Risk Models for Prediction of Implantable Cardioverter-Defibrillator Benefit

ACC/AHA/ASE/HFSA/HRS/SCAI/SCCT/SCMR 2025 Appropriate Use Criteria for Implantable Cardioverter-Defibrillators, Cardiac Resynchronization Therapy, and Pacing

This appropriate use criteria (AUC) document is developed by the American College of Cardiology along with key specialty and subspecialty societies. It provides a comprehensive review of common clinical scenarios where implantable cardioverter-defibrillator (ICD), cardiac resynchronization therapy (CRT), cardiac contractility modulation, leadless pacing, and conduction system pacing therapies are frequently considered. The 335 clinical scenarios covered in this document address ICD indications including those related to secondary prevention, primary prevention, comorbidities, generator replacement at elective replacement indicator, dual-chamber, and totally subcutaneous ICDs, as well as device indications related to CRT, conduction system pacing, leadless pacing, cardiac contractility modulation, and ICD therapy in the setting of left ventricular assist devices (LVADs).

The indications (clinical scenarios) were derived from common applications or anticipated uses, as well as from current clinical practice guidelines and results of studies examining device implantation. The indications in this document were developed by a multidisciplinary writing group and scored by a separate independent rating panel on a scale of 1 to 9 to designate care that is considered “Appropriate” (median 7 to 9), “May Be Appropriate” (median 4 to 6), and “Rarely Appropriate” (median 1 to 3). The final ratings reflect the median score of the 17 rating panel members.

In general, Appropriate designations were assigned to scenarios for which clinical trial evidence and/or clinical experience was available that supported device implantation. In contrast, scenarios for which clinical trial evidence was limited or device implantation seemed reasonable for extenuating or practical reasons were categorized as May Be Appropriate. Scenarios for which there were data showing harm, or no data were available, and medical judgment deemed device therapy was illadvised were categorized as Rarely Appropriate. For example, comorbidities including reduced life expectancy related to noncardiac conditions or severe cognitive dysfunction would negatively impact appropriateness ratings.

The appropriate use criteria for ICD, CRT, and pacing have the potential to enhance clinician decision making, healthcare delivery, and payment policy. Furthermore, recognition of clinical scenarios rated as May Be Appropriate facilitates the identification of areas where there may be gaps in evidence that would benefit from future research.

The American College of Cardiology (ACC) has a long history of developing documents (eg, expert consensus decision pathways, health policy statements, AUC documents) to provide members with guidance on both clinical and nonclinical topics relevant to cardiovascular care. In most circumstances, these documents have been created to complement clinical practice guidelines and to inform clinicians about areas where evidence is new and evolving or where sufficient data are more limited. Despite this, numerous gaps persist, highlighting the need for more streamlined and efficient processes to implement best practices in patient care.

Central to the ACC’s strategic plan is the generation of actionable knowledge —a concept that places emphasis on making clinical information easier to consume, share, integrate, and update. To this end, the ACC has shifted from developing isolated documents to creating integrated “solution sets.” These are groups of closely related activities, policy, mobile applications, decision-support tools, and other resources necessary to transform care and/or improve heart health. Solution sets address key questions facing care teams and attempt to provide practical guidance to be applied at the point of care. They use both established and emerging methods to disseminate information for cardiovascular conditions and their related management. The success of solution sets rests firmly on their ability to have a measurable impact on the delivery of care. Because solution sets reflect current evidence and ongoing gaps in care, the associated tools will be refined across time to match changing evidence and member needs.

AUC represent a key component of solution sets. They consist of common clinical scenarios associated with given disease states and ratings that define when it is reasonable to perform testing or provide therapies and, importantly, when it is not. AUC methodology relies on content development work groups, which create patient scenarios, and independent rating panels that employ a modified Delphi process to rate the relevant options for testing and intervention as Appropriate, May Be Appropriate, or Rarely Appropriate. AUC should not replace clinician judgment and practice experience but should function as tools to improve patient care and health outcomes in a cost-effective manner.

I extend sincere gratitude to the writing group for their invaluable contributions to the development of this document’s structure and clinical scenarios; to the rating panelists—a distinguished group of professionals with diverse expertise—for their thoughtful deliberation of the merits of device implantation across various clinical contexts; and to the reviewers for their thoughtful evaluation of the clinical scenarios and evidence mapping. Additionally, I am grateful to the members of the Solution Set Oversight Committee, which provided insight and guidance, and to ACC staff members María Velásquez and Lara Gold, for their support in bringing this document to fruition.

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia

BACKGROUND

Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however, their application in predicting ventricular arrhythmias in patients with ICDs remains unexplored. We aimed to predict and stratify ventricular arrhythmias requiring ICD therapy using 12-lead electrocardiograms (ECGs) in patients with an ICD.

METHODS AND RESULTS

This retrospective analysis included 200 adult patients who underwent ICD implantation at a single center. Patient demographics, clinical features, and 12-lead ECG data were collected. Unsupervised learning techniques, including K-means and hierarchical clustering, were used to stratify patients based on 12-lead ECG features. Dimensionality reduction methods were also used to optimize clustering accuracy. The silhouette coefficient was used to determine the optimal method and number of clusters. Of the 200 patients, 59 (29.5%) received appropriate therapy. The mean age of patients was 62.3 years, and 81.0% were male. The mean follow-up period was 2,953 days, with no significant intergroup differences. Hierarchical clustering into 3 clusters proved to be the most accurate (silhouette coefficient=0.585). Kaplan-Meier curves for these 3 clusters revealed significant differences (P=0.026).

CONCLUSIONS

We highlight the potential of ML-based clustering using 12-lead ECGs to help in the risk stratification of ventricular arrhythmia. Future research in a larger multicenter setting may provide further insights and refine ICD indications.

The Uncertain Benefit from Implantable Cardioverter-Defibrillators in Nonischemic Cardiomyopathy: How to Guide Clinical Decision-Making?

Life-threatening dysrhythmias remain a significant cause of mortality in patients with nonischemic cardiomyopathy (NICM). Implantable cardioverter-defibrillators (ICD) effectively reduce mortality in patients who have survived a life-threatening arrhythmic event. The evidence for survival benefit of primary prevention ICD for patients with high-risk NICM on guideline-directed medical therapy is not as robust, with efficacy questioned by recent studies. In this review, we summarize the data on the risk of life-threatening arrhythmias in NICM, the recommendations, and the evidence supporting the efficacy of primary prevention ICD, and highlight tools that may improve the identification of patients who could benefit from primary prevention ICD implantation.

Seattle proportional risk model in GISSI-HF: Estimated benefit of ICD in patients with EF less than 50

BACKGROUND

The Seattle Proportional Risk Model (SPRM) estimates the proportion of sudden cardiac death (SCD) in heart failure (HF) patients, identifying those most likely to benefit from implantable cardioverter-defibrillator (ICD) therapy (those with ≥50% estimated proportion of SCD). The GISSI-HF trial tested fish oil and rosuvastatin in HF patients. We used the SPRM to evaluate its accuracy in this cohort in predicting potential ICD benefit in patients with EF ≤50% and an SPRM-predicted proportion of SCD either ≥50% or <50%.

METHODS

The SPRM was estimated in patients with EF ≤50% and in a logistic regression model comparing SCD with non-SCD.

RESULTS

We evaluated 6,750 patients with EF ≤50%. There were 1,892 all-cause deaths, including 610 SCDs. Fifty percent of EF ≤35% patients and 43% with EF 36% to 50% had an SPRM of ≥50%. The SPRM (OR: 1.92, P < 0.0001) accurately predicted the risk of SCD vs non-SCD with an estimated proportion of SCD of 44% vs the observed proportion of 41% at 1 year. By traditional criteria for ICD implantation (EF ≤35%, NYHA class II or III), 64.5% of GISSI-HF patients would be eligible, with an estimated ICD benefit of 0.81. By SPRM >50%, 47.8% may be eligible, including 30.2% with EF >35%. GISSI-HF participants with EF ≤35% with SPRM ≥50% had an estimated ICD HR of 0.64, comparable to patients with EF 36% to 50% with SPRM ≥50% (HR: 0.65).

CONCLUSIONS

The SPRM discriminated SCD vs non-SCD in GISSI-HF, both in patients with EF ≤35% and with EF 36% to 50%. The comparable estimated ICD benefit in patients with EF ≤35% and EF 36% to 50% supports the use of a proportional risk model for shared decision making with patients being considered for primary prevention ICD therapy.

Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias

The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.

Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning

We aimed to develop machine learning-based predictive models for identifying inappropriate implantable cardioverter-defibrillator (ICD) therapy. Our study included 182 consecutive cases (average age 62.2 ± 4.5 years, 169 men) and employed 14 non-deep learning models for prediction (hold-out method). These models utilized selected electrocardiogram parameters and clinical features collected after ICD implantation. From the feature importance analysis of the best ML model, we established easily calculable scores. Among the patients, 25 (13.7%) experienced inappropriate therapy, and we identified 16 significant predictors. Using recursive feature elimination with cross-validation, we reduced the features to six with high feature importance: history of atrial arrhythmia (Atr-arrhythm), ischemic cardiomyopathy (ICM), absence of diabetes mellitus (DM), lack of cardiac resynchronization therapy (CRT), V3 ST level at J point (V3 STJ), and V5 R-wave amplitudes (V5R amp). The extra-trees classifier yielded the highest area under receiver operating characteristics curve (AUROC; 0.869 on test data). Thus, the Cardi35 score was defined as [+ 5.5*Atr-arrhythm - 1.5*CRT + 1.0*V3STJ + 1.0*V5R - 1.0*ICM - 0.5*DM], which demonstrated a hazard ratio of 1.62 (P < 0.001). A cut-off value of the score + 5.5 showed high AUROC (0.826). The ML approach can yield a robust prediction model, and the Cardi35 score was a convenient predictor for inappropriate therapy.

The Uncertain Benefit from Implantable Cardioverter-Defibrillators in Nonischemic Cardiomyopathy: How to Guide Clinical Decision-Making?

Life-threatening dysrhythmias remain a significant cause of mortality in patients with nonischemic cardiomyopathy (NICM). Implantable cardioverter-defibrillators (ICD) effectively reduce mortality in patients who have survived a life-threatening arrhythmic event. The evidence for survival benefit of primary prevention ICD for patients with high-risk NICM on guideline-directed medical therapy is not as robust, with efficacy questioned by recent studies. In this review, we summarize the data on the risk of life-threatening arrhythmias in NICM, the recommendations, and the evidence supporting the efficacy of primary prevention ICD, and highlight tools that may improve the identification of patients who could benefit from primary prevention ICD implantation.

The Lancet Commission to reduce the global burden of sudden cardiac death: a call for multidisciplinary action

Despite major advancements in cardiovascular medicine, sudden cardiac death (SCD) continues to be an enormous medical and societal challenge, claiming millions of lives every year. Efforts to prevent SCD are hampered by imperfect risk prediction and inadequate solutions to specifically address arrhythmogenesis. Although resuscitation strategies have witnessed substantial evolution, there is a need to strengthen the organisation of community interventions and emergency medical systems across varied locations and health-care structures. With all the technological and medical advances of the 21st century, the fact that survival from sudden cardiac arrest (SCA) remains lower than 10% in most parts of the world is unacceptable. Recognising this urgent need, the Lancet Commission on SCD was constituted, bringing together 30 international experts in varied disciplines. Consistent progress in tackling SCD will require a completely revamped approach to SCD prevention, with wide-sweeping policy changes that will empower the development of both governmental and community-based programmes to maximise survival from SCA, and to comprehensively attend to survivors and decedents' families after the event. International collaborative efforts that maximally leverage and connect the expertise of various research organisations will need to be prioritised to properly address identified gaps. The Commission places substantial emphasis on the need to develop a multidisciplinary strategy that encompasses all aspects of SCD prevention and treatment. The Commission provides a critical assessment of the current scientific efforts in the field, and puts forth key recommendations to challenge, activate, and intensify efforts by both the scientific and global community with new directions, research, and innovation to reduce the burden of SCD worldwide.

Sudden Cardiac Death in Heart Failure: A 20-Year Perspective From a Mediterranean Cohort

BACKGROUND

The prediction of sudden cardiac death (SCD) in heart failure (HF) remains an unmet need. The aim of our study was to assess the prevalence of SCD over 20 years in outpatients with HF managed in a Mediterranean multidisciplinary HF Clinic, and to compare the proportion of SCD (SCD/all-cause death) to the expected proportional occurrence based on the validated Seattle Proportional Risk Model (SPRM) score.

METHODS AND RESULTS

This prospective observational registry study included 2772 outpatients with HF admitted between August 2001 and May 2021. Patients were included when the cause of death was known and SPRM score was available. Over the 20-year study period, 1351 patients (48.7%) died during a median follow-up period of 3.8 years (interquartile range 1.6-7.6). Among these patients, the proportion of SCD out of the total of deaths was 13.6%, whereas the predicted by SPRM was 39.6%. This lower proportion of SCD was observed independently of left ventricular ejection fraction, ischemic etiology, and the presence of an implantable cardiac defibrillator.

CONCLUSIONS

In a Mediterranean cohort of outpatients with HF, the proportion of SCD was lower than expected based on the SPRM score. Future studies should investigate to what extend epidemiological and guideline-directed medical therapy patterns influence SCD.

Predicting the survival benefit of cardiac resynchronization therapy with defibrillator function for non-ischemic heart failure-Role of the Goldenberg risk score

Aims

Primary prevention of sudden cardiac death (SCD) in non-ischemic heart failure (HF) patients remains a topic of debate at cardiac resynchronization therapy (CRT) implantation requiring individual risk assessment. Using the Goldenberg SCD risk score, we aimed to predict, which non-ischemic HF patients will benefit from the addition of an implantable cardioverter defibrillator (ICD) to CRT at long-term.

Methods

Between 2000 and 2018 non-ischemic HF patients undergoing CRT implantation were collected into our retrospective registry. The Goldenberg risk score (GRS) was calculated by the presence of atrial fibrillation, New York Heat Association (NYHA) class > 2, age > 70 years, blood urea nitrogen > 26 mg/dl and QRS > 120 ms. The primary endpoint was all-cause mortality, heart transplantation or left ventricular assist device implantation.

Results

From 667 patients, 347 (52%) underwent cardiac resynchronization therapy-pacemaker (CRT-P), 320 (48%) cardiac resynchronization therapy-defibrillator (CRT-D) implantations. During the median follow up time of 4.3 years, 306 (46%) patients reached the primary endpoint (CRT-D 37% vs. CRT-P 63%; p < 0.001). CRT-D patients were younger (64 vs. 69 years; p < 0.001), infrequently females (26 vs. 39%; p < 0.001), and had a lower ejection fraction (27 vs. 29%; p < 0.01) compared to CRT-P patients. After GRS calculation, patients were dichotomized by low (< 3) and high (≥ 3) scores. CRT-D patients with low GRS showed a mortality benefit compared to CRT-P (HR 0.68; 95% CI 0.48-0.96; p = 0.03), high-risk patients did not (HR 0.84; 95% CI 0.62-1.13; p = 0.26).

Conclusion

In our non-ischemic cohort, patients with low GRS showed a clear long-term mortality benefit by adding ICD to CRT, however, in high-risk patients no further benefit could be observed.

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.

Survival Probability and Survival Benefit Associated With Primary Prevention Implantable Cardioverter-Defibrillator Generator Changes

Background As patients derive variable benefit from generator changes (GCs) of implantable cardioverter-defibrillators (ICDs) with an original primary prevention (PP) indication, better predictors of outcomes are needed. Methods and Results In the National Cardiovascular Data Registry ICD Registry, patients undergoing GCs of initial non-cardiac resynchronization therapy PP ICDs in 2012 to 2016, predictors of post-GC survival and survival benefit versus control heart failure patients without ICDs were assessed. These included predicted annual mortality based on the Seattle Heart Failure Model, left ventricular ejection fraction (LVEF) >35%, and the probability that a patient's death would be arrhythmic (proportional risk of arrhythmic death [PRAD]). In 40 933 patients undergoing GCs of initial noncardiac resynchronization therapy PP ICDs (age 67.7±12.0 years, 24.5% women, 34.1% with LVEF >35%), Seattle Heart Failure Model-predicted annual mortality had the greatest effect size for decreased post-GC survival (P<0.0001). Patients undergoing GCs of initial noncardiac resynchronization therapy PP ICDs with LVEF >35% had a lower Seattle Heart Failure Model-adjusted survival versus 23 472 control heart failure patients without ICDs (model interaction hazard ratio, 1.21 [95% CI, 1.11-1.31]). In patients undergoing GCs of initial noncardiac resynchonization therapy PP ICDs with LVEF ≤35%, the model indicated worse survival versus controls in the 21% of patients with a PRAD <43% and improved survival in the 10% with PRAD >65%. The association of the PRAD with survival benefit or harm was similar in patients with or without pre-GC ICD therapies. Conclusions Patients who received replacement of an ICD originally implanted for primary prevention and had at the time of GC either LVEF >35% alone or both LVEF ≤35% and PRAD <43% had worse survival versus controls without ICDs.

Strategies for Sudden Cardiac Death Prevention

Sudden cardiac death (SCD) represents a major challenge in modern medicine. The prevention of SCD orbits on two levels, the general population level and individual level. Much research has been done with the aim to improve risk stratification of SCD, although no radical changes in evidence and in therapeutic strategy have been achieved. Artificial intelligence (AI), and in particular machine learning (ML) models, represent novel technologic tools that promise to improve predictive ability of fatal arrhythmic events. In this review, firstly, we analyzed the electrophysiological basis and the major clues of SCD prevention at population and individual level; secondly, we reviewed the main research where ML models were used for risk stratification in other field of cardiology, suggesting its potentiality in the field of SCD prevention.

2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC

Document Reviewers: Rudolf A. de Boer (CPG Review Coordinator) (Netherlands), P. Christian Schulze (CPG Review Coordinator) (Germany), Magdy Abdelhamid (Egypt), Victor Aboyans (France), Stamatis Adamopoulos (Greece), Stefan D. Anker (Germany), Elena Arbelo (Spain), Riccardo Asteggiano (Italy), Johann Bauersachs (Germany), Antoni Bayes-Genis (Spain), Michael A. Borger (Germany), Werner Budts (Belgium), Maja Cikes (Croatia), Kevin Damman (Netherlands), Victoria Delgado (Netherlands), Paul Dendale (Belgium), Polychronis Dilaveris (Greece), Heinz Drexel (Austria), Justin Ezekowitz (Canada), Volkmar Falk (Germany), Laurent Fauchier (France), Gerasimos Filippatos (Greece), Alan Fraser (United Kingdom), Norbert Frey (Germany), Chris P. Gale (United Kingdom), Finn Gustafsson (Denmark), Julie Harris (United Kingdom), Bernard Iung (France), Stefan Janssens (Belgium), Mariell Jessup (United States of America), Aleksandra Konradi (Russia), Dipak Kotecha (United Kingdom), Ekaterini Lambrinou (Cyprus), Patrizio Lancellotti (Belgium), Ulf Landmesser (Germany), Christophe Leclercq (France), Basil S. Lewis (Israel), Francisco Leyva (United Kingdom), AleVs Linhart (Czech Republic), Maja-Lisa Løchen (Norway), Lars H. Lund (Sweden), Donna Mancini (United States of America), Josep Masip (Spain), Davor Milicic (Croatia), Christian Mueller (Switzerland), Holger Nef (Germany), Jens-Cosedis Nielsen (Denmark), Lis Neubeck (United Kingdom), Michel Noutsias (Germany), Steffen E. Petersen (United Kingdom), Anna Sonia Petronio (Italy), Piotr Ponikowski (Poland), Eva Prescott (Denmark), Amina Rakisheva (Kazakhstan), Dimitrios J. Richter (Greece), Evgeny Schlyakhto (Russia), Petar Seferovic (Serbia), Michele Senni (Italy), Marta Sitges (Spain), Miguel Sousa-Uva (Portugal), Carlo G. Tocchetti (Italy), Rhian M. Touyz (United Kingdom), Carsten Tschoepe (Germany), Johannes Waltenberger (Germany/Switzerland) All experts involved in the development of these guidelines have submitted declarations of interest. These have been compiled in a report and published in a supplementary document simultaneously to the guidelines. The report is also available on the ESC website www.escardio.org/guidelines For the Supplementary Data which include background information and detailed discussion of the data that have provided the basis for the guidelines see European Heart Journal online.

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.

The Arrhythmic Phenotype in Cardiomyopathy

In the wide phenotypic spectrum of cardiomyopathies, sudden cardiac death (SCD) has always been the most visible and devastating disease complication. The introduction of implantable cardioverter-defibrillators for SCD prevention by the late 1980s has moved the question from how to whom we should protect from SCD, leaving clinicians with a measure of uncertainty regarding the most reliable option to guide identification of the highest-risk patients. In this review, we will go through all the available evidence in the field of arrhythmic expression and arrhythmic risk stratification in the different phenotypes of cardiomyopathies to provide practical suggestions in daily clinical management.

A year in heart failure: an update of recent findings

Major changes have occurred in these last years in heart failure (HF) management. Landmark trials and the 2021 European Society of Cardiology guidelines for the diagnosis and treatment of HF have established four classes of drugs for treatment of HF with reduced ejection fraction: angiotensin-converting enzyme inhibitors or an angiotensin receptor-neprilysin inhibitor, beta-blockers, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter 2 inhibitors, namely, dapagliflozin or empagliflozin. These drugs consistently showed benefits on mortality, HF hospitalizations, and quality of life. Correction of iron deficiency is indicated to improve symptoms and reduce HF hospitalizations. AFFIRM-AHF showed 26% reduction in total HF hospitalizations with ferric carboxymaltose vs. placebo in patients hospitalized for acute HF (P = 0.013). The guanylate cyclase activator vericiguat and the myosin activator omecamtiv mecarbil improved outcomes in randomized placebo-controlled trials, and vericiguat is now approved for clinical practice. Treatment of HF with preserved ejection fraction (HFpEF) was a major unmet clinical need until this year when the results of EMPEROR-Preserved (EMPagliflozin outcomE tRial in Patients With chrOnic HFpEF) were issued. Compared with placebo, empagliflozin reduced by 21% (hazard ratio, 0.79; 95% confidence interval, 0.69 to 0.90; P < 0.001), the primary outcome of cardiovascular death or HF hospitalization. Advances in the treatment of specific phenotypes of HF, including atrial fibrillation, valvular heart disease, cardiomyopathies, cardiac amyloidosis, and cancer-related HF, also occurred. Coronavirus disease 2019 (COVID-19) pandemic still plays a major role in HF epidemiology and management. All these aspects are highlighted in this review.

Frequency, predictors and prognostic impact of implantable cardioverter defibrillator shocks in a primary prevention population with heart failure and reduced ejection fraction

AIMS

The role of the implantable cardioverter defibrillator (ICD) in primary prevention real-world population is debated. We sought to evaluate the incidence, predictors and prognostic impact of ICD shocks in consecutive heart failure patients implanted for primary prevention at our tertiary institution.

METHODS AND RESULTS

We retrospectively selected a sample of 497 patients (mean age 64.8 years, 82.1% men, average left ventricular ejection fraction, LVEF, 27.1%). At long-term follow-up (median time 70.4 months), total mortality was 40.8%, and 16.5% of patients had received at least one appropriate shock (3.12%/year). Inappropriate shock [odds ratio (OR) 1.93, 95% confidence interval (95% CI) 1.08-3.47; P = 0.027] and length of follow-up (1 year, OR 1.01, 95% CI 1.00-1.01; P = 0.0031) were associated with the occurrence of appropriate shock, whereas atrial fibrillation (OR 2.65, 95% CI 1.55-4.51, P < 0.001), length of follow-up (1-year OR 1.01, 95% CI 1.00-1.01, P < 0.001) and appropriate shock (OR 1.93, 95% CI 1.08-3.47, P = 0.027) were associated with the occurrence of inappropriate shock. Neither appropriate nor inappropriate shock independently increased mortality risk, whereas older age (hazard ratio 1.05; 95% CI 1.04-1.07; P < 0.001), atrial fibrillation (hazard ratio 2.25; 95% CI 1.67-3.02; P < 0.001) and lower LVEF (hazard ratio 0.97; 95% CI 0.94-0.99; P = 0.004) did.

CONCLUSION

Incidence of shocks in real-world primary prevention ICD recipients might be lower than expected, and the association between ICD shocks and prolongation of survival is not as clear-cut as might be perceived. Further investigations from larger real-world samples are warranted.

Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach

PURPOSE OF REVIEW

Sudden cardiac death is recognised as a devastating consequence of non-ischaemic dilated cardiomyopathy. Although implantable cardiac defibrillators offer protection against some forms of sudden death, the identification of patients in this population most likely to benefit from this therapy remains challenging and controversial. In this review, we evaluate current guidelines and explore established and novel predictors of sudden cardiac death in patients with non-ischaemic dilated cardiomyopathy.

RECENT FINDINGS

Current international guidelines for primary prevention implantable defibrillator therapy do not result in improved longevity for many patients with non-ischemic cardiomyopathy and severe left ventricular dysfunction. More precise methods for identifying higher-risk patients that derive true prognostic benefit from this therapy are required. Dynamic and multi-parametric characterization of myocardial, electrical, serological and genetic substrate offers novel strategies for predicting major arrhythmic risk. Balancing the risk of non-sudden death offers an opportunity to personalize therapy and avoid unnecessary device implantation for those less likely to derive benefit.

Modeling defibrillation benefit for survival among cardiac resynchronization therapy defibrillator recipients

BACKGROUND

Patients with heart failure having a low expected probability of arrhythmic death may not benefit from implantable cardioverter defibrillators (ICDs).

OBJECTIVE

The objective was to validate models to identify cardiac resynchronization therapy (CRT) candidates who may not require CRT devices with ICD functionality.

METHODS

Heart failure (HF) patients with CRT-Ds and non-CRT ICDs from the National Cardiovascular Data Registry and others with no device from 3 separate registries and 3 heart failure trials were analyzed using multivariable Cox proportional hazards regression for survival with the Seattle Heart Failure Model (SHFM; estimates overall mortality) and the Seattle Proportional Risk Model (SPRM; estimates proportional risk of arrhythmic death).

RESULTS

Among 60,185 patients (age 68.6 ± 11.3 years, 31.9% female) meeting CRT-D criteria, 38,348 had CRT-Ds, 11,389 had non-CRT ICDs, and 10,448 had no device. CRT-D patients had a prominent adjusted survival benefit (HR 0.52, 95% CI 0.50-0.55, P < .0001 versus no device). CRT-D patients with SHFM-predicted 4-year survival ≥81% (median) and a low SPRM-predicted probability of an arrhythmic mode of death ≤42% (median) had an absolute adjusted risk reduction attributable to ICD functionality of just 0.95%/year with the majority of survival benefit (70%) attributable to CRT pacing. In contrast, CRT-D patients with SHFM-predicted survival <median or SPRM >median had substantially more ICD-attributable benefit (absolute risk reduction of 2.6%/year combined; P < .0001).

CONCLUSIONS

The SPRM and SHFM identified a quarter of real-world, primary prevention CRT-D patients with minimal benefit from ICD functionality. Further studies to evaluate CRT pacemakers in these low-risk CRT candidates are indicated.

Prediction of sudden cardiac death in Japanese heart failure patients: international validation of the Seattle Proportional Risk Model

Aims

Heart failure (HF) is associated with an increased risk of sudden cardiac death (SCD). This study sought to demonstrate the incidence of SCD within a multicentre Japanese registry of HF patients hospitalized for acute decompensation, and externally validate the Seattle Proportional Risk Model (SPRM).

Methods and results

We consecutively registered 2240 acute HF patients from academic institutions in Tokyo, Japan. The discrimination and calibration of the SPRM were assessed by the c-statistic, Hosmer–Lemeshow statistic, and visual plotting among non-survivors. Patient-level SPRM predictions and implantable cardioverter-defibrillator (ICD) benefit [ICD estimated hazard ratio (HR), derived from the Cox proportional hazards model in the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT)] was calculated. During the 2-year follow-up, 356 deaths (15.9%) occurred, which included 76 adjudicated SCDs (3.4%) and 280 non-SCDs (12.5%). The SPRM showed acceptable discrimination [c-index = 0.63; 95% confidence interval (CI) 0.56–0.70], similar to that of original SPRM-derivation cohort. The calibration plot showed reasonable conformance. Among HF patients with reduced ejection fraction (EF; &lt; 40%), SPRM showed improved discrimination compared with the ICD eligibility criteria (e.g. New York Heart Association functional Class II–III with EF ≤ 35%): c-index = 0.53 (95% CI 0.42–0.63) vs. 0.65 (95% CI 0.55–0.75) for SPRM. Finally, in the subgroup of 246 patients with both EF ≤ 35% and SPRM-predicted risk of ≥ 42.0% (SCD-HeFT defined ICD benefit threshold), mean ICD estimated HR was 0.70 (30% reduction of all-cause mortality by ICD).

Conclusion

The cumulative incidence of SCD was 3.4% in Japanese HF registry. The SPRM performed reasonably well in Japanese patients and may aid in improving SCD prediction.

Myocardial fibrosis and the effect of primary prophylactic defibrillator implantation in patients with non-ischemic systolic heart failure-DANISH-MRI

AIMS

Patients with non-ischemic systolic heart failure have an increased risk of sudden cardiac death (SCD). Myocardial fibrosis, detected as late gadolinium enhancement (LGE) with cardiac magnetic resonance (CMR), has been shown to predict all-cause mortality. We hypothesized that LGE can identify patients with non-ischemic heart failure who will benefit from ICD implantation.

METHODS AND RESULTS

In this prospective observational sub-study of the Danish Study to Assess the Efficacy of ICDs in Patients with Nonischemic Systolic Heart Failure on Mortality (DANISH), 252 patients underwent CMR. LGE was quantified by the full width/half maximum method. The primary endpoint was all-cause mortality. LGE could be adequately assessed in 236 patients, median age was 61 years and median duration of heart failure was 14 months; there were 108 patients (46%) randomized to ICD. Median follow-up time was 5.3 years. Median left ventricular ejection fraction on CMR was 35%. In all, 50 patients died. LGE was present in 113 patients (48%). The presence of LGE was an independent predictor of all-cause mortality (HR 1.82; 95% CI 1.002-3.29; P = .049) after adjusting for known cardiovascular risk factors. ICD implantation did not impact all-cause mortality, for either patients with LGE (HR 1.18; 95% CI 0.59-2.38; P = .63), or for patients without LGE (HR 1.00; 95% CI 0.39-2.53; P = .99), (P for interaction =0.79).

CONCLUSION

In patients with non-ischemic systolic heart failure, LGE predicted all-cause mortality. However, in this cohort, LGE did not identify a group of patients who survived longer by receiving an ICD.

Present criteria for prophylactic ICD implantation: Insights from the EU-CERT-ICD (Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope) project

BACKGROUND

The clinical effectiveness of primary prevention implantable cardioverter defibrillator (ICD) therapy is under debate. It is urgently needed to better identify patients who benefit from prophylactic ICD therapy. The EUropean Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators (EU-CERT-ICD) completed in 2019 will assess this issue.

SUMMARY

The EU-CERT-ICD is a prospective investigator-initiated non-randomized, controlled, multicenter observational cohort study done in 44 centers across 15 European countries. A total of 2327 patients with heart failure due to ischemic heart disease or dilated cardiomyopathy indicated for primary prophylactic ICD implantation were recruited between 2014 and 2018 (>1500 patients at first ICD implantation, >750 patients non-randomized non-ICD control group). The primary endpoint was all-cause mortality, and first appropriate shock was co-primary endpoint. At baseline, all patients underwent 12‑lead ECG and Holter-ECG analysis using multiple advanced methods for risk stratification as well as documentation of clinical characteristics and laboratory values. The EU-CERT-ICD data will provide much needed information on the survival benefit of preventive ICD therapy and expand on previous prospective risk stratification studies which showed very good applicability of clinical parameters and advanced risk stratifiers in order to define patient subgroups with above or below average ICD benefit.

CONCLUSION

The EU-CERT-ICD study will provide new and current data about effectiveness of primary prophylactic ICD implantation. The study also aims for improved risk stratification and patient selection using clinical risk markers in general, and advanced ECG risk markers in particular.