P &lt; 0.001) higher than for PREVENT HF (AUC: 0.77, 95% CI: 0.74-0.81) for 10-year HF prediction. Conclusion: By integrating AI-generated opportunistic screening biomarkers from CAC scans with clinical data, the AI-CVD risk score significantly outperformed the PREVENT risk score for HF prediction in MESA participants over 10 years.

Hypertensive heart disease is a major contributor to cardiovascular morbidity and mortality, especially in African Americans, in whom LV hypertrophy is 2 to 3-fold more common in the general population as compared with whites.1 In the classic paradigm of hypertensive heart disease, concentric hypertrophy (a nondilated, thick-walled left ventricle typically with a normal left ventricular ejection fraction [LVEF]) is a common precursor to LV failure (an increased LV volume with reduced LVEF).2 Although molecular triggers of this transition from concentric hypertrophy to failure have been the subject of intense investigation, there are no previous large, longitudinal cohort studies in humans demonstrating that this progression occurs frequently. The transition from concentric LV hypertrophy to failure has been well demonstrated in animal models including the spontaneously hypertensive rat,3 or after aortic banding4 or transgenic manipulation,5 and also in humans with aortic stenosis6 or familial hypertrophic cardiomyopathy.7 Whether this paradigm faithfully represents the natural history of hypertensive heart disease is not yet known (Figure). An alternative paradigm is that the LV response to elevated blood pressure is either hypertrophy or failure, with transition between the 2 uncommon in the absence of an interval cardiac injury. Potential pathways in progression of hypertensive heart disease. Hypertension can lead to concentric left ventricular hypertrophy (LVH), characterized by nondilated, thick-walled left ventricle (arrow, top left). After “transition to failure,” the LV is dilated with reduced LVEF. Coronary artery disease often via MI is a common contributor to this transition (first horizontal arrow). Whether concentric LVH commonly leads to low EF in absence of an interval MI or significant coronary artery disease is uncertain (second horizontal arrow). If LVH is not common precursor to LV …

Background Defining an individual’s heart failure (HF) risk is crucial in defining the need for aggressive risk reduction. Several scores to define HF risk have been developed and validated, but their performance across diverse populations has been inconsistent. Concurrently, AI-enabled electrocardiography (AI-ECG) is an emerging strategy for detecting subclinical cardiac dysfunction, with the potential to refine HF risk prediction beyond traditional risk scores. Objective To evaluate the relative performance of 2 key traditional risk scores for HF risk prediction, and evaluate the added role of AI-ECG in improving risk stratification for HF. Methods We used data from 2 large community-based prospective cohort studies, UK Biobank (UKB) from a high-income population and the ELSA-Brasil representing a middle-income population. We computed PREVENT-HF and PCP-HF scores for all adults without baseline HF, who were followed for incident HF (based on EHR events in UKB and adjudicated events in ELSA). We compared 2 AI-ECG models developed for cross-sectional markers of LV dysfunction for improving HF risk stratification (i) for LV systolic dysfunction, and (ii) for a structural heart disorders (SHD) composite, including LV systolic or diastolic dysfunction, or valvular heart disease. We computed the relative rate of events for AI-ECG-positive vs negative groups across tertiles of predicted risk. We used Cox proportional survival analyses to measure the hazard ratios (HR) for HF events for those with positive vs. negative AI-ECG predictions. Results There were 42,147 (mean age 64.6 SD 7.7, 51.7% female) individuals in the UK Biobank who had 231 HF events over median 7.1 years and 13,512 (mean age 52.1 SD 9.1, 54.5% female) individuals in the ELSA-Brasil who had 35 HF events over a median 5.4 years. PREVENT-HF overpredicted HF risk by 4-fold in the UKB and 6-fold in ELSA-Brasil (P/O ratio 4.3 and 6.5, respectively), with similar overestimation for the PCP-HF score (P/O ratios 3.6 and 5.9, respectively). Positive vs negative AI-ECG screen for both LVSD and SHD at baseline were associated with substantially elevated HF risk, independent of PREVENT (HRs of 5.5-12.4 for LVSD and 3.4-19.3 for SHD) and PCP-HF (HRs of 6.1-19.4 for LVSD and 4.2-26.7 for SHD) predicted risk. Within each predicted risk tertile, the percentage of HF events was consistently higher for AI-ECG-positive compared with AI-ECG-negative groups (Figure 1), with substantial reclassification of risk even among the highest-risk tertile for both PREVENT and PCP-HF, across both UKB and ELSA-Brasil (Figure 2). Conclusion AI-ECG tools that identify markers of LV dysfunction substantially improve risk stratification, identifying individuals at significantly higher HF risk within each risk group predicted by traditional HF risk scores. These findings suggest that AI-ECG predictions may complement traditional risk scores for improved HF risk stratification and enhanced clinical decision-making.Figure 1 Figure 2

Effective heart failure (HF) prevention strategies are needed, given concerning epidemiological trends1 and continued difficulty in identifying disease-modifying therapies for HF with preserved ejection fraction (HFpEF).2 However, focusing strategies on those with established risk factors for HF presents challenges because of the large numbers involved. Moreover, most of these patients are at relatively low risk, never developing HF. It is essential, therefore, to phenotype those at-risk patients who transition to symptomatic HF, identifying predictors of transition risk, in order to target preventative strategies appropriately. The St Vincent's screening TO Prevent-Heart Failure (STOP-HF) programme,3 a clinically proven and cost-effective approach to HF prevention,4 offers a valuable opportunity to obtain insight into risk stratification and new-onset HF over time, based on longitudinal follow-up of an at-risk cohort. This programme, an extension of the STOP-HF trial,3 utilises B-type natriuretic peptide (BNP)-based risk stratification and collaborative care between community physicians and specialist cardiology services to optimise risk factor control, prevent HF and monitor for the onset of symptomatic HF. Herein, we report on the baseline phenotype of at-risk patients who later developed HF within the STOP-HF cohort, and examine their change in natriuretic peptide over time. Moreover, we investigate whether ongoing surveillance for HF symptoms results in earlier HF diagnosis compared with patients diagnosed through routine referral pathways from the community. A total of 237 patients within the STOP-HF programme (no baseline HF, >40 years of age with one or more risk factors for HF) were followed for a median of 4.1 years. At initial assessment and approximately yearly review,3 patient history, clinical examination, HF risk factors, medications, Doppler echocardiogram and blood biochemistry, including BNP, were recorded. New-onset HF diagnosis was made by a staff cardiologist using established criteria5 and these patients were designated ‘transitioners’. Transitioners were compared firstly with those who did not develop HF (non-transitioners) and subsequently, following HF diagnosis, with community-based patients referred by family physicians to our rapid-access new diagnostic HF clinic. Propensity score matching was applied to match transitioners to non-transitioners by age, gender and follow-up, and change in BNP over time was examined. Summary statistics were mean or median (± standard deviation or interquartile range) and number (%). Differences between groups were tested using parametric/non-parametric tests as appropriate. Logistic regression analysis and net reclassification index were used to assess associates of transition to HF. Bootstrapping analyses used 10 000 iterations, and 2-sided P-values <0.05 were considered statistically significant. During a median follow-up of 4.1 years, 86 of the 2037 patients transitioned to HF (4.2%, 8.3 per 1000 patient-years). The majority (70.1%) of transitioners developed HFpEF. Univariate baseline characteristics of transitioners vs. non-transitioners are presented in Table 1. Transitioners were older, more likely to be male, and had higher baseline body mass index, BNP and creatinine than non-transitioners. Co-morbidities, including hypertension, were more prevalent in transitioners, however both groups had similar usage of renin–angiotensin–aldosterone-modifying therapies. Doppler echocardiography showed lower left ventricular ejection fraction and higher left ventricular mass index, left atrial volume index and E/E' ratio (all P < 0.001) in transitioners at baseline. Baseline BNP was a strong, independent associate of transition to HF in univariate and multivariate analyses (P < 0.001). Baseline BNP and increase in BNP over time were greater in transitioners than non-transitioners (P < 0.001) (Figure 1). Each unit increase in log10 BNP tripled the likelihood of transitioning and the magnitude of this estimate did not change in multivariable or stepwise models, suggesting a robust estimate. Following transition to symptomatic HF, the 86 transitioners were compared with 607 contemporaneous community-referred patients with newly diagnosed HF (Table 1). Although transitioners had higher prevalence of diabetes, hypertension and vascular disease, they were younger, and had lower heart rates, systolic blood pressure and BNP than community-referred patients at time of HF diagnosis. Atrial fibrillation and chronic obstructive pulmonary disease (COPD) were more prevalent in the community-referred cohort, reflecting its older, multi-morbid profile, with evidence of higher cardiovascular risk on presentation. This report provides original data on the incidence of transition to new-onset HF within the STOP-HF prevention programme, and establishes a baseline phenotype of patients transitioning to HF in this setting, with baseline BNP emerging as the strongest predictor of transition, confirming previous results.6, 7 A novel finding is the potential of change in BNP over time to further refine risk prediction. Similar observations have been made regarding BNP,8 but not in a heightened-risk cohort such as STOP-HF. Although further work is needed to understand the prognostic importance of temporal changes in BNP in these patients, it may allow HF risk to be tracked, prognosis to be refined, and the impact of therapeutic strategies to be assessed over time. Whilst elevated natriuretic peptides correlate with prognosis in both HF with reduced and preserved ejection fraction, natriuretic peptide levels are lower and can be normal in HFpEF, reflecting, among other issues, the complicating impact of co-morbidity in this phenotype, in particular the impact of obesity in lowering natriuretic peptides. Therefore, combining natriuretic peptides, left atrial volume index and, indeed, change in natriuretic peptides over time, may be of particular value in this setting. Furthermore, the data suggest that diagnosis of HF in the STOP-HF programme occurs at an earlier stage in the natural history of the syndrome, with less atrial fibrillation, less COPD and lower BNP than community-referred, newly diagnosed, HF patients. Delayed onward-referral from the community may be explained, in part, by the subtle, non-specific nature of presenting symptoms. Earlier HF diagnosis within a prevention programme provides opportunities to improve outcome using self-care advice and disease-modifying therapies where applicable. These findings underline the role of HF prevention programmes, such as STOP-HF, and may have particular relevance to primary care, where the vast majority of at-risk patients are managed. Traditional HF risk profiling in the community involves large numbers of patients, the majority of whom are at low transition risk. Characterization of the baseline phenotype of transitioners, and the observation that baseline BNP is the strongest independent predictor of transition risk and tracks risk over time, may enable community physicians to better define risk through serial BNP measurement in at-risk patients. Coupled with enhanced access to specialist cardiology care for patients at high transition risk, this may provide a platform for cost-effective and efficacious HF prevention efforts. Conflict of interest: none declared.

Predicting Heart Failure in Arrhythmogenic Right Ventricular Cardiomyopathy