Heart Failure Risk Prediction Research Articles

Machine learning in the prevention of heart failure.

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.

Advances in AI-based combination therapies versus standard treatments for heart failure in patients with advanced kidney injury

Background: Recent advancements in AI and Machine Learning (ML) can help manage kidney disease and heart failure by providing predictive analytics and personalized treatment plans, improving diagnostic accuracy, and assisting clinicians in controlling chronic disease symptoms like acute kidney injury (AKI) and Managing Heart Failure (HF). We evaluated AI and ML role in predicting AKI and managing HF patients and it involved analyzing various predictive models and technologies to improve early detection provide personalized treatment, and enhance overall patient outcomes in complex renal and cardiac conditions. Methodology: We conducted our search on databases Scopus, PubMed, and Web of Science. Empirical evidence and state of the art of cutting-edge literature and real-time field research was analyzed. Results: AI advancements in HF management for advanced kidney disease AKI include unsupervised machine learning for risk stratification, reinforcement learning for predictive modeling and dynamic management, and LLMs and chatbots for diagnostic support and patient education. New developments in AI and medical technology are predictive analytics for AKI, automated ultrasound interpretation, AI-enhanced dialysis monitoring, HF risk prediction, remote monitoring, and telehealth integration, and making personal treatment strategies according to patient needs. Innovations like wearable dialysis devices, bioengineered kidney tissues, ECMO, LVADs, TAVR, SGLT2 inhibitors, CRT, and nephroprotective drugs have also improved patient outcomes as these have enabled personalized care and early intervention strategies. Conclusion: It is possible to conclude that AI has revolutionized AKI and HF management. Novel technologies offer precise and adaptive care that strictly addresses individual patient needs.

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population.

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction. While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Heart Failure Risk Prediction in a Population With a High Burden of Diabetes: Evidence From the Strong Heart Study.

Despite the high burden of diabetes and cardiovascular risk factors in American Indian communities in the United States, prospective studies of heart failure (HF) in this population group are scarce, and the generalizability of previous HF risk scales may be limited. We developed a parsimonious HF risk prediction equation that accounts for relevant risk factors affecting American Indian communities, focusing on diabetes and kidney damage. A total of 3059 participants from the SHS (Strong Heart Study) (56±8 years of age, 58% women) were included. Five hundred seven developed HF. Progressively adjusted Cox proportional hazards models were used to identify risk factors for HF and HF subtypes. Predictors of risk at 5 and 10 years included older age (hazard ratio [HR], 1.79 [95% CI, 1.43-2.25]; HR, 1.68 [95% CI, 1.44-1.95]), smoking (HR, 2.26 [95% CI, 1.23-4.13]; HR, 2.08 [95% CI, 1.41-3.06]), macroalbuminuria (HR, 8.38 [95% CI, 4.44-15.83]; HR, 5.20 [95% CI, 3.42-7.9]), microalbuminuria (HR, 2.72 [95% CI, 1.51-4.90]; HR, 1.92 [95% CI, 1.33, 2.78]), and previous myocardial infarction (HR, 6.58 [95% CI, 2.54-17.03]; HR, 3.87 [95% CI, 2.29-6.54]), respectively. These predictors, together with diabetes diagnosis and glycated hemoglobin were significant at 10 and 28 years. High discrimination performance was achieved (C index, 0.81 [95% CI, 0.76-0.84]; C index, 0.78 [95% CI, 0.75-0.81]; and C index, 0.77 [95% CI, 0.74-0.78] at 5, 10, and up to 28 years of follow up, respectively). Some associations varied across HF subtypes, although diabetes, albuminuria, and previous myocardial infarction were associated with all subtypes. This prospective study of HF risk factors in American Indian communities identifies that smoking, body mass index, and indicators of diabetes control and kidney damage (glycated hemoglobin and albuminuria) are major determinants of HF. Our findings can improve HF risk assessment in populations with a high burden of diabetes.

Development a nomogram prognostic model for survival in heart failure patients based on the HF-ACTION data

BackgroundThe risk assessment for survival in heart failure (HF) remains one of the key focuses of research. This study aims to develop a simple and feasible nomogram model for survival in HF based on the Heart Failure-A Controlled Trial Investigating Outcomes of Exercise TraiNing (HF-ACTION) to support clinical decision-making.MethodsThe HF patients were extracted from the HF-ACTION database and randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Multivariate Cox regression was used to identify and integrate significant prognostic factors to form a nomogram, which was displayed in the form of a static nomogram. Bootstrap resampling (resampling = 1000) and cross-validation was used to internally validate the model. The prognostic performance of the model was measured by the concordance index (C-index), calibration curve, and the decision curve analysis.ResultsThere were 1394 patients with HF in the overall analysis. Seven prognostic factors, which included age, body mass index (BMI), sex, diastolic blood pressure (DBP), exercise duration, peak exercise oxygen consumption (peak VO2), and loop diuretic, were identified and applied to the nomogram construction based on the training cohort. The C-index of this model in the training cohort was 0.715 (95% confidence interval (CI): 0.700, 0.766) and 0.662 (95% CI: 0.646, 0.752) in the validation cohort. The area under the ROC curve (AUC) value of 365- and 730-day survival is (0.731, 0.734) and (0.640, 0.693) respectively in the training cohort and validation cohort. The calibration curve showed good consistency between nomogram-predicted survival and actual observed survival. The decision curve analysis (DCA) revealed net benefit is higher than the reference line in a narrow range of cutoff probabilities and the result of cross-validation indicates that the model performance is relatively robust.ConclusionsThis study created a nomogram prognostic model for survival in HF based on a large American population, which can provide additional decision information for the risk prediction of HF.

Advancements in Incident Heart Failure Risk Prediction and Screening Tools

Heart Failure (HF) is a major cause of mortality and morbidity in the United States that carries substantial healthcare costs. Multiple risk prediction models and strategies have been developed over the past 30-years with the aim to identify those at high risk of developing HF and implement preventive therapies effectively. This review highlights recent developments in HF risk prediction tools including emerging risk factors, innovative risk prediction models, and novel screening strategies from AI to biomarkers. These developments allow for more accurate prediction but their impact on clinical outcomes remain to be investigated. Implementation of these risk models into clinical practice is a considerable challenge, but HF risk prediction tools offer a promising opportunity to improve outcomes while maintaining value.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Echocardiography-Derived Forward Left Ventricular Output Improves Risk Prediction in Systolic Heart Failure

Although it is widely used to classify patients with heart failure (HF), the prognostic role of left ventricular ejection fraction (LVEF) is debated. The aim of this study was to test the hypothesis that echocardiographic measures of forward left ventricular (LV) output, being more representative of cardiac hemodynamics, might improve risk prediction in a large cohort of patients with HF with systolic dysfunction. Consecutive stable patients with HF with LVEF <50% on guideline-recommended therapies undergoing echocardiography including the evaluation of forward LV output (i.e., LV outflow tract [LVOT] velocity-time integral [VTI], stroke volume index [SVi], and cardiac index) over a 6-year period were selected and followed for the end point of cardiac and all-cause death. Among the 1,509 patients analyzed (mean age, 71±12years; 75% men; mean LVEF, 35±9%), 328 (22%) died during a median follow-up period of 28 months (interquartile range, 14-40 months), 165 (11%) of cardiac causes. On multivariable regression analysis, LVOT VTI (P<.001), SVi (P<.001), and cardiac index (P<.001), but not LVEF (P>.05), predicted cardiac and all-cause death. The optimal prognostic cutoffs for LVOT VTI, SVi, and cardiac index were 15cm, 38mL/m2, and 2L/min/m2, respectively. Adding each of these measures to a multivariable risk model (including clinical, biohumoral, and echocardiographic markers) improved risk prediction (P<.001). Among the different measures of forward LV output, cardiac index was less accurate than LVOT VTI and SVi. The echocardiographic evaluation of forward LV output improves risk prediction in patients with HF across a wide LVEF spectrum over other well-established clinical, biohumoral, and echocardiographic prognostic markers.

New joint model for the dynamic prediction of heart failure in phospholamban (PLN) p.(Arg14del) positive individuals

Abstract Background PLN p.(Arg14del) positive individuals are at risk for developing arrhythmogenic cardiomyopathy which can cause severe heart failure. Future gene therapy could be lifesaving and is rapidly advancing. It is therefore of importance to identify eligible patients for this evolving treatment, with individuals risk prediction playing an imperative role in this process. Purpose To develop a dynamic prediction model for the individual risk prediction of heart failure in PLN p.(Arg14del) positive individuals. Method Data were collected of 564 PLN p.(Arg14del) positive individuals with no baseline history of heart failure or myocardial infarction. During a median follow-up of 6.3 years (interquartile range 2.9-10.0), 74 individuals experienced heart failure, defined as a composite endpoint of heart failure hospitalization, left ventricle assist device implantation, heart transplantation and heart failure-related death. For the individual prediction of time to heart failure we used a joint model which simultaneously estimates a longitudinal model for repeatedly measured left ventricle ejection fraction (LVEF) during follow-up and a Cox regression model for the hazards of the heart failure endpoint. Using the longitudinal model we predicted the LVEF-values of all patients at future time points using the estimated random-effects and these predicted LVEF values were used as a time-dependent covariate in the Cox model which also contained age at start of follow-up as predictor variable. Results The joint model trained in this study had a very good performance in discriminating the individuals at risk for heart failure, with the 5-year mean AUC of 0.86 and 10-year mean AUC of 0.84. The LVEF had a hazard ratio of 0.89 (95% confidence interval (CI) 0.86-0.93, p&amp;lt;0.001), and age at baseline 1.04 (95% CI 1.03-1.06], p&amp;lt;0.001), meaning a lower LVEF and higher age at presentation were associated with heart failure. Figure 1 shows an example of an individual risk prediction with on the x-axis the follow-up time in years, on the y-axis (left) the LVEF in % and on the y-axis (right) the predicted survival probability. Conclusion Individual risk prediction with a simple model including only LVEF and age is of great importance for identifying PLN p.(Arg14del) positive individuals who are at risk of developing heart failure. The introduction of this novel joint prediction model is the first step in identifying individuals at risk for heart failure and may aid patient selection for future gene therapy interventions.

Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns.

In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.

Genetic susceptibility to chronic diseases leads to heart failure among Europeans: the influence of leukocyte telomere length.

Genetic susceptibility to various chronic diseases has been shown to influence heart failure (HF) risk. However, the underlying biological pathways, particularly the role of leukocyte telomere length (LTL), are largely unknown. We investigated the impact of genetic susceptibility to chronic diseases and various traits on HF risk, and whether LTL mediates or modifies the pathways. We conducted prospective cohort analyses on 404 883 European participants from the UK Biobank, including 9989 incident HF cases. Multivariable Cox regression was used to estimate associations between HF risk and 24 polygenic risk scores (PRSs) for various diseases or traits previously generated using a Bayesian approach. We assessed multiplicative interactions between the PRSs and LTL previously measured in the UK Biobank using quantitative PCR. Causal mediation analyses were conducted to estimate the proportion of the total effect of PRSs acting indirectly through LTL, an integrative marker of biological aging. We identified 9 PRSs associated with HF risk, including those for various cardiovascular diseases or traits, rheumatoid arthritis (P = 1.3E-04), and asthma (P = 1.8E-08). Additionally, longer LTL was strongly associated with decreased HF risk (P-trend = 1.7E-08). Notably, LTL strengthened the asthma-HF relationship significantly (P-interaction = 2.8E-03). However, LTL mediated only 1.13% (P < 0.001) of the total effect of the asthma PRS on HF risk. Our findings shed light onto the shared genetic susceptibility between HF risk, asthma, rheumatoid arthritis, and other traits. Longer LTL strengthened the genetic effect of asthma in the pathway to HF. These results support consideration of LTL and PRSs in HF risk prediction.

Serum metabolomics improves risk stratification for incident heart failure.

Prediction and early detection of heart failure (HF) is crucial to mitigate its impact on quality of life, survival, and healthcare expenditure. Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance [1H-NMR] spectroscopy) for incident HF. Leveraging data of 68 311 individuals and >0.8 million person-years of follow-up from the UK Biobank cohort, we (i) fitted per-metabolite Cox proportional hazards models to assess individual metabolite associations, and (ii) trained and validated elastic net models to predict incident HF using the serum metabolome. We benchmarked discriminative performance against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF [PCP-HF]). During a median follow-up of ≈12.3 years, several metabolites showed independent association with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈80% feature reduction). Adding metabolomics to PCP-HF improved predictive performance (Harrel's C: 0.768 vs. 0.755, ΔC = 0.013, [95% confidence interval [CI] 0.004-0.022], continuous net reclassification improvement [NRI]: 0.287 [95% CI 0.200-0.367], relative integrated discrimination improvement [IDI]: 17.47% [95% CI 9.463-27.825]). Models including age, sex and metabolomics performed almost as well as PCP-HF (Harrel's C: 0.745 vs. 0.755, ΔC = 0.010 [95% CI -0.004 to 0.027], continuous NRI: 0.097 [95% CI -0.025 to 0.217], relative IDI: 13.445% [95% CI -10.608 to 41.454]). Risk and survival stratification was improved by integrating metabolomics. Serum metabolomics improves incident HF risk prediction over PCP-HF. Scores based on age, sex and metabolomics exhibit similar predictive power to clinically-based models, potentially offering a cost-effective, standardizable, and scalable single-domain alternative.

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide, with a high associated economic burden. This study aimed to assess whether artificial intelligence models incorporating clinical, stress test, and imaging parameters could predict hospitalization for acute HF exacerbation in patients undergoing SPECT/CT myocardial perfusion imaging. Methods: The HF risk prediction model was developed using data from 4,766 patients who underwent SPECT/CT at a single center (internal cohort). The algorithm used clinical risk factors, stress variables, SPECT imaging parameters, and fully automated deep learning-generated calcium scores from attenuation CT scans. The model was trained and validated using repeated hold-out (10-fold cross-validation). External validation was conducted on a separate cohort of 2,912 patients. During a median follow-up of 1.9 y, 297 patients (6%) in the internal cohort were admitted for HF exacerbation. Results: The final model demonstrated a higher area under the receiver-operating-characteristic curve (0.87 ± 0.03) for predicting HF admissions than did stress left ventricular ejection fraction (0.73 ± 0.05, P < 0.0001) or a model developed using only clinical parameters (0.81 ± 0.04, P < 0.0001). These findings were confirmed in the external validation cohort (area under the receiver-operating-characteristic curve: 0.80 ± 0.04 for final model, 0.70 ± 0.06 for stress left ventricular ejection fraction, 0.72 ± 0.05 for clinical model; P < 0.001 for all). Conclusion: Integrating SPECT myocardial perfusion imaging into an artificial intelligence-based risk assessment algorithm improves the prediction of HF hospitalization. The proposed method could enable early interventions to prevent HF hospitalizations, leading to improved patient care and better outcomes.

Global longitudinal strain in long-term risk prediction after acute coronary syndrome: an investigation of added prognostic value to ejection fraction.

This study aimed to investigate the additional value of global longitudinal strain (GLS) on top of left ventricular ejection fraction (LVEF) in long-term risk prediction of combined death and heart failure (HF) re-hospitalization after acute coronary syndrome (ACS). This retrospective study included patients admitted with ACS between 2008 and 2014 from the three participating university hospitals. LVEF and GLS were assessed at a core lab from images acquired during the index hospital stay. Their prognostic value was studied with the Cox proportional hazards model (median follow-up 6.2years). A nested model comparison was performed with C-statistics. A total of 941 patients qualified for multivariable analysis after multiple imputation of missing baseline covariables. The combined outcome was reached in 17.7% of the cases. Both GLS and LVEF were independent predictors of the combined outcome, hazard ratio (HR) 1.068 (95% CI 1.017-1.121) and HR 0.980 (95% CI 0.962-0.998), respectively. The C-statistic increased from 0.742 (95% CI 0.702-0.783) to 0.749 (95% CI 0.709-0.789) (P = 0.693) when GLS entered the model with clinical data and LVEF. GLS emerged as an independent long-term risk predictor of all-cause death and HF re-hospitalization. However, there was no significant incremental predictive value of GLS when LVEF was already known.

Metabolomic Association and Risk Prediction With Heart Failure in Older Adults.

Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults. Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication. Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05). Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

Examining arterial pulsation to identify and risk-stratify heart failure subjects with deep neural network

Hemodynamic parameters derived from pulse wave analysis have been shown to predict long-term outcomes in patients with heart failure (HF). Here we aimed to develop a deep-learning based algorithm that incorporates pressure waveforms for the identification and risk stratification of patients with HF. The first study, with a case–control study design to address data imbalance issue, included 431 subjects with HF exhibiting typical symptoms and 1545 control participants with no history of HF (non-HF). Carotid pressure waveforms were obtained from all the participants using applanation tonometry. The HF score, representing the probability of HF, was derived from a one-dimensional deep neural network (DNN) model trained with characteristics of the normalized carotid pressure waveform. In the second study of HF patients, we constructed a Cox regression model with 83 candidate clinical variables along with the HF score to predict the risk of all-cause mortality along with rehospitalization. To identify subjects using the HF score, the sensitivity, specificity, accuracy, F1 score, and area under receiver operating characteristic curve were 0.867, 0.851, 0.874, 0.878, and 0.93, respectively, from the hold-out cross-validation of the DNN, which was better than other machine learning models, including logistic regression, support vector machine, and random forest. With a median follow-up of 5.8 years, the multivariable Cox model using the HF score and other clinical variables outperformed the other HF risk prediction models with concordance index of 0.71, in which only the HF score and five clinical variables were independent significant predictors (p < 0.05), including age, history of percutaneous coronary intervention, concentration of sodium in the emergency room, N-terminal pro-brain natriuretic peptide, and hemoglobin. Our study demonstrated the diagnostic and prognostic utility of arterial waveforms in subjects with HF using a DNN model. Pulse wave contains valuable information that can benefit the clinical care of patients with HF.

A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure.

Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations. Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P<0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P<0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF. The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.

Biomarkers for Heart Failure Prediction and Prevention.

Heart failure (HF) is a global pandemic affecting over 64 million people worldwide. Its prevalence is on an upward trajectory, with associated increasing healthcare expenditure. Organizations including the American College of Cardiology (ACC) and the American Heart Association (AHA) have identified HF prevention as an important focus. Recently, the ACC/AHA/Heart Failure Society of America (HFSA) Guidelines on heart failure were updated with a new Class IIa, Level of Evidence B recommendation for biomarker-based screening in patients at risk of developing heart failure. In this review, we evaluate the studies that have assessed the various roles and contributions of biomarkers in the prediction and prevention of heart failure. We examined studies that have utilized biomarkers to detect cardiac dysfunction or abnormality for HF risk prediction and screening before patients develop clinical signs and symptoms of HF. We also included studies with biomarkers on prognostication and risk prediction over and above existing HF risk prediction models and studies that address the utility of changes in biomarkers over time for HF risk. We discuss studies of biomarkers to guide management and assess the efficacy of prevention strategies and multi-biomarker and multimodality approaches to improve risk prediction.

Longitudinal modeling of cardiac function to identify trajectories with differential risk of heart failure

Abstract Background Impairments in left ventricular (LV) systolic and diastolic function are common precursors of clinical heart failure (HF) and underlie HF risk. Little data exist regarding trajectories of cardiac function in mid- to late-life, when HF risk is greatest, and the extent to which such trajectories may differentially relate to risk of HF with preserved ejection fraction (HFpEF) or HF with reduced ejection fraction (HFrEF). Purpose To (1) identify multidimensional trajectories of cardiac function from mid- to late-life using serial echocardiographic data, (2) apply the resulting models to predict trajectory membership in a separate sample, and (3) determine associations between assigned trajectory and incident HFpEF and HFrEF. Methods We used a Bayesian nonparametric trajectory approach to jointly model longitudinal measures of LV ejection fraction (LVEF) and E/A ratio among 747 participants in two longitudinal cohort studies (the Atherosclerosis Risk in Communities [ARIC] study and Jackson Heart Study [JHS]) who underwent ≥2 echocardiograms in 2000-04 (mean age 65±5 years), 2011-2013 (age 75±5 years), and 2018-19 (age 81±5 years). Using the trajectories inferred from this modeling, we applied Bayes theorem to assign 4,419 ARIC-only participants to their most likely trajectory using single time-point measures of LVEF and E/A ratio acquired in 2011-2013 (age 75±5 years). These assignments were treated as a categorical variable in subsequent analysis. In ARIC-only participants, we used Cox proportional hazards models adjusted for age, sex, obesity, hypertension, diabetes, chronic kidney disease, and coronary heart disease to assess the relationship between assigned trajectory and incident HF. Results In the discovery set (n=747), we identified six trajectories of cardiac function based jointly on longitudinal changes in LVEF and E/A ratio (Figure 1). The pink (prevalence 50%) and light green (17%) trajectories showed increasing LVEF and decreasing E/A ratio with increasing age, while the red trajectory (22%) demonstrated a lack of increase in LVEF. The dark green trajectory (4%) demonstrated progressive LVEF decline, the orange trajectory (2%) steep LVEF decline and rise in E/A ratio, and the blue trajectory (4%) rising E/A ratio despite increasing in LVEF. Analysis of the ARIC-only participants (n=4,419) showed that trajectory membership was differentially associated with risk of incident HFpEF and HFrEF (Figure 2). Compared to the pink trajectory, membership in the orange trajectory was associated with incident HFpEF and HFrEF, the red and the dark green trajectory associated with HFrEF alone, and the blue trajectory with HFpEF alone. Conclusion(s) These results demonstrate the feasibility of identifying trajectories of cardiac function with multidimensional longitudinal data and may have implications for HF risk prediction.

A generalizable electrocardiogram-based artificial intelligence model for 10-year heart failure risk prediction

BackgroundHeart failure (HF) is a progressive condition with high global incidence. HF has two main subtypes: HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF). There is an inherent need for simple yet effective ECG-based artificial intelligence (AI; ECG-AI) models which can predict HF risk early to allow for risk modification. ObjectiveThe main objectives were to validate HF risk prediction models using Multi-Ethnic Study of Atherosclerosis (MESA) data and assess performance of on HFpEF and HFrEF classification. MethodsECG-AI model was developed using raw 12-lead ECGs within a convolutional neural network (CNN). The clinical models from ARIC (ARIC-HF) and Framingham Heart Study (FHS-HF) used nine and eight variables, respectively. ECG-AI plus the clinical data within a Cox Proportional Hazard model (ECG-AI-Cox) and, for ECG-based comparison, a new Light Gradient Boosting Machine model using 288 ECG Characteristics (ECG-Chars) were developed. All the models were validated on MESA. The performances of these models were evaluated using the area under the receiver operating characteristic curve (ROC AUC) and compared using De-Long Test. ResultsECG-AI, ECG-Chars, and ECG-AI-Cox resulted in validation AUCs of 0.77, 0.73, and 0.84, respectively. ARIC-HF and FHS-HF yielded AUCs=0.76 and 0.74, respectively and CPH resulted in AUC=0.78. ECG-AI-Cox outperformed all other models. ECG-AI-Cox provided an AUC of 0.85 for HFrEF and 0.83 for HFpEF. ConclusionECG-AI utilizing ECGs provides better validated predictions when compared to HF risk calculators and the ECG feature model and also works well with HFpEF and HFrEF classification.