The MUSIC Risk score: a simple method for predicting mortality in ambulatory patients with chronic heart failure

Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations

Sudden death after myocardial infarction (MI) is associated with electrophysiological heterogeneities and ionic current remodelling. Low ejection fraction (EF) is used in risk stratification, but its mechanistic links with pro-arrhythmic heterogeneities are unknown. We aim to provide mechanistic explanations of clinical phenotypes in acute and chronic MI, from ionic current remodelling to ECG and EF, using human electromechanical modelling and simulation to augment experimental and clinical investigations. A human ventricular electromechanical modelling and simulation framework is constructed and validated with rich experimental and clinical datasets, incorporating varying degrees of ionic current remodelling as reported in literature. In acute MI, T-wave inversion and Brugada phenocopy were explained by conduction abnormality and local action potential prolongation in the border zone. In chronic MI, upright tall T-waves highlight large repolarisation dispersion between the border and remote zones, which promoted ectopic propagation at fast pacing. Post-MI EF at resting heart rate was not sensitive to the extent of repolarisation heterogeneity and the risk of repolarisation abnormalities at fast pacing. T-wave and QT abnormalities are better indicators of repolarisation heterogeneities than EF in post-MI.

Vectorcardiography Predicts Heart Failure in Patients Following ST Elevation Myocardial Infarction

BACKGROUND

Modeling outcomes, such as onset of heart failure (HF) or mortality, in patients following ST elevation myocardial infarction (STEMI) is challenging but clinically very useful. The acute insult following a myocardial infarction and chronic degeneration seen in HF involve a similar process where a loss of cardiomyocytes and abnormal remodeling lead to pump failure. This process may alter the strength and direction of the heart's net depolarization signal. We hypothesize that changes over time in unique parameters extracted using vectorcardiography (VCG) have the potential to predict outcomes in patients post-STEMI and could eventually be used as a noninvasive and cost-effective surveillance tool for characterizing the severity and progression of HF to guide evidence-based therapies.

METHODS

We identified 162 patients discharged from Michigan Medicine between 2016 and 2021 with a diagnosis of acute STEMI. For each patient, a single 12-lead ECG > 1 week pre-STEMI and > 1 week post-STEMI were collected. A set of unique VCG parameters were derived by analyzing features of the QRS complex. We used LASSO regression analysis incorporating clinical variables and VCG parameters to create a predictive model for HF, mortality, or the composite at 90, 180, and 365 days post-STEMI.

RESULTS

The VCG model is most predictive for HF onset at 90 days with a robust AUC. Variables from the HF model mitigating or driving risk, at a p < 0.05, were primarily parameters that assess the area swept by the depolarization vector including the 3D integral and convex hull in select spatial octants and quadrants.

Lower diastolic tension may be indicative of higher proarrhythmic propensity in failing human cardiomyocytes

Chronic heart failure is one of the most common reasons for hospitalization. Current risk stratification is based on ejection fraction, whereas many arrhythmic events occur in patients with relatively preserved ejection fraction. We aim to investigate the mechanistic link between proarrhythmic abnormalities, reduced contractility and diastolic dysfunction in heart failure, using electromechanical modelling and simulations of human failing cardiomyocytes. We constructed, calibrated and validated populations of human electromechanical models of failing cardiomyocytes, that were able to reproduce the prolonged action potential, reduced contractility and diastolic dysfunction as observed in human data, as well as increased propensity to proarrhythmic incidents such as early afterdepolarization and beat-to-beat alternans. Our simulation data reveal that proarrhythmic incidents tend to occur in failing myocytes with lower diastolic tension, rather than with lower contractility, due to the relative preserved SERCA and sodium calcium exchanger current. These results support the inclusion of end-diastolic volume to be potentially beneficial in the risk stratifications of heart failure patients.

Prognostic impact of abnormal sodium burden in heart failure patients with preserved ejection fraction

BACKGROUND

Sodium abnormality is common in patients with heart failure (HF) and is associated with adverse clinical outcomes. The aim of this study is to determine the impact of abnormal sodium burden on long-term mortality and hospitalization in HF with preserved ejection fraction (HFpEF).

METHODS

We analysed participants from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial with available baseline and follow-up data (n = 1717). Abnormal sodium burden was defined as the proportion of days with abnormal sodium plasma levels (either <135 mmol/L or > 145 mmol/L). To determine the independent prognostic impact of abnormal sodium burden on the long-term clinical adverse outcomes (The primary outcome was any cause death, the secondary outcomes include cardiovascular disease death, HF hospitalization, any cause hospitalization and the primary endpoint of the original study), a multivariable Cox proportional hazard model and time-updated Cox regression model were performed.

RESULTS

Abnormal sodium burden occurred in 717 patients (41.76%). A high abnormal sodium burden was associated with 1.47 (95% CI, 1.15-1.89) higher risk with any cause mortality, 1.51 (95% CI, 1.08-2.09) higher risk with CVD death and 1.31 (95% CI, 1.02-1.69) higher risk with HF hospitalization when compared with no burden group. When sodium level changes over time were accounted for in time-updated models, abnormal sodium level was still associated with poor clinical outcomes. Diuretic and spironolactone usage did not show a statistical interaction effect on the prognostic significance.

CONCLUSIONS

In HFpEF patients, abnormal sodium burden was an independent predictor long-term any-cause mortality and HF hospitalization.

Association between hyponatremia and adverse clinical outcomes of heart failure: current evidence based on a systematic review and meta-analysis

Background

Heart failure (HF) is a global health challenge. The perturbations in fluid and electrolyte equilibrium, particularly the compromised sodium balance associated with HF lead to high mortality rates. Hence, elucidating the correlation between serum sodium levels and the prognosis of HF is of paramount importance. This study aimed to conduct a comprehensive meta-analysis to thoroughly investigate the interplay between hyponatremia and the prognostic outlook of individuals with HF.

Methods

A comprehensive search of bibliographic databases including PubMed, Embase, and the Cochrane Central Register of Controlled Trials was conducted to identify relevant observational studies examining the association between hyponatremia and prognosis of HF. Data extraction, synthesis, and assessment of risk of bias were conducted. Meta-analytic methods, sensitivity analyses, and heterogeneity test were employed as appropriate to synthesize the data.

Results

A total of 43,316 patients with HF were included spanning 25 selected studies. The pooled data revealed a notable association between hyponatremia and elevated risks across short and long-term mortality of HF. Specifically, hyponatremia was found to significantly increase the likelihood of all-cause mortality (Hazard ratio [HR] = 1.94, 95% confidence interval [CI]: 1.78-2.12); 1-year mortality (HR = 1.67, 95%CI: 1.46-1.90); 30-day mortality (HR = 2.03, 95%CI: 1.73-2.25); cardiac mortality (HR = 2.11, 95%CI: 1.81-2.46); and in-hospital mortality (HR = 1.64, 95%CI: 1.15-2.34).

Conclusion

Our meta-analysis emphasizes the significant impact of hyponatremia on mortality in the HF patient population, highlighting the critical importance of maintaining stable serum sodium levels in HF management.

Admission hyponatraemia as heart failure events predictor in patients with acute heart failure

AIMS

Heart failure remained consistent as one of the biggest cardiovascular problems in Indonesia. Hyponatraemia is a common electrolyte disorder among patients presented with heart failure; however, the prognostic value for worsening heart failure has not been well defined.

METHODS AND RESULTS

We studied 134 patients admitted with acute heart failure and investigated the relationship between admission serum sodium and the composite clinical outcomes of all-cause mortality and hospitalization ambispectively with a follow-up duration of 6 months. We also try to look for low sodium-level impacts in several conditions. Among 134 patients, 84 patients presented with low sodium during admission, defined as a serum sodium level of <135 mEq/L, and it was associated with higher composite clinical outcome risk [odds ratio (OR), 5.9; 95% confidence interval (CI), 2.8-12.0; P < 0.001]. Moreover, hyponatraemia impacts on composite endpoints were driven by both parameters; it was independently associated with mortality (OR, 3.1; 95% CI, 1.4-6.8; P = 0.003) and rehospitalization (OR, 5.3; 95% CI, 2.4-11.7; P < 0.001). This result remained consistent in most subgroups.

CONCLUSIONS

On-admission hyponatraemia is a predictor for 6 month mortality and rehospitalization. Further work is needed to determine if correction of hyponatraemia translates into clinical benefit.

Impact of dapagliflozin on cardiac remodelling in patients with chronic heart failure: The DAPA-MODA study

AIMS

Dapagliflozin improves the prognosis of patients with heart failure (HF), regardless of left ventricular ejection fraction (LVEF). However, its effect on cardiac remodelling parameters, specifically left atrial (LA) remodelling, is not well established.

METHODS AND RESULTS

The DAPA-MODA trial (NCT04707352) is a multicentre, single-arm, open-label, prospective and interventional study that aimed to evaluate the effect of dapagliflozin on cardiac remodelling parameters over 6 months. Patients with stable chronic HF receiving optimized guideline-directed therapy, except for any sodium-glucose cotransporter 2 inhibitor, were included. Echocardiography was performed at baseline, 30 and 180 days, and analysed by a central core-lab in a blinded manner to both patient and time. The primary endpoint was the change in maximal LA volume index (LAVI). A total of 162 patients (64.2% men, 70.5 ± 10.6 years, 52% LVEF >40%) were included in the study. At baseline, LA dilatation was observed (LAVI 48.1 ± 22.6 ml/m2 ) and LA parameters were similar between LVEF-based phenotypes (≤40% vs. >40%). LAVI showed a significant reduction at 180 days (-6.6% [95% confidence interval -11.1, -1.8], p = 0.008), primarily due to a decrease in reservoir volume (-13.8% [95% confidence interval -22.5, -4], p = 0.007). Left ventricular geometry improved with significant reductions in left ventricular mass index (-13.9% [95% confidence interval -18.7, -8.7], p < 0.001), end-diastolic volume (-8.0% [95% confidence interval -11.6, -4.2], p < 0.001) and end-systolic volume (-11.9% [95% confidence interval -16.7, -6.8], p < 0.001) at 180 days. N-terminal pro-B-type natriuretic peptide (NT-proBNP) showed a significant reduction at 180 days (-18.2% [95% confidence interval -27.1, -8.2], p < 0.001), without changes in filling Doppler measures.

CONCLUSION

Dapagliflozin administration in stable out-setting patients with chronic HF and optimized therapy results in global reverse remodelling of cardiac structure, including reductions in LA volumes and improvement in left ventricular geometry and NT-proBNP concentrations.

Utilizing shared frailty with the Cox proportional hazards regression: Post discharge survival analysis of CHF patients

Understanding patients' survival probability as well as the factors affecting it constitute a significant concern for researchers and practitioners, in particular for patients with severe chronic illnesses such as congestive heart failure (CHF). CHF is a clinical syndrome characterized by comorbidities and adverse medical events. Risk stratification to identify patients most likely to die shortly after hospital discharge can improve the quality of care by better allocating organizational resources and personalized interventions. Probability assessment improves clinical decision-making, contributes to personalized care, and saves costs. Although one of the most informative indices is the time to an adverse event for each patient, commonly analyzed using survival analysis methods, these are often challenging to implement due to the complexity of the medical data. Numerous studies have used the Cox proportional hazards (PH) regression method to generate the survival distribution pattern and factors affecting survival. This model, although advantageous for survival analysis, assumes the homogeneity of the hazard ratio across patients and independence of the observations in terms of survival time. These assumptions are often violated in real-world data, especially when the dataset is composed of readmission data for chronically ill patients, since these recurring observations are inherently dependent. This study ran the Cox PH regression on a feature set selected by machine learning algorithms from a rich hospital dataset. The event modeled here was patient mortality within 90 days post-hospital discharge. The sample was composed of medical records of patients hospitalized in the Israeli Sheba Medical Center more than once, with CHF as the primary diagnosis. We modeled the survival of CHF patients using the Cox PH regression with and without the shared frailty correction that addresses the shortcomings of the Cox Model. The results of the two models of the Cox PH regression - with and without the shared frailty correction were compared. The results demonstrate that the shared frailty correction, which was statistically significant in our analysis, improved the performance of the basic Cox PH model. While this is the main contribution, we also show that this model outperforms two commonly used measures (ADHERE and EFFECT) for predicting early mortality of CHF patients. Thus, the results illustrate how applying advanced analytics can outperform traditional methods. An additional contribution is the feature set selected using machine-learning methods that is different from those used in the extant literature.

Association between triglyceride glucose index and worsening heart failure in significant secondary mitral regurgitation following percutaneous coronary intervention

BACKGROUND

The triglyceride glucose (TyG) index is an alternative to insulin resistance (IR) as an early indicator of worsening heart failure (HF). Patients with secondary mitral regurgitation (sMR) often experience progressive deterioration of cardiac function. This study aimed to investigate the relationship between the TyG index and worsening of HF in significant sMR (grade  ≥ 2) following percutaneous coronary intervention (PCI).

METHODS

This study enrolled participants with significant sMR following PCI from a multicenter cohort study. The patients were divided into the following 3 groups according to tertiles of TyG index: T1, TyG ≤ 8.51; T2, TyG > 8.51 to ≤ 8.98; and T3, TyG > 8.98. The main clinical outcome was worsening HF including unplanned rehospitalization or unscheduled physician office/emergency department visit due to HF and unplanned mitral valve surgery.

RESULTS

A total of 922 patients (mean ± SD age, 64.1 ± 11.0 years; 79.6% male) were enrolled. The incidence of worsening HF was 15.5% in T1, 15.7% in T2, and 26.4% in T3. In the multivariable model, the highest TyG tertile (T3 group) was more strongly correlated with worsening HF than the lowest tertile (T1 group) after adjusting for confounders (adjusted hazard ratio, 2.44; 95% confidence interval, 1.59-3.72; P < 0.001). The addition of TyG to risk factors such as N-terminal pro brain natriuretic peptide and clinical models improved the predictive ability of TyG for worsening HF.

CONCLUSIONS

Elevated preprocedural TyG index is a significant and independent risk factor for worsening HF in sMR following PCI that can be used for risk stratification.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

Introduction

Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.

Materials and methods

Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.

Results

The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.

Conclusion

This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

Assessment of clinical and hemodynamic congestion as predictors of mortality in elderly outpatients with heart failure

INTRODUCTION

This work aims to evaluate whether a clinical examination and measurement of N-terminal pro-brain natriuretic peptide can predict poor prognosis in outpatients with heart failure.

PATIENTS AND METHODS

We carried out a retrospective study from 2010 to 2018 in 238 patients diagnosed with heart failure. At baseline, we evaluated the presence of pulmonary rales and bilateral leg edema (clinical congestion) together with N-terminal pro-brain natriuretic peptide ≥ 1500 pg/mL (hemodynamic congestion). Patients were classified into 4 groups depending on their congestion pattern: no congestion (G1) (n = 50); clinical congestion (G2) (n = 43); hemodynamic congestion (G3) (n = 73); and clinical and hemodynamic congestion (G4) (n = 72). The primary outcome was all-cause mortality at one year of follow-up.

RESULTS

A total of 238 patients were included. The mean age was 82 years, 61.8% were women, and 20.7% had reduced left ventricular ejection fraction. Thirty patients died in the first year of follow-up (12.6%). After controlling for confounding variables (sex, recent discharge for heart failure, estimated glomerular filtration rate, and left ventricular ejection fraction), the independent risk of death in each group compared to G1 as the reference group was: G2: HR 4.121 (95%CI 1.131-15.019); G3: HR 2.511 (95%CI 1.007-6.263); and G4: HR 7.418 (95%CI 1.630-33.763).

CONCLUSION

Congestion in outpatients with heart failure correlates with prognosis. Patients with both clinical and hemodynamic congestion had the highest risk of all-cause death at one year.

Burden of heart failure in primary healthcare

Objectives

To determine the epidemiology of heart failure registered in primary healthcare clinical records in Catalunya, Spain, between 2010 and 2014, focusing on incidence, mortality, and resource utilization.

Design

Retrospective observational cohort study.

Setting

Study was carried out in primary care setting.

Participants and interventions

Patients registered as presenting a new heart failure diagnosis. The inclusion period ran from 1st January 2010 to 31st December 2013, but patients were followed until 31st December 2013 in order to analyze mortality.

Main measures

Information came from electronic medical records.

Results

A total of 64 441 patients were registered with a new diagnosis of heart failure (2.76 new cases per 1000 persons-year). Among them, 85.8% were ≥65 years. The number of cases/1000 persons-year was higher in men in all age groups. Incidence ranged from 0.04 in women <45 years to 27.61 in the oldest group, and from 0.08 in men <45 years to 28.52 in the oldest group. Mortality occurred in 16 305 (25.3%) patients. Primary healthcare resource utilization increased after the occurrence of heart failure, especially the number of visits made by nurses to the patients’ homes.

Conclusion

Heart failure incidence increases with age, is greater in men, and remains stable. Mortality continues to be high in newly diagnosed patients in spite of the current improvements in treatment. Home visits represent the greatest cost for the management of this disease in primary care setting.

QT interval Adaptation to Heart Rate Changes in Atrial Fibrillation as a Predictor of Sudden Cardiac Death

OBJECTIVE

The clinical significance of QT interval adaptation to heart rate changes has been poorly investigated in atrial fibrillation (AF), since QT delineation in the presence of f-waves is challenging. Therefore, the objective of the present study is to investigate new techniques for QT adaptation estimation in permanent AF.

METHODS

A multilead strategy based on generalized periodic component analysis is proposed for QT delineation, involving a spatial, linear transformation which emphasizes Twave periodicity and attenuates f-waves. QT adaptation is modeled by a linear, time-invariant filter, whose impulse response describes the dependence between the current QT interval and the preceding RR intervals, followed by a memoryless, possibly nonlinear, function. The QT adaptation time lag is determined from the estimated impulse response.

RESULTS

Using simulated ECGs in permanent AF, the transformed lead was found to offer more accurate QT delineation and time lag estimation than did the original ECG leads for a wide range of f-wave amplitudes (the time lag estimation error was found to be -0.2+/-0.6 s for SNR = 12 dB). In a population with chronic heart failure and permanent AF, the time lag estimated from the transformed lead was found to have the strongest, statistically significant association with sudden cardiac death (SCD) (hazard ratio = 3.49), whereas none of the original, orthogonal leads had any such association.

CONCLUSIONS

Periodic component analysis provides more accurate QT delineation and improves time lag estimation in AF. A prolonged adaptation time of the QT interval to heart rate changes is associated with a high risk for SCD.

SIGNIFICANCE

This study demonstrates that SCD risk markers, originally developed for sinus rhythm, can also be used in AF, provided that Twave periodicity is emphasized. The time lag is a potentially useful marker for identifying patients at high risk for SCD, guiding clinicians in adopting effective therapeutic decisions.

Weighted Time Warping Improves T-wave Morphology Markers Clinical Significance

Background: T-wave (TW) morphology indices based on time-warping (d_w) have shown significant cardiovascular risk stratification value. However, errors in the location of TW boundaries may impact their prognostic power. Our aim was to test the hypothesis that a weighted time-warping function (WF) would reduce the sensitivity of d_w to these errors and improve their clinical significance. Methods: The WFs were proportional to (i) the reference TW (T), and (ii) the absolute value of its derivative (D). The index d_w was recalculated using these WFs, and its performance was compared to the unweighted control case (C) in four different scenarios: 1) robustness against simulated TW boundaries location errors; 2) ability to retain physiological information in an electrophysiological cardiac model; 3) ability to monitor blood potassium concentration changes ([K⁺]) in 29 hemodialysis (HD) patients; 4) and the sudden cardiac death (SCD) risk stratification value of the TW morphology restitution (TMR) index, derived from d_w, in 651 chronic heart failure (CHF) patients. Results and Discussion: The WFs led to a reduced sensitivity (R) of d_w to TW boundary location errors as compared to C (median R=0.19 and 0.22 and 0.35 for T, D and C, respectively). They also preserved the physiological relationship between d_w and repolarization dispersion changes at ventricular level. No improvements in [K⁺] tracking were observed for the HD patients (Pearsons median correlation [r] between [K⁺] and d_w was 0.86r0.90 for T, D and C). In CHF patients, the SCD risk stratification value of TMR was improved by applying T (hazard ratio, HAR, of 2.80), followed by D (HAR=2.32) and C (HAR=2.23). Conclusions and Significance: The proposed WFs, with T showing the best performance, increased the robustness of time-warping based markers against TW location errors preserving their physiological information content and boosting their SCD risk stratification value. Results from this work support the use of T when deriving d_w for future clinical applications.

Clinical Utility and Validation of the Krakow DCM Risk Score—A Prognostic Model Dedicated to Dilated Cardiomyopathy

Background: One of the most common causes of heart failure is dilated cardiomyopathy (DCM). In DCM, the mortality risk is high and reaches approximately 20% in 5 years. A patient’s prognosis should be established for appropriate HF management. However, so far, no validated tools have been available for the DCM population. Methods: The study population consisted of 735 DCM patients: 406 from the derivation cohort (previously described) and 329 from the validation cohort (from 2009 to 2020, with outcome data after a mean of 42 months). For each DCM patient, the individual mortality risk was calculated based on the Krakow DCM Risk Score. Results: During follow-up, 49 (15%) patients of the validation cohort died. They had shown significantly higher calculated 1-to-5-year mortality risks. The Krakow DCM Risk Score yielded good discrimination in terms of overall mortality risk, with an AUC of 0.704–0.765. Based on a 2-year mortality risk, patients were divided into non-high (≤6%) and high (>6%) mortality risk groups. The observed mortality rates were 8.3% (n = 44) vs. 42.6% (n = 75), respectively (HR 3.37; 95%CI 1.88–6.05; p < 0.0001). Conclusions: The Krakow DCM Risk Score was found to have good predictive accuracy. The 2-year mortality risk > 6% has good discrimination for the identification of high-risk patients and can be applied in everyday practice.

Periodic repolarization dynamics as predictor of risk for sudden cardiac death in chronic heart failure patients

The two most common modes of death among chronic heart failure (CHF) patients are sudden cardiac death (SCD) and pump failure death (PFD). Periodic repolarization dynamics (PRD) quantifies low-frequency oscillations in the T wave vector of the electrocardiogram (ECG) and has been postulated to reflect sympathetic modulation of ventricular repolarization. This study aims to evaluate the prognostic value of PRD to predict SCD and PFD in a population of CHF patients. 20-min high-resolution (1000 Hz) ECG recordings from 569 CHF patients were analyzed. Patients were divided into two groups, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PRD}^+$$\end{document}PRD+ and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PRD}^-$$\end{document}PRD-, corresponding to PRD values above and below the optimum cutoff point of PRD in the study population. Univariate Cox regression analysis showed that SCD risk in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PRD}^+$$\end{document}PRD+ group was double the risk in the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {PRD}^-$$\end{document}PRD- group [hazard ratio (95% CI) 2.001 (1.127–3.554), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {p}<0.05$$\end{document}p<0.05]. The combination of PRD with other Holter-based ECG indices, such as turbulence slope (TS) and index of average alternans (IAA), improved SCD prediction by identifying groups of patients at high SCD risk. PFD could be predicted by PRD only when combined with TS [hazard ratio 2.758 (1.572–4.838), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {p}<0.001$$\end{document}p<0.001]. In conclusion, the combination of PRD with IAA and TS can be used to stratify the risk for SCD and PFD, respectively, in CHF patients.

A review of machine learning for cardiology

This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

Sudden cardiac death after acute decompensation in heart failure patients: implications of discharge haemoglobin levels

Aims

Heart failure (HF) patients have a high risk of mortality due to sudden cardiac death (SCD) and non‐SCD, including pump failure death (PFD). Anaemia predicts more severe symptomatic burden and higher morbidity, as noted by markedly increased hospitalizations and readmission rates, and mortality, underscoring its importance in HF management. Herein, we aimed to determine whether haemoglobin (Hb) level at discharge affects the mode of death and influences SCD risk prediction.

Methods

We evaluated the data of 3020 consecutive acute HF patients from a Japanese prospective multicentre registry. Patients were divided into four groups based on discharge Hb levels. SCD was defined as an unexpected and otherwise unexplained death in a previously stable patient or death due to documented or presumed cardiac arrhythmia without a clear non‐cardiovascular cause. The mode of death (SCD, PFD or other cause) was adjudicated by a central committee. Finally, we investigated whether adding Hb level to the Seattle Proportional Risk Model (SPRM; established risk score utilized to estimate ‘proportion’ of SCD among death events) would affect its performance.

Results

The mean age of studied patients was 74.3 ± 12.9 years, and 59.8% were male. The mean Hb level was 12.0 ± 2.1 g/dL (61.3% of patients had anaemia defined by World Health Organization criteria). During the 2‐year follow‐up, 474 deaths (15.7%) occurred, including 93 SCDs (3.1%), 171 PFDs (5.7%) and 210 other deaths (7.0%; predominantly non‐cardiac death). Absolute risk of PFD (P < 0.001) or other death (P < 0.001) increased along with the severity of anaemia, whereas the incidence of SCD was low but remained consistent across all four groups (P = 0.440). As a proportion of total deaths in each Hb level group, the contributions from non‐SCD increased and from SCD decreased along with anaemia severity (P = 0.007). Adding Hb level to the SPRM improved the overall discrimination (c‐index: 0.62 [95% confidence interval (CI) 0.56–0.69] to 0.65 [95% CI 0.59–0.71]), regardless of the baseline ejection fraction (EF) (c‐index: 0.64 [95% CI 0.55–0.73] to 0.67 [95% CI 0.58–0.75] for reduced EF and 0.55 [95% CI 0.45–0.66] to 0.61 [95% CI 0.52–0.70] for preserved EF).

Conclusions

The discharge Hb level provides information about both absolute and proportional risks for each mode of death in acute HF patients, and the addition of Hb level improves the performance of SPRM by identifying more non‐SCD cases. Future ‘proportional’ SCD risk models should incorporate Hb level as a covariate to meet this high performance.

Risk Scores and Prediction Models in Chronic Heart Failure: A Comprehensive Review

BACKGROUND

Heart failure affects a substantial proportion of the adult population, with an estimated prevalence of 1-2% in developed countries. Over the previous decades, many prediction models have been introduced for this specific population in an attempt to better stratify and manage heart failure patients.

OBJECTIVE

The aim of this study is the systematic review of recent, relevant literature regarding risk scores or prediction models in ambulatory patients with an established diagnosis of chronic heart failure.

METHODS

We conducted a systematic search of the literature in PubMed and CENTRAL from their inception up till December 2019 for studies assessing the performance of risk scores and prediction models and original research studies. Grey literature was searched as well. This review is reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement.

RESULTS

We included 16 eligible studies in this systematic review. Major heart failure risk scores derived from large heart failure populations were among the included studies. Due to significant heterogeneity regarding the main endpoints, a direct comparison of the included prediction scores was inevitable. The majority referred to patients with heart failure with reduced ejection fraction, while only two out of 16 prediction scores have been developed exclusively for heart failure patients with preserved ejection fraction. Ischemic heart disease was the most common aetiology of heart failure in the included studies. Finally, more than half of the prediction scores have not been externally validated.

CONCLUSION

Prediction models aiming at heart failure patients with a preserved or mid-range ejection fraction are lacking. Prediction scores incorporating recent advances in pharmacotherapy should be developed in the future.

Hyponatremia in heart failure: not just 135 to 145

One of the most frequent in-hospital electrolyte disturbances is hyponatremia. Hyponatremia in heart failure (HF) is mainly associated with hypervolemia resulting from activation of baroreceptor-mediated hormones, such as arginine vasopressin (AVP), renin-angiotensin-aldosterone system, and catecholamines. Various electrolyte imbalance can occur as heart failure progress. The goal of this review was to outline the current literature on hyponatremia in HF patients.

Development and external validation of prognostic models to predict sudden and pump-failure death in patients with HFrEF from PARADIGM-HF and ATMOSPHERE

Background

Sudden death (SD) and pump failure death (PFD) are the two leading causes of death in patients with heart failure and reduced ejection fraction (HFrEF).

Objective

Identifying patients at higher risk for mode-specific death would allow better targeting of individual patients for relevant device and other therapies.

Methods

We developed models in 7156 patients with HFrEF from the Prospective comparison of ARNI with ACEI to Determine Impact on Global Mortality and morbidity in Heart Failure (PARADIGM-HF) trial, using Fine-Gray regressions counting other deaths as competing risks. The derived models were externally validated in the Aliskiren Trial to Minimize Outcomes in Patients with Heart Failure (ATMOSPHERE) trial.

Results

NYHA class and NT-proBNP were independent predictors for both modes of death. The SD model additionally included male sex, Asian or Black race, prior CABG or PCI, cancer history, MI history, treatment with LCZ696 vs. enalapril, QRS duration and ECG left ventricular hypertrophy. While LVEF, ischemic etiology, systolic blood pressure, HF duration, ECG bundle branch block, and serum albumin, chloride and creatinine were included in the PFD model. Model discrimination was good for SD and excellent for PFD with Harrell’s C of 0.67 and 0.78 after correction for optimism, respectively. The observed and predicted incidences were similar in each quartile of risk scores at 3 years in each model. The performance of both models remained robust in ATMOSPHERE.

Conclusion

We developed and validated models which separately predict SD and PFD in patients with HFrEF. These models may help clinicians and patients consider therapies targeted at these modes of death.

Trial registration number

PARADIGM-HF: ClinicalTrials.gov NCT01035255, ATMOSPHERE: ClinicalTrials.gov NCT00853658.

Graphics abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-021-01888-x.

MMMELD-XI Score Is Associated With Short-Term Adverse Events in Patients With Heart Failure With Preserved Ejection Fraction

Aim: Accumulating evidence suggests that MELD-XI score holds the ability to predict the prognosis of congestive heart failure. However, most of the evidence is based on the end-stage heart failure population; thus, we aim to explore the association between the MELD-XI score and the prognosis in heart failure with preserved ejection fraction (HFpEF).

Methods: A total of 30,096 patients hospitalized for HFpEF in Fujian Provincial Hospital between January 1, 2014 and July 17, 2020 with available measures of creatinine and liver function were enrolled. The primary endpoint was 60-day in-hospital all-cause mortality. Secondary endpoints were 60-day in-hospital cardiovascular mortality and 30-day rehospitalization for heart failure.

Results: A total of 222 patients died within 60 days after admission, among which 75 deaths were considered cardiogenic. And 73 patients were readmitted for heart failure within 30 days after discharge. Generally, patients with an elevated MELD-XI score tended to have more comorbidities, higher NYHA class, and higher inflammatory biomarkers levels. Meanwhile, the MELD-XI score was positively correlated with NT-pro BNP, left atrial diameter, E/e' and negatively correlated with LVEF. After adjusting for conventional risk factors, the MELD-XI score was independently associated with 60-day in-hospital all-cause mortality [hazard ratio(HR) = 1.052, 95% confidential interval (CI) 1.022–1.083, P = 0.001], 60-day in-hospital cardiovascular mortality (HR = 1.064, 95% CI 1.013–1.118, P = 0.014), and 30-day readmission for heart failure (HR = 1.061, 95% CI 1.015–1.108, P = 0.009). Furthermore, the MELD-XI score added an incremental discriminatory capacity to risk stratification models developed based on this cohort.

Conclusion: The MELD-XI score was associated with short-term adverse events and provided additional discriminatory capacity to risk stratification models in patients hospitalized for HFpEF.

Prediction of HF-Related Mortality Risk Using Genetic Risk Score Alone and in Combination With Traditional Risk Factors

Background: Common variants may contribute to the variation of prognosis of heart failure (HF) among individual patients, but no systematical analysis was conducted using transcriptomic and whole exome sequencing (WES) data. We aimed to construct a genetic risk score (GRS) and estimate its potential as a predictive tool for HF-related mortality risk alone and in combination with traditional risk factors (TRFs). Methods and Results: We reanalyzed the transcriptomic data of 177 failing hearts and 136 healthy donors. Differentially expressed genes (fold change >1.5 or <0.68 and adjusted P < 0.05) were selected for prognosis analysis using our whole exome sequencing and follow-up data with 998 HF patients. Statistically significant variants in these genes were prepared for GRS construction. Traditional risk variables were in combination with GRS for the construct of the composite risk score. Kaplan-Meier curves and receiver operating characteristic (ROC) analysis were used to assess the effect of GRS and the composite risk score on the prognosis of HF and discriminant power, respectively. We found 157 upregulated and 173 downregulated genes. In these genes, 31 variants that were associated with the prognosis of HF were finally identified to develop GRS. Compared with individuals with low risk score, patients with medium- and high-risk score showed 2.78 (95%CI = 1.82-4.24, P = 2 × 10^-6) and 6.54 (95%CI = 4.42-9.71, P = 6 × 10^-21) -fold mortality risk, respectively. The composite risk score combining GRS and TRF predicted mortality risk with an HR = 5.41 (95% CI = 2.72-10.64, P = 1 × 10^-6) for medium vs. low risk and HR = 22.72 (95% CI = 11.9-43.48, P = 5 × 10^-21) for high vs. low risk. The discriminant power of GRS is excellent with a C statistic of 0.739, which is comparable to that of TRF (C statistic = 0.791). The combination of GRS and TRF could significantly increase the predictive ability (C statistic = 0.853). Conclusions: The 31-SNP GRS could well distinguish those HF patients with poor prognosis from those with better prognosis and provide clinician with reference for the intensive therapy, especially when combined with TRF. Clinical Trial Registration: https://www.clinicaltrials.gov/, identifier: NCT03461107.

Machine Learning-Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study

Background

With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand.

Objective

Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate.

Methods

For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions.

Results

Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×10⁹/L).

Conclusions

ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making.

Conception and bicentric validation of the proSCANNED score, a simplified bedside prognostic score for Heart Failure patients

A simple and accurate prognostic tool for Heart Failure (HF) patients is critical to improve follow-up. Different risk scores are accurate but with limited clinical applicability. The current study aims to derive and validate a simple predictive tool for HF prognosis. French outpatients with stable HF of two university hospitals were included in the derivation (N = 134) or in the validation (N = 274) sample and followed up for a median of 23 months. Potential predictors were variables with known association with mortality and easily available. The proSCANNED risk score was derived using a parametric survival model on complete case data; it includes 8 binary variables and its values are 0–8. In the validation sample, the ability of the score to discriminate the 1-year vital status was moderate (AUC = 0.71, IC95% = [0.64–0.71]). However, the stratification of the score in three groups showed a good calibration for patients in the low- and medium-risk risk group. The proSCANNED score is an easy-to-use tool in clinical practice with a good discrimination, stability, and calibration sufficient to improve the medical care of patients. Other follow up studies are necessary to assess score applicability in larger populations, and its impact.

Developing and validating models to predict sudden death and pump failure death in patients with heart failure and preserved ejection fraction

BACKGROUND

Sudden death (SD) and pump failure death (PFD) are leading modes of death in heart failure and preserved ejection fraction (HFpEF). Risk stratification for mode-specific death may aid in patient enrichment for new device trials in HFpEF.

METHODS

Models were derived in 4116 patients in the Irbesartan in Heart Failure with Preserved Ejection Fraction trial (I-Preserve), using competing risks regression analysis. A series of models were built in a stepwise manner, and were validated in the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM)-Preserved and Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trials.

RESULTS

The clinical model for SD included older age, men, lower LVEF, higher heart rate, history of diabetes or myocardial infarction, and HF hospitalization within previous 6 months, all of which were associated with a higher SD risk. The clinical model predicting PFD included older age, men, lower LVEF or diastolic blood pressure, higher heart rate, and history of diabetes or atrial fibrillation, all for a higher PFD risk, and dyslipidaemia for a lower risk of PFD. In each model, the observed and predicted incidences were similar in each risk subgroup, suggesting good calibration. Model discrimination was good for SD and excellent for PFD with Harrell's C of 0.71 (95% CI 0.68-0.75) and 0.78 (95% CI 0.75-0.82), respectively. Both models were robust in external validation. Adding ECG and biochemical parameters, model performance improved little in the derivation cohort but decreased in validation. Including NT-proBNP substantially increased discrimination of the SD model, and simplified the PFD model with marginal increase in discrimination.

CONCLUSIONS

The clinical models can predict risks for SD and PFD separately with good discrimination and calibration in HFpEF and are robust in external validation. Adding NT-proBNP further improved model performance. These models may help to identify high-risk individuals for device intervention in future trials.

CLINICAL TRIAL REGISTRATION

I-Preserve: ClinicalTrials.gov NCT00095238; TOPCAT: ClinicalTrials.gov NCT00094302; CHARM-Preserved: ClinicalTrials.gov NCT00634712.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

Aims

This study aimed to review the performance of machine learning (ML) methods compared with conventional statistical models (CSMs) for predicting readmission and mortality in patients with heart failure (HF) and to present an approach to formally evaluate the quality of studies using ML algorithms for prediction modelling.

Methods and results

Following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines, we performed a systematic literature search using MEDLINE, EPUB, Cochrane CENTRAL, EMBASE, INSPEC, ACM Library, and Web of Science. Eligible studies included primary research articles published between January 2000 and July 2020 comparing ML and CSMs in mortality and readmission prognosis of initially hospitalized HF patients. Data were extracted and analysed by two independent reviewers. A modified CHARMS checklist was developed in consultation with ML and biostatistics experts for quality assessment and was utilized to evaluate studies for risk of bias. Of 4322 articles identified and screened by two independent reviewers, 172 were deemed eligible for a full‐text review. The final set comprised 20 articles and 686 842 patients. ML methods included random forests (n = 11), decision trees (n = 5), regression trees (n = 3), support vector machines (n = 9), neural networks (n = 12), and Bayesian techniques (n = 3). CSMs included logistic regression (n = 16), Cox regression (n = 3), or Poisson regression (n = 3). In 15 studies, readmission was examined at multiple time points ranging from 30 to 180 day readmission, with the majority of studies (n = 12) presenting prediction models for 30 day readmission outcomes. Of a total of 21 time‐point comparisons, ML‐derived c‐indices were higher than CSM‐derived c‐indices in 16 of the 21 comparisons. In seven studies, mortality was examined at 9 time points ranging from in‐hospital mortality to 1 year survival; of these nine, seven reported higher c‐indices using ML. Two of these seven studies reported survival analyses utilizing random survival forests in their ML prediction models. Both reported higher c‐indices when using ML compared with CSMs. A limitation of studies using ML techniques was that the majority were not externally validated, and calibration was rarely assessed. In the only study that was externally validated in a separate dataset, ML was superior to CSMs (c‐indices 0.913 vs. 0.835).

Conclusions

ML algorithms had better discrimination than CSMs in most studies aiming to predict risk of readmission and mortality in HF patients. Based on our review, there is a need for external validation of ML‐based studies of prediction modelling. We suggest that ML‐based studies should also be evaluated using clinical quality standards for prognosis research. Registration: PROSPERO CRD42020134867

The use of the CALL Risk Score for predicting mortality in Brazilian heart failure patients

Aims

This study aimed to develop and validate a simple method for predicting long‐term all‐cause mortality in ambulatory patients with chronic heart failure (CHF) residing in an area where Chagas disease is endemic, which will be important not only for patients living in Latin America but also to those living in developed non‐endemic countries.

Methods and results

A total of 677 patients with a wide spectrum of aetiologies for left ventricular systolic dysfunction and receiving optimized evidence‐based treatment for CHF were prospectively followed for approximately 11 years. We established a risk score using Cox proportional hazard regression models. After multivariable analysis, four variables were independently associated with mortality and included in the CALL Risk Score: Chagas cardiomyopathy aetiology alone [hazard ratio, 3.36; 95% confidence interval (CI), 2.61–4.33; P < 0.001], age ≥60 years (hazard ratio, 1.36; 95% CI, 1.06–1.74; P = 0.016), left anterior fascicular block (hazard ratio, 1.64; 95% CI, 1.27–2.11; P < 0.001), and left ventricular ejection fraction <40% (hazard ratio, 1.73; 95% CI, 1.30–2.28; P < 0.001). The internal validation considered the bootstrapping, a resampling technique recommended for prediction model development. Hence, we established a scoring system attributing weights according to each risk factor: 3 points for Chagas cardiomyopathy alone, 1 point for age ≥60 years, and 2 points each for left anterior fascicular block and left ventricular ejection fraction <40%. Three risk groups were identified: low risk (score ≤2 points), intermediate risk (score of 3 to 5 points), and high risk (score ≥6 points). High‐risk patients had more than two‐fold increase in mortality (26.9 events/100 patient‐years) compared with intermediate‐risk patients (10.1 events/100 patient‐years) and almost seven‐fold increase compared with low‐risk patients (4.3 events/100 patient‐years). The CALL Risk Score data sets from the development and internal validation cohorts both displayed suitable discrimination c‐index of 0.689 (95% CI, 0.688–0.690; P < 0.001) and 0.687 (95% CI, 0.686–0.688; P < 0.001), respectively, and satisfactory calibration [Greenwood–Nam–D'Agostino test (8) = 7.867; P = 0.447] and [Greenwood–Nam–D'Agostino test (8) = 10.08; P = 0.273], respectively.

Conclusions

The CALL Risk Score represents a simple and validated method with a limited number of non‐invasive variables that successfully predicts long‐term all‐cause mortality in a real‐world cohort of patients with CHF. Patients with CHF stratified as high risk according to the CALL Risk Score should be monitored and aggressively managed, including those with CHF secondary to Chagas disease.

Mortality risk in dilated cardiomyopathy: the accuracy of heart failure prognostic models and dilated cardiomyopathy-tailored prognostic model

Aims

The aims of this paper were to investigate the analytical performance of the nine prognostic scales commonly used in heart failure (HF), in patients with dilated cardiomyopathy (DCM), and to develop a unique prognostic model tailored to DCM patients.

Methods and results

The hospital and outpatient records of 406 DCM patients were retrospectively analysed. The information on patient status was gathered after 48.2 ± 32.0 months. Tests were carried out to ascertain the prognostic accuracy in DCM using some of the most frequently applied HF prognostic scales (Barcelona Bio‐Heart Failure, Candesartan in Heart Failure‐Assessment of Reduction in Mortality and Morbidity, Studio della Streptochinasi nell'Infarto Miocardico‐Heart Failure, Eplerenone in Mild Patients Hospitalization and Survival Study in Heart Failure, Meta‐Analysis Global Group in Chronic Heart Failure, MUerte Subita en Insuficiencia Cardiaca, Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure, Seattle Heart Failure Model) and one dedicated to DCM, that of Miura et al. At follow‐up, 70 DCM patients (17.2%) died. Most analysed scores substantially overestimated the mortality risk, especially in survivors. The prognostic accuracy of the scales were suboptimal, varying between 60% and 80%, with the best performance from Barcelona Bio‐Heart Failure and Seattle Heart Failure Model for 1–5 year mortality [areas under the receiver operating curve 0.792–0.890 (95% confidence interval 0.725–0.918) and 0.764–0.808 (95% confidence interval 0.682–0.934), respectively].Based on our accumulated data, a self‐developed DCM prognostic model was constructed. The model consists of age, gender, body mass index, symptoms duration, New York Heart Association class, diabetes mellitus, prior stroke, abnormal liver function, dyslipidaemia, left bundle branch block, left ventricle end‐diastolic diameter, ejection fraction, N terminal pro brain natriuretic peptide, haemoglobin, estimated glomerular filtration rate, and pharmacological and resynchronisation therapy. This newly created prognostic model outperformed the analysed HF scales.

Conclusions

An analysis of various HF prognostic models found them to be suboptimal for DCM patients. A self‐developed DCM prognostic model showed improved performance over the nine other models studied. However, further validation of the prognostic model in different DCM populations is required.

Changes in causes of death and influence of therapeutic improvement over time in patients with heart failure and reduced ejection fraction

INTRODUCTION AND OBJECTIVES

In patients with heart failure and reduced ejection fraction (HFrEF), several therapies have been proven to reduce mortality in clinical trials. However, there are few data on the effect of the use of evidence-based therapies on causes of death in clinical practice.

METHODS

This study included 2351 outpatients with HFrEF (< 40%) from 2 multicenter prospective registries: MUSIC (n=641, period: 2003-2004) and REDINSCOR I (n=1710, period: 2007-2011). Variables were recorded at inclusion and all patients were followed-up for 4 years. Causes of death were validated by an independent committee.

RESULTS

Patients in REDINSCOR I more frequently received beta-blockers (85% vs 71%; P <.001), mineralocorticoid antagonists (64% vs 44%; P <.001), implantable cardioverter-defibrillators (19% vs 2%; P <.001), and resynchronization therapy (7.2% vs 4.8%; P=.04). In these patients, sudden cardiac death was less frequent than in those in MUSIC (6.8% vs 11.4%; P <.001). After propensity score matching, we obtained 2 comparable populations differing only in treatments (575 vs 575 patients). In patients in REDINSCOR I, we found a lower risk of total mortality (HR, 0.70; 95%CI, 0.57-0.87; P=.001) and sudden cardiac death (sHR, 0.46; 95%CI, 0.30-0.70; P <.001), and a trend toward lower mortality due to end-stage HF (sHR, 0.73; 95%CI, 0.53-1.01; P=.059), without differences in other causes of death (sHR, 1.17; 95%CI, 0.78-1.75; P=.445), regardless of functional class.

CONCLUSIONS

In ambulatory patients with HFrEF, implementation of evidence-based therapies was associated with a lower risk of death, mainly due to a significant reduction in sudden cardiac death.

Clinical characteristics and long-term prognosis of ischemic and non-ischemic cardiomyopathy

Objectives

The different etiology of HF has different prognostic risk factors. Prognosis assessment of ICM and NICM has important clinical value. This study is aimed to explore the predicting factors for ICM and NICM.

Methods

1082 HFrEF patients were retrospectively enrolled from Jan. 01, 2016 to Dec. 31, 2017. On Jan. 31, 2019, 873 patients were enrolled for analysis excluding incomplete, unfollowed, and unexplained data. The patients were divided into ischemic and non-ischemic group. The differences in clinical characteristics and long-term prognosis between the two groups were analyzed, and multivariate Cox analysis was used to predict the respective all-cause mortality, SCD and rehospitalization of CHF.

Results

873 patients aged 64(53,73) were divided into two groups: ICM (403, 46.16%) and NICM. At the end, 203 died (111 in ICM, 54.68%), of whom 87 had SCD (53 in ICM, 60.92%) and 269 had rehospitalization for HF(134 in ICM, 49.81%). Independent risk factors affecting all-cause mortality in ICM: DM, previous hospitalization of HF, age, eGFR, LVEF; for SCD: PVB, eGFR, Hb, revascularization; for readmission of HF: low T3 syndrome, PVB, DM, previous hospitalization of HF, eGFR. Otherwise; factors affecting all-cause mortality in NICM: NYHA III-IV, paroxysmal AF/AFL, previous hospitalization of HF, β-blocker; for SCD: low T3 syndrome, PVB, nitrates, sodium, β-blocker; for rehospitalization of HF: paroxysmal AF/AFL, previous admission of HF, LVEF.

Conclusions

Both all-cause mortality and SCD in ICM is higher than that in NICM. Different etiologies of CHF have different risk factors affecting the prognosis.

Short-term mortality in end-stage heart failure patients

Objectives

This study is aimed at analyzing the impact of the main factors contributing to short and long-term mortality in patients at final stages of heart failure (HF).

Setting

Patients attended at any of the 279 primary health care centers belonging to the Institut Català de la Salut, in Catalonia (Spain).

Participants

Patients with Advanced HF.

Design

Multicenter cohort study including 1148 HF patients followed for one-year after reaching New York Heart Association (NYHA) IV.

Main measurements

The primary outcome was all-cause mortality. Multivariate logistic regression models were performed to assess the outcomes at 1, 3, 6, and 12 months.

Results

Mean age of patients was 82 (SD 9) years and women represented 61.7%. A total of 135 (11.8%) and 397 (34.6%) patients died three months and one year after inclusion, respectively. Male gender, age, and decreased body mass index were associated with higher mortality at three, six and twelve months. In addition, low systolic blood pressure levels, severe reduction in glomerular filtration, malignancy, and higher doses of loop diuretics were related to higher mortality from 6 to 12 months.

The most important risk factor over the whole period was presenting a body mass index lower than 20 kg/m² (three months OR 3.06, 95% CI: 1.58–5.92; six months OR 4.42, 95% CI: 2.08–9.38; and 12 months OR 3.68, 95% CI: 1.76–7.69).

Conclusions

We may conclude that male, age, and decreased body mass index determined higher short-term mortality in NYHA IV. In addition, low systolic blood pressure, reduced glomerular filtration, malignancy, and higher doses of loop diuretics contribute to increasing the risk of mortality at medium and long-term. Such variables are easily measurable and can help to decide the best way to face the most advances stages of the disease.

Genetic risk scores to predict the prognosis of chronic heart failure patients in Chinese Han

Chronic heart failure (CHF) has poor prognosis and polygenic heritability, and the genetic risk score (GRS) to predict CHF outcome has not yet been researched comprehensively. In this study, we sought to establish GRS to predict the outcomes of CHF. We re-analysed the proteomics data of failing human heart and combined them to filter the data of high-throughput sequencing in 1000 Chinese CHF cohort. Cox hazards models were used based on single nucleotide polymorphisms (SNPs) to estimate the association of GRS with the prognosis of CHF, and to analyse the difference between individual SNPs and tertiles of genetic risk. In the cohort study, GRS encompassing eight SNPs harboured in seven genes were significantly associated with the prognosis of CHF (P = 2.19 × 10^-10 after adjustment). GRS was used in stratifying individuals into significantly different CHF risk, with those in the top tertiles of GRS distribution having HR of 3.68 (95% CI: 2.40-5.65 P = 2.47 × 10^-10 ) compared with those in the bottom. We developed GRS and demonstrated its association with first event of heart failure endpoint. GRS might be used to stratify individuals for CHF prognostic risk and to predict the outcomes of genomic screening as a complement to conventional risk and NT-proBNP.

Prognosis and Risk Stratification of Patients With Advanced Heart Failure (from PROBE)

In recent years, many prognostic scores have been developed for advanced chronic heart failure (CHF), but none of them is comprised of first- and second level echocardiographic indexes. The aim was to create a new prognostic echocardiographic score for patients with advanced CHF. Patients with advanced CHF were analyzed by standard, 3D, and speckle tracking echocardiography and followed prospectively for 2 ± 0.7 years recording major adverse cardiac events (MACE): cardiovascular death, hospitalization for HF, emergency heart transplantation, and left ventricular assist device or intra-aortic balloon pump implantation. A total of 110 patients were enrolled. The best predictors of MACE were selected on the basis of area under the curve by receiver operating characteristic analysis >0.70: left atrial volume index (no MACE vs MACE groups, 51.3 ± 20 ml/m² vs 67 ± 20 ml/m², p = 0.0003), right ventricular sphericity index (0.53 ± 0.09 vs 0.61 ± 0.10, p = 0.0002), right ventricular fractional area change (41 ± 9% vs 33 ± 9.5, p <0.0001), free-wall right ventricular longitudinal strain (-20 ± 4.5% vs -16 ± 6%, p = 0.0013). A prognostic score formula was calculated as: PROBE score = 1(if left atrial volume index >65 ml/m²) + 1(if right ventricular sphericity index >0.53) + 0.5(if right ventricular fractional area change <36.5%) + 1(if free-wall right ventricular longitudinal strain >-14%). It presented an area under the curve by receiver operating characteristic analysis of 0.90 and classified patients at low (PROBE ≤1), intermediate (PROBE = 1 to 2), or high (PROBE >2) risk of MACE. The Kaplan-Meier analysis revealed a strong correlation between the event-free survival rate and the 3 groups. In conclusion, the PROBE score, with first- and second level echocardiographic parameters, demonstrated a good predictive value for MACE. It represents a useful tool for a noninvasive, individualized, and accurate evaluation and stratification of prognosis in patients with advanced CHF.

A bio-clinical approach for prediction of sudden cardiac death in outpatients with heart failure: The ST2-SCD score

BACKGROUND

Sudden cardiac death (SCD) is one of the main modes of death in heart failure (HF) patients and its prediction remains a real challenge. Our aim was to assess the incidence of SCD at 5 years HF contemporary managed outpatients, and to find a simple prediction model for SCD.

METHODS

SCD was considered any unexpected death, witnessed or not, occurring in a previously stable patient with no evidence of worsening HF or any other cause of death. A competing risk strategy was adopted using the Fine-Gray method of Cox regressions analyses that considered other causes of death as the competing event.

RESULTS

The derivation cohort included 744 consecutive outpatients (72% men, age 67.9 ± 12.2 years, left ventricular ejection fraction [LVEF] 36% ± 14). During follow-up, 312 deaths occurred, 40 SCDs (5.4%). Age, haemoglobin, eGFR, HF duration, high-sensitivity troponin T, NTproBNP, and ST2 were associated with SCD in univariate analyses; HF duration (p = 0.006), eGFR (p < 0.001), LVEF <45% (p = 0.03), and ST2 (p = 0.006) remained in multivariable analysis. A predictive score (ST2-SCD) including dichotomous variables (ST2 > 45, LVEF <45%, HF duration >3 years, eGFR < 55, age ≥ 60 years and male sex) provided a Harrell's C-statistic of 0.82 (0.76-0.89)), reaching 0.87 (0.80-0.95) in the validation cohort (n = 149).

CONCLUSIONS

In contemporary managed HF, SCD occurred in 5.4% of outpatients, accounting for 12.8% of all deaths at 5 years. Of the 3 studied biomarkers, only ST2 remained independently associated with SCD. A model containing age, sex, ST2, eGFR, LVEF, and HF duration reasonably predicted 5-years risk of SCD.

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

INTRODUCTION

Left ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.

METHODS AND ANALYSIS

We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.

ETHICS AND DISSEMINATION

The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.

TRIAL REGISTRATION NUMBER

ChiCTR-POC-17011842; Pre-results.

An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

PURPOSE OF REVIEW

While prediction models incorporating biomarkers are used in heart failure, these have shown wide-ranging discrimination and calibration. This review will discuss externally validated biomarker-based risk models in chronic heart failure patients assessing their quality and relevance to clinical practice.

RECENT FINDINGS

Biomarkers may help in determining prognosis in chronic heart failure patients as they reflect early pathologic processes, even before symptoms or worsening disease. We present the characteristics and describe the performance of 10 externally validated prediction models including at least one biomarker among their predictive factors. Very few models report adequate discrimination and calibration. Some studies evaluated the additional predictive value of adding a biomarker to a model. However, these have not been routinely assessed in subsequent validation studies. New and existing prediction models should include biomarkers, which improve model performance. Ongoing research is needed to assess the performance of models in contemporary patients.

Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure

Background

In clinical practice, a risk prediction model is an effective solitary program to predict prognosis in particular patient groups. B-type natriuretic peptide (BNP)and N-terminal pro-b-type natriuretic peptide (NT-proBNP) are widely recognized outcome-predicting factors for patients with heart failure (HF).This study derived external validation of a risk score to predict 1-year mortality after discharge in hospitalized patients with HF using the Meta-analysis Global Group in Chronic Heart Failure (MAGGIC)program data. We also assessed the effect of adding BNP or NT-proBNP to this risk score model in a Korean HF registry population.

Method and results

We included 5625 patients from the Korean acute heart failure registry (KorAHF) and excluded those who died in hospital. The MAGGIC constructed a risk score to predict mortality in patients with HF by using 13 routinely available patient characteristics (age, gender, diabetes, chronic obstructive pulmonary disorder (COPD), HF diagnosed within the last 18 months, current smoker, NYHA class, use of beta blocker, ACEI or ARB, body mass index, systolic blood pressure, creatinine, and EF). We added BNP or NT-proBNP, which are the most important biomarkers, to the MAGGIC risk scoring system in patients with HF. The outcome measure was 1-year mortality. In multivariable analysis, BNP or NT-proBNP independently predicted death. The risk score was significantly varied between alive and dead groups (30.61 ± 6.32 vs. 24.80 ± 6.81, p < 0.001). After the conjoint use of BNP or NT-proBNP and MAGGIC risk score in patients with HF, a significant difference in risk score was noted (31.23 ± 6.46 vs. 25.25 ± 6.96, p < 0.001).The discrimination abilities of the risk score model with and without biomarker showed minimal improvement (C index of 0.734 for MAGGIC risk score and 0.736 for MAGGIC risk score plus BNP or NT-proBNP, p = 0.0502) and the calibration was found good. However, we achieved a significant improvement in net reclassification and integrated discrimination for mortality (NRI of 33.4%,p < 0.0001 and IDI of 0.002, p < 0.0001).

Conclusion

In the KorAHF, the MAGGIC project HF risk score performed well in a large nationwide contemporary external validation cohort. Furthermore, the addition of BNP or NT-proBNPto the MAGGIC risk score was beneficial in predicting more death in hospitalized patients with HF.

The prognosis of heart failure patients: Does sodium level play a significant role?

Introduction

Heart failure (HF), a major cardiovascular disorder, remains a grievous clinical condition regardless of advances in medical care. Hyponatremia is classified as a serum sodium concentration of <135 mEq/L, and the prevalence, clinical impact and prognostic factor of hyponatremia in heart failure patients varies widely. The current study was conducted with the aim of assessing the prevalence of hyponatremia in patients hospitalized with a diagnosis of HF and comparing baseline clinical characteristic of HF patients based on their sodium status. Survival difference between patients with hyponatremia and normonatremia was also assessed and the clinical prognostic indicators of overall mortality in HF patients were evaluated.

Method

A retrospective cohort study was conducted to assess medical records of heart failure patients who were admitted to Gondar University Referral Hospital. Patients were categorized based on their sodium level status at their first admission to the internal medicine department. Each patient was assigned to either of the following groups: hyponatremia if sodium < 135 mmol/L, or normonatremia if sodium ≥ 135 mmol/L.

Result

Among 388 participants, the prevalence of hyponatremia in the study cohorts was 51.03%. Kaplan-Meier survival curves showed that there was a significant difference in survival status of HF patients among the two cohorts (Log—Rank test, P <0.0001). Hence, patients with normal sodium levels had a higher chance of survival over hyponatremic patients. Multivariate Cox regression has revealed a statistically significant association of mortality with the following variables: advanced age (AHR = 1.035 (1.012–1.058), P = 0.003), hyponatremia (AHR = 4.003 (1.778–9.009), P = 0.001), higher creatinine level (AHR = 1.929 (1.523–2.443), P = <0.0001) and, prescription of angiotensin-converting enzyme inhibitors (AHR = 0.410 (0.199–0.842), P = 0.015) and spironolactone (AHR = 0.511 (0.275–0.949), P = 0.033.

Conclusion

In conclusion, hyponatremia is one of the crucial factors in the clinical prognosis of heart failure patients. However, as other prognostic factors (i.e. medication, creatine level, and age) also played vital roles in overall survival, well-controlled clinical trials (complete with medication dosing, laboratory outputs and long-term prospective follow up) are required to further study the impact of hyponatremia in HF patient’s prognosis in low income nations.

HF progression among outpatients with HF in a community setting

BACKGROUND

Incidence and prognostic impact of heart failure (HF) progression has been not well addressed.

METHODS

From 2009 until 2015, consecutive ambulatory HF patients were recruited. HF progression was defined by the presence of at least two of the following criteria: step up of ≥1 New York Heart Association (NYHA) class; decrease LVEF ≥ 10 points; association of diuretics or increase ≥ 50% of furosemide dosage, or HF hospitalization.

RESULTS

2528 met study criteria (mean age 76; 42% women). Of these, 48% had ischemic heart disease, 18% patients with LVEF ≤ 35%. During a median follow-up of 2.4 years, overall mortality was 31% (95% CI: 29%-33%), whereas rate of HF progression or death was 57% (95% CI: 55%-59%). The 4-year incidence of HF progression was 39% (95% CI: 37%-41%) whereas the competing mortality rate was 18% (95% CI: 16%-19%). Rates of HF progression and death were higher in HF patients with LVEF ≤ 35% vs >35% (HF progression: 42% vs 38%, p = 0.012; death as a competing risk: 22% vs 17%, p = 0.002). HF progression identified HF patients with a worse survival (HR = 3.16, 95% CI: 2.75-3.72). In cause-specific Cox models, age, previous HF hospitalization, chronic obstructive pulmonary disease, chronic kidney disease, anemia, sex, LVEF ≤ 35% emerged as prognostic factors of HF progression.

CONCLUSIONS

Among outpatients with HF, at 4 years 39% presented a HF progression, while 18% died before any sign of HF progression. This trend was higher in patients with LVEF ≤ 35%. These findings may have implications for healthcare planning and resource allocation.

Quantification of Ventricular Repolarization Variation for Sudden Cardiac Death Risk Stratification in Atrial Fibrillation

OBJECTIVE

Atrial fibrillation (AF) rhythm gives rise to an irregular response in ventricular activity, preventing the use of standard ECG-derived risk markers based on ventricular repolarization heterogeneity under this particular condition. In this study, we proposed new indices to quantify repolarization variations in AF patients, assessing their stratification performance in a chronic heart failure population with AF.

METHODS

We developed a method based on a selective bin averaging technique. Consecutive beats preceded by a similar RR interval were selected, from which the average variation within the ST-T complex for each RR range was computed. We proposed two sets of indices: 1) the 2-beat index of ventricular repolarization variation, ( I_V2), computed from pairs of stable consecutive beats; and 2) the 3-beat indices of ventricular repolarization variation, computed in triplets of stable consecutive beats ( I_V3).

RESULTS

These indices showed a significant association with sudden cardiac death (SCD) outcome in the study population. In addition, risk assessment based on the combination of the proposed indices improved stratification performance compared to their individual potential.

CONCLUSION

Patients with enhanced ventricular repolarization variation computed in terms of the proposed indices were successfully associated to a higher SCD incidence in our study population, evidencing their prognostic value.

SIGNIFICANCE

using a simple ambulatory ECG recording, it is possible to stratify AF patients at risk of SCD, which may help cardiologists in adopting most effective therapeutic strategies, with a positive impact in both the patient and healthcare systems.

Long-term outcomes of patients admitted with heart failure in a tertiary care center in India

There are limited studies on heart failure in Indian population OBJECTIVE: Present study aimed to assess the in-hospital 90-day and two year outcomes in patients with ischemic (IHD-HF) and non ischemic heart failure (NIHD-HF).

METHODS

Patients with NYHA Class III & IV, who were admitted to our intensive care unit with heart failure (HF), were evaluated and followed up for 2years.

RESULTS

In our cohort of 287 patients, there were 192 (66.9%) males and 95 (33.1%) females. Patients were divided into IHD-HF of 180 (62.7%) patients and NIHD-HF of 107 (37.3%) patients. Mean age of IHD-HF group was 66 (+/-10) and in the NIHD-HF group was 61 (+/-11). Prevalence of HF increased with age in the IHD-HF population and there was no relation with age in the NIHD-HF population .Patients readmitted within 90days in the IHD-HF were 56% (n-101) and in the NIHD-HF were 32.7% (n-35) [p- 0.001]. Two- year recurrent admissions were 69.4% (n-125) in the IHD-HF patients and 52.3% (n-56) in the NIHD-HF patients, respectively (p-0.004). Mortality at 90days in the IHD-HF patients was 26.6% (n-48) and in NIHD-HF patients were 14.9% (n-16) [p- 0.021]. Two-year mortality was 42.3% (n-76) in the IHD-HF patients and 29.9%(n-32) in the NIHD-HF patients, respectively (p-0.037).

CONCLUSIONS

HF in IHD-HF heralds a bad prognosis with recurrent hospitalizations and high mortality when compared to patients with NIHD-HF.

Electrical risk score beyond the left ventricular ejection fraction: prediction of sudden cardiac death in the Oregon Sudden Unexpected Death Study and the Atherosclerosis Risk in Communities Study

Aims

There is an urgent need to extend sudden cardiac death (SCD) risk stratification beyond the left ventricular ejection fraction (LVEF). We evaluated whether a cumulative electrocardiogram (ECG) risk score would improve identification of individuals at high risk of SCD.

Methods and results

In the community-based Oregon Sudden Unexpected Death Study (catchment population ∼1 million), 522 SCD cases with archived 12-lead ECG available (65.3 ± 14.5 years, 66% male) were compared with 736 geographical controls to assess the incremental value of multiple ECG parameters in SCD prediction. Heart rate, LV hypertrophy, QRS transition zone, QRS-T angle, QTc, and Tpeak-to-Tend interval remained significant in the final model, which was externally validated in the Atherosclerosis Risk in Communities (ARIC) Study. Sixteen percent of cases and 3% of controls had ≥4 abnormal ECG markers. After adjusting for clinical factors and LVEF, increasing ECG risk score was associated with progressively greater odds of SCD. Overall, subjects with ≥4 ECG abnormalities had an odds ratio (OR) of 21.2 for SCD [95% confidence interval (CI) 9.4-47.7; P < 0.001]. In the LVEF >35% subgroup, the OR was 26.1 (95% CI 9.9-68.5; P < 0.001). The ECG risk score increased the C-statistic from 0.625 to 0.753 (P < 0.001), with net reclassification improvement of 0.319 (P < 0.001). In the ARIC cohort validation, risk of SCD associated with ≥4 ECG abnormalities remained significant after multivariable adjustment (hazard ratio 4.84; 95% CI 2.34-9.99; P < 0.001; C-statistic improvement 0.759-0.774; P = 0.019).

Conclusion

This novel cumulative ECG risk score was independently associated with SCD and was particularly effective for LVEF >35% where risk stratification is currently unavailable. These findings warrant further evaluation in prospective clinical investigations.