Clinical Trial Details
— Status: Recruiting
Administrative data
| NCT number |
NCT06321900 |
| Other study ID # |
09M206 |
| Secondary ID |
|
| Status |
Recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
June 2, 2023 |
| Est. completion date |
April 30, 2025 |
Study information
| Verified date |
March 2024 |
| Source |
Istituto Auxologico Italiano |
| Contact |
Luigi Badano, MD, Ph.D. |
| Phone |
+3902619112319 |
| Email |
l.badano[@]auxologico.it |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational [Patient Registry]
|
Clinical Trial 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.
Description:
Background / State of the art Electrocardiography, imaging biomarkers (LVEF measured by 3D
echocardiography, LV global longitudinal strain, and mechanical dispersion by
speckle-tracking 2D echocardiography, the extent of myocardial fibrosis at CMR, reduced
123ImIBG uptake (or accelerated 123I-mIBG washout rate from the heart), and genetic testing,
such as selected DNA variants as Desmoplakin, Lamin A/C, PLN and FLMNC) have been associated
with cardiac arrest events in pts with LV dysfunction.
Although circulating biomarkers of myocardial stress and fibrosis have been reported to
predict prognosis, these biomarkers generally reflect the severity of cardiac dysfunction
rather than the specific risk of SCD. Accordingly, they may be used to identify pts who are
unlikely to benefit from ICD therapy because of the high risk of death resulting from the
progression of HF. However, the predictive power of all these biomarkers of arrhythmic risk
has been tested individually in different studies. Because of the complexity of the
substrates that underlie SCD, it is improbable that any single marker/ test will achieve
significantly better predictive accuracy than LVEF. To overcome this limitation, a
combination of markers could be used to identify potential mechanisms associated with an
increased risk of SCD in individual patients independent of LVEF.
Description and distribution of activities of each operating unit Prof Badano, in Milan, will
be responsible for the whole research project, coordinating the research team and
collaborating with the partners. IRCCS Istituto Auxologico in Milan has a dedicated research
unit with data managers, statisticians, dedicated research nurses, and research technicians
who will collaborate to run the RESPECT project. The partners in Operative Units 1,2,3 and 4
will enroll the patients (WP1) and follow them for a minimum of 12 months (WP2). The central
database will be developed in UO 3 using REDCap (Research Electronic Data Capture powered by
Vanderbilt, WP3). The 4 UOs will set up an independent clinical event committee to review and
classify the events reported during follow-up. Specific core-labs will be organized to
centralize the quali-quantitative analyses scheduled within the project: electrocardiography
(UO3, WP4), quantitative analysis and radiomics of echocardiography data (UO1, WP5) genetic
testing (UO1, WP6), quantitative analysis and radiomics of cardiac magnetic resonance (UO2,
WP7); quantitative analysis of nuclear imaging data on myocardial sympathetic
innervation(UO2, WP 8). Images and ECG tracings will be transferred from the recruiting
partners to a dedicated PACS system in UO1 using DICOM connectivity, and the various core
labs will hook to the MILAN PACS to download images on the workstations for reading. Data
will then be transferred to UO4 to develop the Machine Learning algorithm (WP9) and the App
(WP10). The predictive accuracy of the App will be validated in UO4 using 50% of the enrolled
patients. The evaluation of the economic impact on national health systems of a strategy that
implants ICDs only in high arrhythmic risk patients identified by App, independently on
patients' LVEF, versus the implantation of ICDs guided by current clinical guidelines will be
performed in UO3. Finally, all the RESPECT project partners will collaborate to interpret,
write, and disseminate the results. The publication record of the partners and their national
and international visibility will guarantee the wide and quality dissemination of the results
of the RESPECT project. In summary, the RESPECT project proposes combining several partners
with cutting-edge complementary competencies to contribute to the project jointly. The
proposed network will progress in an area of medical research that requires interdisciplinary
interaction to develop a web-based platform that leverages artificial intelligence, data
visualization, and mobile health technologies to empower physicians to personalize the risk
SCD of their patients independent of the value of LVEF and pursue personalized prevention
programs to reduce the number of patients who die suddenly or who survive a CA with residual
post-ischemic cognitive impairment. The RESPECT consortium is a unique marriage of different
but highly complementary scientific fields and expertise that will allow a comprehensive
evaluation of the potential clinical and cardiac predictors of SCD. The partners of the
RESPECT Project are worldwide renowned experts in their different fields, know each other
very well, and have worked together to run scientific projects. Moreover, all of them are
working in large academic organizations able to recruit many patients, ensure strict
adherence to the protocol, and ensure the high quality of the clinical data, imaging
datasets, and electrocardiography tracings.
Specific aim 1
1. To use statistical and Artificial Intelligence techniques to integrate demographic,
clinical, humoral, genetic, electrocardiography, echocardiography, nuclear imaging, and
cardiac magnetic resonance data from 1250 patients with ischemic and non-ischemic LV
dysfunction to obtain a multiparametric predictive model to personalize the risk of
occurrence of SCD based on phenotyping individual patients based on complex information
similarity. 2. To implement this approach in a web-based app and test its accuracy to predict
the individual risk of SCD To reach these aims, the research partners will enroll consecutive
patients of both genders with either ischemic or nonischemic LV dysfunction defined as LVEF<
50% measured by two-dimensional echocardiography) and NYHA class II-III.
A recruitment period of 12 months to enroll 250 patients/UO and a minimum follow-up of 12
months are planned. In the sample, according to inclusion criteria, a cumulative incidence of
hard events (SCD, resuscitated cardiac arrest, appropriate ICD intervention (i.e., both
anti-tachycardia pacing and device shock), and from 10 to 15% in a median follow-up of 12
months is expected. The expected power to detect a difference between a predictive model with
an area under the ROC curve (AUC) higher than 80% (e.g., 82%), compared with a reference AUC
of 0.75, will be between 86% and 92%.
The development of the predicting model will employ and integrate a variety of statistical
and Artificial Intelligence. The various steps of the development of the predicting model are
summarized as follows:
1a) feature selection and construction will be achieved by investigating filters, wrappers,
and embedded approaches, together with unsupervised deep learning embedding methods, to
reduce the model complexity and prevent the overfitting of the available data set. Bayesian
Networks (BN) will be used to model the probabilistic relationships between the selected
variables and the constructed features. Furthermore, domain knowledge through knowledge
elicitation will also be exploited.
Then, both the score-based and the constraint-based structural learning methods for BN will
be used to build the predicting model;
1b) Semiparametric or parametric regression models with time-dependent outcomes (e.g., Cox or
parametric models as appropriate) will be used to build a statistical predictive model by
cross-validation. Variable selection will be performed with LASSO (the least absolute
shrinkage and selection operator) models. Selected interaction terms between relevant new
parameters and traditional parameters (i.e., LVFE value, NYHA class, ischemic vs.
non-ischemic etiology) will be tested to detect if these new parameters can improve
prediction for the individual patients; 2) image analysis will be carried out by exploiting
Convolutional Neural Networks for classification tasks; 3) an expert system based on fuzzy
reasoning will automatically integrate the output provided by steps 1a, 1b, and 2. The
reasoner will be based on a (minimal) set of probabilistic fuzzy rules to train the clinical
data and provide the final suggestion to the medical doctor. This solution will implicitly
define a transparent, interpretable, and easily extensible decision-support AI system thanks
to human-comprehensible statements.
Altogether, these methods are robust concerning the noise intrinsic in the clinical data and
are effective in (i) learning, although the number of patients is relatively limited, (ii)
leveraging and integrating the physicians¿ knowledge, and (iii) mitigating the overfitting.
To evaluate the economic impact on national health systems of a strategy that implants ICDs
only in high arrhythmic risk patients identified by our predictive model versus the
implantation of ICDs guided by current clinical guidelines The costs related to each implant
of ICD will be calculated based on the average price of the device implant (17500€) and the
mean annual per capita costs after ICD implantation (4136€, 95%CI: 4004-4262) estimated in
the Lomb Cost-effectiveness of the personalized arrhythmic risk stratification model proposed
will also be assessed about the potential implications for the National Health System related
to the reduction of ICD implants in patients with LVEF<35% and low likelihood of clinical
benefit, the number of additional implants of life-saving ICDS in patients with LVEF > 35%,
and the number of ICD-related complications (i.e. infections, inappropriate discharges. Monte
Carlo simulations will be used to evaluate the economic impact of the model, applying the
model to the demographics, sanitary characteristics, and healthcare-related costs of the
Lombardia region. This model will build two scenarios: the first one, in which the implant of
ICDs is according to current guidelines, and the second one, in which ICDs are implanted only
in patients identified by our predictive model to be at high risk of SCD. The expected
specificity of the model will be 90-95%.
Specific aim 3 To assess the fundamental cellular arrhythmogenic mechanisms associated with
specific cardiomyopathy mutations using in vitro cellular models based on patient-specific
induced pluripotent stem cells (iPSC) To reach this goal, 4-5 patients with a genetic
diagnosis and a documented outcome event during follow-up (cardiac arrest, appropriate ICD
firing) will be recruited for a translational substudy. Mononuclear cells will be extracted
from peripheral blood and subjected to reprogramming to generate patient-specific lines of
induced pluripotent stem cells (iPSC). iPSC will be differentiated into cardiomyocytes
(iPSC-CMs) using appropriate protocols for optimal cell maturation. iSPC-CMs from specific
patient lines will be used to explore the main cellular arrhythmogenic mechanisms in vitro,
aiming to correlate the disease-causing mutations with the primary arrhythmic triggers
occurring at a single cardiomyocyte level. Cells will be studied with patch-clamp
measurements and ion fluorescence assays. Molecular biology studies on relevant molecular
targets will also be performed.
