Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators

BACKGROUND

Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable.

OBJECTIVE

The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs.

METHODS

The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed.

RESULTS

Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events.

CONCLUSION

In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Deep learning-based prediction of major arrhythmic events in dilated cardiomyopathy: A proof of concept study

Prediction of major arrhythmic events (MAEs) in dilated cardiomyopathy represents an unmet clinical goal. Computational models and artificial intelligence (AI) are new technological tools that could offer a significant improvement in our ability to predict MAEs. In this proof-of-concept study, we propose a deep learning (DL)-based model, which we termed Deep ARrhythmic Prevention in dilated cardiomyopathy (DARP-D), built using multidimensional cardiac magnetic resonance data (cine videos and hypervideos and LGE images and hyperimages) and clinical covariates, aimed at predicting and tracking an individual patient's risk curve of MAEs (including sudden cardiac death, cardiac arrest due to ventricular fibrillation, sustained ventricular tachycardia lasting ≥30 s or causing haemodynamic collapse in <30 s, appropriate implantable cardiac defibrillator intervention) over time. The model was trained and validated in 70% of a sample of 154 patients with dilated cardiomyopathy and tested in the remaining 30%. DARP-D achieved a 95% CI in Harrell's C concordance indices of 0.12-0.68 on the test set. We demonstrate that our DL approach is feasible and represents a novelty in the field of arrhythmic risk prediction in dilated cardiomyopathy, able to analyze cardiac motion, tissue characteristics, and baseline covariates to predict an individual patient's risk curve of major arrhythmic events. However, the low number of patients, MAEs and epoch of training make the model a promising prototype but not ready for clinical usage. Further research is needed to improve, stabilize and validate the performance of the DARP-D to convert it from an AI experiment to a daily used tool.

An ECG-based artificial intelligence model for assessment of sudden cardiac death risk

BACKGROUND

Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment.

METHODS

Out-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model.

RESULTS

The DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort.

CONCLUSIONS

An ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.

Association of epicardial and intramyocardial fat with ventricular arrhythmias

BACKGROUND

Among patients with ischemic cardiomyopathy (ICM) and nonischemic cardiomyopathy (NICM), myocardial fibrosis is associated with an increased risk for ventricular arrhythmia (VA). Growing evidence suggests that myocardial fat contributes to ventricular arrhythmogenesis. However, little is known about the volume and distribution of epicardial adipose tissue and intramyocardial fat and their relationship with VAs.

OBJECTIVE

The purpose of this study was to assess the association of contrast-enhanced computed tomography (CE-CT)-derived left ventricular (LV) tissue heterogeneity, epicardial adipose tissue volume, and intramyocardial fat volume with the risk of VA in ICM and NICM patients.

METHODS

Patients enrolled in the PROSE-ICD registry who underwent CE-CT were included. Intramyocardial fat volume (voxels between -180 and -5 Hounsfield units [HU]), epicardial adipose tissue volume (between -200 and -50 HU), and LV tissue heterogeneity were calculated. The primary endpoint was appropriate ICD shocks or sudden arrhythmic death.

RESULTS

Among 98 patients (47 ICM, 51 NICM), LV tissue heterogeneity was associated with VA (odds ratio [OR] 1.10; P = .01), particularly in the ICM cohort. In the NICM subgroup, epicardial adipose tissue and intramyocardial fat volume were associated with VA (OR 1.11, P = .01; and OR = 1.21, P = .01, respectively) but not in the ICM patients (OR 0.92, P =.22; and OR = 0.96, P =.19, respectively).

CONCLUSION

In ICM patients, increased fat distribution heterogeneity is associated with VA. In NICM patients, an increased volume of intramyocardial fat and epicardial adipose tissue is associated with a higher risk for VA. Our findings suggest that fat's contribution to VAs depends on the underlying substrate.

Addressing PTSD in Implantable Cardioverter Defibrillator Patients: State-of-the-Art Management of ICD Shock and PTSD

PURPOSE OF REVIEW

This paper reviews the unique processes and treatments of post-traumatic stress in implantable cardioverter-defibrillator (ICD) patients and posits specific clinical management recommendations.

RECENT FINDINGS

PTSD is a common presenting problem for a prospective ICD patient and is a common response to ICD shocks. Approximately 32% of patients with sudden cardiac arrest report significant PTSD symptoms. Following ICD shock, approximately 20% experience PTSD from the shocks. Regardless, PTSD can interrupt and undermine clinical management from a cardiologist perspective and create significant disturbance in patients and families. Few cardiology clinics are outfitted to effectively manage psychological distress, in general, and PTSD, in particular. Effective management of PTSD patients can be achieved with both direct care in the cardiac clinic, as well as indirect care via multidisciplinary consultation and expertise. The importance of emotional validation, return to physical activity, and family engagement is emphasized in current management.

Artificial Intelligence in Ventricular Arrhythmias and Sudden Death

Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

Machine Learning-Based Phenomapping in Patients with Heart Failure and Secondary Prevention Implantable Cardioverter-Defibrillator Implantation: A Proof-of-Concept Study

Background

Previous studies have failed to implement risk stratification in patients with heart failure (HF) who are eligible for secondary implantable cardioverter-defibrillator (ICD) implantation. We aimed to evaluate whether machine learning-based phenomapping using routinely available clinical data can identify subgroups that differ in characteristics and prognoses.

Methods

A total of 389 patients with chronic HF implanted with an ICD were included, and forty-four baseline variables were collected. Phenomapping was performed using hierarchical k-means clustering based on factor analysis of mixed data (FAMD). The utility of phenomapping was validated by comparing the baseline features and outcomes of the first appropriate shock and all-cause death among the phenogroups.

Results

During a median follow-up of 2.7 years for device interrogation and 5.1 years for survival status, 142 (36.5%) first appropriate shocks and 113 (29.0%) all-cause deaths occurred. The first 12 principal components extracted using the FAMD, explaining 60.5% of the total variability, were left for phenomapping. Three mutually exclusive phenogroups were identified. Phenogroup 1 comprised the oldest patients with ischemic cardiomyopathy; had the highest proportion of diabetes mellitus, hypertension, and hyperlipidemia; and had the most favorable cardiac structure and function among the phenogroups. Phenogroup 2 included the youngest patients, mostly those with non-ischemic cardiomyopathy, who had intermediate heart dimensions and function, and the fewest comorbidities. Phenogroup 3 had the worst HF progression. Kaplan-Meier curves revealed significant differences in the first appropriate shock (p = 0.002) and all-cause death (p < 0.001) across the phenogroups. After adjusting for medications in Cox regression, phenogroups 2 and 3 displayed a graded increase in appropriate shock risk (hazard ratio [HR] 1.54, 95% confidence interval [CI] 1.03-2.28, p = 0.033; HR 2.21, 95% CI 1.42-3.43, p < 0.001, respectively; p for trend < 0.001) compared to phenogroup 1. Regarding mortality risk, phenogroup 3 was associated with an increased risk (HR 2.25, 95% CI 1.45-3.49, p < 0.001). In contrast, phenogroup 2 had a risk (p = 0.124) comparable with phenogroup 1.

Conclusions

Machine-learning-based phenomapping can identify distinct phenotype subgroups in patients with clinically heterogeneous HF with secondary prophylactic ICD therapy. This novel strategy may aid personalized medicine for these patients.

Toward Better Risk Stratification for Implantable Cardioverter-Defibrillator Recipients: Implications of Explainable Machine Learning Models

Background: Current guideline-based implantable cardioverter-defibrillator (ICD) implants fail to meet the demands for precision medicine. Machine learning (ML) designed for survival analysis might facilitate personalized risk stratification. We aimed to develop explainable ML models predicting mortality and the first appropriate shock and compare these to standard Cox proportional hazards (CPH) regression in ICD recipients. Methods and Results: Forty-five routine clinical variables were collected. Four fine-tuned ML approaches (elastic net Cox regression, random survival forests, survival support vector machine, and XGBoost) were applied and compared with the CPH model on the test set using Harrell’s C-index. Of 887 adult patients enrolled, 199 patients died (5.0 per 100 person-years) and 265 first appropriate shocks occurred (12.4 per 100 person-years) during the follow-up. Patients were randomly split into training (75%) and test (25%) sets. Among ML models predicting death, XGBoost achieved the highest accuracy and outperformed the CPH model (C-index: 0.794 vs. 0.760, p < 0.001). For appropriate shock, survival support vector machine showed the highest accuracy, although not statistically different from the CPH model (0.621 vs. 0.611, p = 0.243). The feature contribution of ML models assessed by SHAP values at individual and overall levels was in accordance with established knowledge. Accordingly, a bi-dimensional risk matrix integrating death and shock risk was built. This risk stratification framework further classified patients with different likelihoods of benefiting from ICD implant. Conclusions: Explainable ML models offer a promising tool to identify different risk scenarios in ICD-eligible patients and aid clinical decision making. Further evaluation is needed.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

Artificial Intelligence-Augmented Propensity Score, Cost Effectiveness and Computational Ethical Analysis of Cardiac Arrest and Active Cancer with Novel Mortality Predictive Score

Background and objectives: Little is known about outcome improvements and disparities in cardiac arrest and active cancer. We performed the first known AI and propensity score (PS)-augmented clinical, cost-effectiveness, and computational ethical analysis of cardio-oncology cardiac arrests including left heart catheterization (LHC)-related mortality reduction and related disparities. Materials and methods: A nationally representative cohort analysis was performed for mortality and cost by active cancer using the largest United States all-payer inpatient dataset, the National Inpatient Sample, from 2016 to 2018, using deep learning and machine learning augmented propensity score-adjusted (ML-PS) multivariable regression which informed cost-effectiveness and ethical analyses. The Cardiac Arrest Cardio-Oncology Score (CACOS) was then created for the above population and validated. The results informed the computational ethical analysis to determine ethical and related policy recommendations. Results: Of the 101,521,656 hospitalizations, 6,656,883 (6.56%) suffered cardiac arrest of whom 61,300 (0.92%) had active cancer. Patients with versus without active cancer were significantly less likely to receive an inpatient LHC (7.42% versus 20.79%, p < 0.001). In ML-PS regression in active cancer, post-arrest LHC significantly reduced mortality (OR 0.18, 95%CI 0.14−0.24, p < 0.001) which PS matching confirmed by up to 42.87% (95%CI 35.56−50.18, p < 0.001). The CACOS model included the predictors of no inpatient LHC, PEA initial rhythm, metastatic malignancy, and high-risk malignancy (leukemia, pancreas, liver, biliary, and lung). Cost-benefit analysis indicated 292 racial minorities and $2.16 billion could be saved annually by reducing racial disparities in LHC. Ethical analysis indicated the convergent consensus across diverse belief systems that such disparities should be eliminated to optimize just and equitable outcomes. Conclusions: This AI-guided empirical and ethical analysis provides a novel demonstration of LHC mortality reductions in cardio-oncology cardiac arrest and related disparities, along with an innovative predictive model that can be integrated within the digital ecosystem of modern healthcare systems to improve equitable clinical and public health outcomes.

Role of artificial intelligence in defibrillators: a narrative review

Automated external defibrillators (AEDs) and implantable cardioverter defibrillators (ICDs) are used to treat life-threatening arrhythmias. AEDs and ICDs use shock advice algorithms to classify ECG tracings as shockable or non-shockable rhythms in clinical practice. Machine learning algorithms have recently been assessed for shock decision classification with increasing accuracy. Outside of rhythm classification alone, they have been evaluated in diagnosis of causes of cardiac arrest, prediction of success of defibrillation and rhythm classification without the need to interrupt cardiopulmonary resuscitation. This review explores the many applications of machine learning in AEDs and ICDs. While these technologies are exciting areas of research, there remain limitations to their widespread use including high processing power, cost and the ‘black-box’ phenomenon.

Myocardial ATP depletion detected noninvasively predicts sudden cardiac death risk in heart failure patients

BACKGROUND

Sudden cardiac death (SCD) remains a worldwide public health problem in need of better noninvasive predictive tools. Current guidelines for primary preventive SCD therapies, such as implantable cardioverter defibrillators (ICDs), are based on left ventricular ejection fraction (LVEF), but these guidelines are imprecise: fewer than 5% of ICDs deliver lifesaving therapy per year. Impaired cardiac metabolism and ATP depletion cause arrhythmias in experimental models, but to our knowledge a link between arrhythmias and cardiac energetic abnormalities in people has not been explored, nor has the potential for metabolically predicting clinical SCD risk.

METHODS

We prospectively measured myocardial energy metabolism noninvasively with phosphorus magnetic resonance spectroscopy in patients with no history of significant arrhythmias prior to scheduled ICD implantation for primary prevention in the setting of reduced LVEF (≤35%).

RESULTS

By 2 different analyses, low myocardial ATP significantly predicted the composite of subsequent appropriate ICD firings for life-threatening arrhythmias and cardiac death over approximately 10 years. Life-threatening arrhythmia risk was approximately 3-fold higher in patients with low ATP and independent of established risk factors, including LVEF. In patients with normal ATP, rates of appropriate ICD firings were several-fold lower than reported rates of ICD complications and inappropriate firings.

CONCLUSION

To the best of our knowledge, these are the first data linking in vivo myocardial ATP depletion and subsequent significant arrhythmic events in people, suggesting an energetic component to clinical life-threatening ventricular arrhythmogenesis. The findings support investigation of metabolic strategies that limit ATP loss to treat or prevent life-threatening cardiac arrhythmias and herald noninvasive metabolic imaging as a complementary SCD risk stratification tool.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00181233.

FUNDING

This work was supported by the DW Reynolds Foundation, the NIH (grants HL61912, HL056882, HL103812, HL132181, HL140034), and Russell H. Morgan and Clarence Doodeman endowments at Johns Hopkins.

Machine learning techniques for arrhythmic risk stratification: a review of the literature

Ventricular arrhythmias (VAs) and sudden cardiac death (SCD) are significant adverse events that affect the morbidity and mortality of both the general population and patients with predisposing cardiovascular risk factors. Currently, conventional disease-specific scores are used for risk stratification purposes. However, these risk scores have several limitations, including variations among validation cohorts, the inclusion of a limited number of predictors while omitting important variables, as well as hidden relationships between predictors. Machine learning (ML) techniques are based on algorithms that describe intervariable relationships. Recent studies have implemented ML techniques to construct models for the prediction of fatal VAs. However, the application of ML study findings is limited by the absence of established frameworks for its implementation, in addition to clinicians’ unfamiliarity with ML techniques. This review, therefore, aims to provide an accessible and easy-to-understand summary of the existing evidence about the use of ML techniques in the prediction of VAs. Our findings suggest that ML algorithms improve arrhythmic prediction performance in different clinical settings. However, it should be emphasized that prospective studies comparing ML algorithms to conventional risk models are needed while a regulatory framework is required prior to their implementation in clinical practice.

Association of Left Ventricular Tissue Heterogeneity and Intramyocardial Fat on Computed Tomography with Ventricular Arrhythmias in Ischemic Cardiomyopathy

Background

Gray zone, a measure of tissue heterogeneity on late gadolinium enhanced–cardiac magnetic resonance (LGE-CMR) imaging, has been shown to predict ventricular arrhythmias (VAs) in ischemic cardiomyopathy (ICM) patients. However, no studies have described whether left ventricular (LV) tissue heterogeneity and intramyocardial fat mass on contrast-enhanced computed tomography (CE-CT), which provides greater spatial resolution, is useful for assessing the risk of VAs in ICM patients with LV systolic dysfunction and no previous VAs.

Objective

The purpose of this proof-of-concept study was to determine the feasibility of measuring global LV tissue heterogeneity and intramyocardial fat mass by CE-CT for predicting the risk of VAs in ICM patients with LV systolic dysfunction and no previous history of VAs.

Methods

Patients with left ventricular ejection fraction ≤35% and no previous VAs were enrolled in a prospective, observational registry and underwent LGE-CMR. From this cohort, patients with ICM who additionally received CE-CT were included in the present analysis. Gray zone on LGE-CMR was defined as myocardium with signal intensity (SI) > peak SI of healthy myocardium but <50% maximal SI. Tissue heterogeneity on CE-CT was defined as the standard deviation of the Hounsfield unit image gradients (HU/mm) within the myocardium. Intramyocardial fat on CE-CT was identified as regions of image pixels between –180 and –5 HU. The primary outcome was VAs, defined as appropriate implantable cardioverter-defibrillator shock or sudden arrhythmic death.

Results

The study consisted of 47 ICM patients, 13 (27.7%) of whom experienced VA events during mean follow-up of 5.6 ± 3.4 years. Increasing tissue heterogeneity (per HU/mm) was significantly associated with VAs after multivariable adjustment, including for gray zone (odds ratio [OR] 1.22; P = .019). Consistently, patients with tissue heterogeneity values greater than or equal to the median (≥22.2 HU/mm) had >13-fold significantly increased risk of VA events, relative to patients with values lower than the median, after multivariable adjustment that included gray zone (OR 13.13; P = .028). The addition of tissue heterogeneity to gray zone improved prediction of VAs (area under receiver operating characteristic curve increased from 0.815 to 0.876). No association was found between intramyocardial fat mass on CE-CT and VAs (OR 1.00; P = .989).

Conclusion

In ICM patients, CE-CT–derived LV tissue heterogeneity was independently associated with VAs and may represent a novel marker useful for risk stratification.

Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart

Sudden cardiac death from arrhythmia is a major cause of mortality worldwide. Here, we develop a novel deep learning (DL) approach that blends neural networks and survival analysis to predict patient-specific survival curves from contrast-enhanced cardiac magnetic resonance images and clinical covariates for patients with ischemic heart disease. The DL-predicted survival curves offer accurate predictions at times up to 10 years and allow for estimation of uncertainty in predictions. The performance of this learning architecture was evaluated on multi-center internal validation data and tested on an independent test set, achieving concordance index of 0.83 and 0.74, and 10-year integrated Brier score of 0.12 and 0.14. We demonstrate that our DL approach with only raw cardiac images as input outperforms standard survival models constructed using clinical covariates. This technology has the potential to transform clinical decision-making by offering accurate and generalizable predictions of patient-specific survival probabilities of arrhythmic death over time.

Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy

Background

Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence–based methods can be used to develop personalized prediction models and improve early-warning systems.

Objective

The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy.

Methods

This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring: 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing). The feasibility of the prediction engine as a clinical tool, the SafeHeart Platform, is assessed during the feasibility study.

Results

Development study recruitment commenced in 2021. The feasibility study starts in 2022.

Conclusion

SafeHeart is the first study to prospectively collect a multimodal data set to construct a personalized prediction engine for ICD therapy. Moreover, SafeHeart explores the integration and added value of detailed objective accelerometer data in the prediction of clinical events. The translation of the SafeHeart Platform to clinical practice is examined during the feasibility study.

N-Terminal Pro-B-Type Natriuretic Peptide in Risk Stratification of Heart Failure Patients With Implantable Cardioverter-Defibrillator

Background

The prognostic value of N-terminal pro-B-type natriuretic peptide (NT-proBNP) in heart failure (HF) is well-established. However, whether it could facilitate the risk stratification of HF patients with implantable cardioverter-defibrillator (ICD) is still unclear.

Objective

To determine the associations between baseline NT-proBNP and outcomes of all-cause mortality and first appropriate shock due to sustained ventricular tachycardia/ventricular fibrillation (VT/VF) in ICD recipients.

Methods and results

N-terminal pro-B-type natriuretic peptide was measured before ICD implant in 500 patients (mean age 60.2 ± 12.0 years; 415 (83.0%) men; 231 (46.2%) Non-ischemic dilated cardiomyopathy (DCM); 136 (27.2%) primary prevention). The median NT-proBNP was 854.3 pg/ml (interquartile range [IQR]: 402.0 to 1,817.8 pg/ml). We categorized NT-proBNP levels into quartiles and used a restricted cubic spline to evaluate its nonlinear association with outcomes. The incidence rates of mortality and first appropriate shock were 5.6 and 9.1%, respectively. After adjusting for confounding factors, multivariable Cox regression showed a rise in NT-proBNP was associated with an increased risk of all-cause mortality. Compared with the lowest quartile, the hazard ratios (HRs) with 95% CI across increasing quartiles were 1.77 (0.71, 4.43), 3.98 (1.71, 9.25), and 5.90 (2.43, 14.30) for NT-proBNP (p for trend &lt; 0.001). A restricted cubic spline demonstrated a similar pattern with an inflection point found at 3,231.4 pg/ml, beyond which the increase in NT-proBNP was not associated with increased mortality (p for nonlinearity &lt; 0.001). Fine-Gray regression was used to evaluate the association between NT-proBNP and first appropriate shock accounting for the competing risk of death. In the unadjusted, partial, and fully adjusted analysis, however, no significant association could be found regardless of NT-proBNP as a categorical variable or log-transformed continuous variable (all p &gt; 0.05). No nonlinearity was found, either (p = 0.666). Interactions between NT-proBNP and predefined factors were not found (all p &gt; 0.1).

Conclusion

In HF patients with ICD, the rise in NT-proBNP is independently associated with increased mortality until it reaches the inflection point. However, its association with the first appropriate shock was not found. Patients with higher NT-proBNP levels might derive less benefit from ICD implant.

Nomogram predicting death and heart transplantation before appropriate ICD shock in dilated cardiomyopathy

Aims

This study aimed to develop and validate a competing risk nomogram for predicting all‐cause mortality and heart transplantation (HT) before first appropriate shock in non‐ischaemic dilated cardiomyopathy (DCM) patients receiving implantable cardioverter‐defibrillators (ICD).

Methods and results

A total of 218 consecutive DCM patients implanted with ICD between 2010 and 2019 at our institution were retrospectively enrolled. Cox proportional hazards model was primarily built to identify variables associated with death and HT. Then, a Fine–Gray model, accounting for the appropriate shock as a competing risk, was constructed using these selected variables along with implantation indication (primary vs. secondary). Finally, a nomogram based on the Fine–Gray model was established to predict 1‐, 3‐, and 5‐year probabilities of all‐cause mortality and HT before first appropriate shock. The area under the receiver operating characteristic (ROC) curve (AUC), Harrell's C‐index, and calibration curves were used to evaluate and internally validate the performance of this model. The decision curve analysis was applied to assess its clinical utility. The 1‐, 3‐, and 5‐year cumulative incidence of all‐cause mortality and HT without former appropriate shock were 5.3% [95% confidence interval (CI) 2.9–9.9%], 16.6% (95% CI 11–25.0%), and 25.3% (95% CI 17.2–37.1%), respectively. Five variables including implantation indication, left ventricular end‐diastolic diameter, N‐terminal pro‐brain natriuretic peptide, angiotensin‐converting enzyme inhibitor/angiotensin receptor blocker, and amiodarone treatment were independently associated with it (all P < 0.05) and were used for constructing the nomogram. The 1‐, 3‐, and 5‐year AUC of the nomogram were 0.83 (95% CI 0.73–0.94, P < 0.001), 0.84 (95% CI 0.75–0.93, P < 0.001), and 0.85 (95% CI 0.77–0.94, P < 0.001), respectively. The Harrell's C‐index was 0.788 (95% CI 0.697–0.877, P < 0.001; 0.762 for the optimism‐corrected C‐index), showing the good discriminative ability of the model. The calibration was acceptable (optimism‐corrected slope 0.896). Decision curve analysis identified our model was clinically useful within the entire range of potential treatment thresholds for ICD implantation. Three risk groups stratified by scores were significantly different between cumulative incidence curves (P < 0.001). The identified high‐risk group composed 17.9% of our population and did not derive long‐term benefit from ICD.

Conclusions

The proposed nomogram is a simple, useful risk stratification tool for selecting potential ICD recipients in DCM patients. It might facilitate the shared decision‐making between patients and clinicians.

CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia (CERTAINTY)

Better models to identify individuals at low risk of ventricular arrhythmia (VA) are needed for implantable cardioverter-defibrillator (ICD) candidates to mitigate the risk of ICD-related complications. We designed the CERTAINTY study (CinE caRdiac magneTic resonAnce to predIct veNTricular arrhYthmia) with deep learning for VA risk prediction from cine cardiac magnetic resonance (CMR). Using a training cohort of primary prevention ICD recipients (n = 350, 97 women, median age 59 years, 178 ischemic cardiomyopathy) who underwent CMR immediately prior to ICD implantation, we developed two neural networks: Cine Fingerprint Extractor and Risk Predictor. The former extracts cardiac structure and function features from cine CMR in a form of cine fingerprint in a fully unsupervised fashion, and the latter takes in the cine fingerprint and outputs disease outcomes as a cine risk score. Patients with VA (n = 96) had a significantly higher cine risk score than those without VA. Multivariate analysis showed that the cine risk score was significantly associated with VA after adjusting for clinical characteristics, cardiac structure and function including CMR-derived scar extent. These findings indicate that non-contrast, cine CMR inherently contains features to improve VA risk prediction in primary prevention ICD candidates. We solicit participation from multiple centers for external validation.

Baseline and Dynamic Risk Predictors of Appropriate Implantable Cardioverter Defibrillator Therapy

Background Current approaches fail to separate patients at high versus low risk for ventricular arrhythmias owing to overreliance on a snapshot left ventricular ejection fraction measure. We used statistical machine learning to identify important cardiac imaging and time-varying risk predictors. Methods and Results Three hundred eighty-two cardiomyopathy patients (left ventricular ejection fraction ≤35%) underwent cardiac magnetic resonance before primary prevention implantable cardioverter defibrillator insertion. The primary end point was appropriate implantable cardioverter defibrillator discharge or sudden death. Patient characteristics; serum biomarkers of inflammation, neurohormonal status, and injury; and cardiac magnetic resonance-measured left ventricle and left atrial indices and myocardial scar burden were assessed at baseline. Time-varying covariates comprised interval heart failure hospitalizations and left ventricular ejection fractions. A random forest statistical method for survival, longitudinal, and multivariable outcomes incorporating baseline and time-varying variables was compared with (1) Seattle Heart Failure model scores and (2) random forest survival and Cox regression models incorporating baseline characteristics with and without imaging variables. Age averaged 57±13 years with 28% women, 66% white, 51% ischemic, and follow-up time of 5.9±2.3 years. The primary end point (n=75) occurred at 3.3±2.4 years. Random forest statistical method for survival, longitudinal, and multivariable outcomes with baseline and time-varying predictors had the highest area under the receiver operating curve, median 0.88 (95% CI, 0.75-0.96). Top predictors comprised heart failure hospitalization, left ventricle scar, left ventricle and left atrial volumes, left atrial function, and interleukin-6 level; heart failure accounted for 67% of the variation explained by the prediction, imaging 27%, and interleukin-6 2%. Serial left ventricular ejection fraction was not a significant predictor. Conclusions Hospitalization for heart failure and baseline cardiac metrics substantially improve ventricular arrhythmic risk prediction.