Personalized Risk Prediction of Sudden Cardiac Death

NCT06321900 | Recruiting DMC

• 1 other identifier: 09M206
• Study Type: observational
• Target: 1,050
• Locations: 1 country
• Sites: 1

Brief Summary

Sudden cardiac death (SCD) is the final result of cardiac arrest (CA) , defined as an abrupt and unexpected loss of cardiovascular function resulting in circulatory collapse and death. Up to 50% of cardiac deaths in Europe are due to CA. The estimated mortality of CA is approximately 90%, and significant functional and/or cognitive disabilities often persist among those who survive. The advent of the implantable cardioverter-defibrillator (ICD) has revolutionized the prevention of SCD in high-risk patients with reduced left ventricular ejection fraction (LVEF\<35%). However, the algorithm recommended by current guidelines based on LVEF, considered the only parameter to identify high-risk patients, cannot stratify the population and the spectrum of risk with high accuracy. Although the risk of CA is higher among patients with LVEF\<35% and NYHA class\>1, because of the enormity of the population size at risk (i.e., with organic heart disease and LVEF\>35%), most SCD does occur in patients with LVEF\>35%. Additionally, the majority of pts who receive the ICD for primary prevention of SCD will not benefit from the device (in the Sudden Cardiac Death in Heart Failure Trial published in 2005, the rate of appropriate ICD therapy was 21% at five years), and/or will experience some side effects of it. In the Israeli registry of patients who underwent ICD (n= 1729) or cardiac resynchronization therapy (n= 1326), the 12-year cumulative incidence of adverse events was 20% for inappropriate shock, 6% for device-related infection, and 17% for lead failure. Moreover, recent improvements in drug treatment for HF and myocardial revascularization have further reduced the incidence of SCD in pts with low LVEF. Finally, pts with advanced HF are unlikely to benefit from ICD therapy because of the high rates of non-arrhythmic deaths. Therefore, improved risk stratification approaches to guide the selection of pts for ICD implantation are needed, and only a multiparametric approach may aim to personalize the risk prediction of SCD across the broad spectrum of the phenotypes of HF patients. The RESPECT project has been designed to personalize the risk of SCD by integrating and interpreting information highly multidisciplinary: clinical and bio-humoral, genetics and electrocardiography, conventional and advanced cardiac imaging, and data science. The investigators hypothesized that machine learning models capable of dealing with non-linearities and complex interactions among predictors, including genetic, clinical, electrocardiographic, bio-humoral, echocardiographic, cardiac magnetic resonance (CMR), and nuclear cardiology data, would have superior accuracy in predicting the occurrence of SCD compared with the currently recommended metrics of NYHA class and LVEF by two-dimensional echocardiography and that the personalized risk prediction of SCD will translate in more cost-effective use of ICDs. In addition, the investigators will use the multiparametric predictive models to develop a cloud-computing app that will allow clinicians to predict the risk of occurrence of SCD based on specific covariate profiles of individual patients.

Conditions

Sudden Cardiac Death Due to Cardiac Arrhythmia; Cardiomyopathies; Ischemic Heart Disease; Cardiovascular Diseases

Keywords

Personalized medicine; Sudden cardiac death; Artificial intelligence; Genetics; Cardiovascular imaging; Radiomics

Outcome Measures

Primary Outcomes (1)

• Sudden cardiac death

  Composite endpoint of sudden cardiac death, resuscitation after cardiac arrest, hemodynamically significant ventricular tachycardia, and approprite ICD discharge

  through study completion, an average of 2 years

Interventions

Personalized risk model DIAGNOSTIC_TEST

WE will test the hypothesis that interpretable artificial intelligence models including clinical, bio-humoral, genetic, electrocardiography, echocardiography, cardiac magnetic resonance, and nuclear imaging data will predict the risk of SCD in patients with left ventricular dysfunction more accurately than NYHA class and LVEF, and this will translate in a more cost-effective use of ICDs compared to current clinical practice.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with organic heart disease, at risk of sudden cardiac death independently from their left ventricular ejection fraction

You may qualify if:

• history of ischemic cardiomyopathy, LVEF \<50% by 2D echo, and NYHA class II or III;
• primitive (dilated, hypertrophic, and arrhythmogenic) cardiomyopathies at risk of SCD;
• signed informed consent to be part of the study.

You may not qualify if:

• unwilling to be part of the study,
• NYHA class IV,
• history of unexplained syncope, aborted SCD or documented sustained ventricular tachycardia,
• insufficient acoustic window to allow the quantitation of LVEF by 2D echocardiography without the infusion of contrast agents (i.e., more than 2 LV segments not adequately visualized),
• atrial fibrillation during the echocardiographic or CMR studies,
• myocardial infarction in the 40 days or revascularization in the 90 days preceding the enrolment,
• more than moderate stenosis or regurgitation of any heart valve.

Sponsors & Collaborators

• Istituto Auxologico Italiano: lead

Study Sites (1)

Istituto Auxologico Italiano IRCCS

Milan, 20145, Italy

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

Blood samples

MeSH Terms

Conditions

Cardiomyopathies; Myocardial Ischemia; Cardiovascular Diseases; Death, Sudden, Cardiac

Condition Hierarchy (Ancestors)

Heart Diseases; Vascular Diseases; Heart Arrest; Death, Sudden; Death; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Luigi Badano, MD, Ph.D.

  Istituto Auxologico Italiano, IRCCS

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Luigi Badano, MD, Ph.D.

CONTACT

+3902619112319; l.badano@auxologico.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 36 Months
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 6, 2024
• First Posted: March 20, 2024
• Study Start: June 2, 2023
• Primary Completion: December 15, 2024
• Study Completion: April 30, 2025
• Last Updated: March 20, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR
• Time Frame: 2 years
• Access Criteria: E-mail to P.I.

Locations

IT (1)