Deep Learning Model for Cardiac Amyloidosis

Overseen By Timothy J. Poterucha, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Timothy Poterucha

Disqualifiers: Primary amyloidosis, Liver transplant, Active malignancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a new deep learning model, a type of computer program, designed to detect signs of cardiac amyloidosis, a heart condition that often goes unnoticed. The model analyzes heart tests and medical records to identify individuals who might unknowingly have this condition. Those identified as high risk by the model will receive invitations for further testing. Individuals with electronically stored heart tests from the past five years and a high probability of having cardiac amyloidosis may be suitable for this trial. As an unphased trial, this study offers a unique opportunity to contribute to groundbreaking research that could enhance early detection of cardiac amyloidosis.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It is best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this deep learning model is safe for identifying cardiac amyloidosis?

Research has shown that advanced computer programs for detecting heart conditions like cardiac amyloidosis are promising, though some safety concerns exist. Studies have primarily assessed the accuracy and effectiveness of these programs, rather than direct health risks to patients. The main safety issues involve maintaining the privacy and security of personal information, as these advanced programs can increase risks in these areas. However, no evidence indicates that the programs themselves harm patients. This tool aims to identify individuals at high risk for serious heart conditions, potentially leading to earlier diagnosis and treatment. Overall, using AI in this manner is generally considered safe for patients, with the primary focus on protecting personal information. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it uses a deep learning model to identify patients at high risk for undiagnosed cardiac amyloidosis. Unlike traditional diagnostic methods, which often rely on invasive procedures or may miss early signs, this AI-driven approach analyzes complex data patterns to flag potential cases more accurately and efficiently. By potentially catching the disease earlier, this method could lead to quicker interventions and better outcomes for patients.

What evidence suggests that this deep learning model is effective for identifying cardiac amyloidosis?

Research has shown that advanced computer programs, known as deep learning models, effectively spot cardiac amyloidosis, a heart condition. These models analyze data from heart ultrasound images and recordings of heart electrical activity. They predict the condition with an accuracy ranging from 71% to 100%. The sensitivity of these models, or their ability to correctly identify people with the condition, ranges from 16% to 100%, while their specificity, or ability to correctly identify those without the condition, ranges from 75% to 100%. One study highlighted that a model using heart ultrasound images excelled at detecting a specific type of cardiac amyloidosis called ATTR-CM, with a low chance of error. This evidence suggests that AI can significantly aid in early diagnosis of cardiac amyloidosis, which is crucial for better treatment outcomes. The deep learning model will identify participants in this trial as being at high risk for undiagnosed cardiac amyloidosis. ^{1 6 7 8 9}

Who Is on the Research Team?

Timothy J. Poterucha, MD

Principal Investigator

Assistant Professor of Medicine

Are You a Good Fit for This Trial?

This trial is for individuals who may have cardiac amyloidosis, a heart condition that can lead to heart failure. It's aimed at those who haven't been diagnosed yet but are suspected of having the disease based on certain heart tests and clinical factors.

Inclusion Criteria

I am 50 years old or older.

I can understand and sign the informed consent.

High predicted probability of having cardiac amyloidosis as determined by deep learning model

Exclusion Criteria

I have been tested for cardiac amyloidosis.

I do not have any conditions like a stroke that would stop me from joining the study.

Disabling dementia or other mental or behavioral disease

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Diagnostic Testing

Patients identified by the deep learning model are invited for further testing to diagnose cardiac amyloidosis

Up to 1 year

Multiple visits as needed for diagnostic testing

Follow-up

Participants are monitored for safety and effectiveness after diagnostic testing

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• Cardiac amyloidosis deep learning model

Trial Overview

The study is testing a deep learning model designed to spot signs of cardiac amyloidosis using data from echocardiograms, ECGs, and patient history. Participants identified as high-risk by this model will be invited for further tests to confirm diagnosis.

How Is the Trial Designed?

Group I: Intervention Arm Experimental Treatment 1 Intervention

Patients who are identified by the deep learning model as being at high risk for undiagnosed cardiac amyloidosis who are enrolled in the study.

Who Is Running the Clinical Trial?

Timothy Poterucha

Lead Sponsor

Trials

1

Recruited

100+

Pierre Elias

Lead Sponsor

Trials

1

Recruited

100+

Pfizer

Industry Sponsor

Trials

4,712

Recruited

50,980,000+

Known For

Vaccine Innovations

Top Products

Viagra, Zoloft, Lipitor, Prevnar 13

Albert Bourla

Pfizer

Chief Executive Officer since 2019

PhD in Biotechnology of Reproduction, Aristotle University of Thessaloniki

Patrizia Cavazzoni

Pfizer

Chief Medical Officer

MD from McGill University

Eidos Therapeutics, a BridgeBio company

Industry Sponsor

Trials

12

Recruited

2,400+

American Heart Association

Collaborator

Trials

352

Recruited

6,196,000+

Published Research Related to This Trial

A convolutional neural network (CNN) trained on cine-MR images achieved a classification accuracy of 75% in distinguishing between AL and ATTR amyloidosis, outperforming human readers who had an accuracy range of 61.7% to 67.5%.

The cine-CNN also demonstrated a higher area under the ROC curve (AUC) of 0.839 compared to 0.679 for gado-CNN and 0.714 for the best human reader, indicating its potential as a more effective tool for diagnosis, although it is still not optimal for clinical use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Deep Learning to Classify AL versus ATTR Cardiac Amyloidosis MR Images. Germain, P., Vardazaryan, A., Labani, A., et al.[2023]

The study developed a statistical learning algorithm that can help detect cardiac amyloidosis, a rare and often misdiagnosed condition, by analyzing clinical records from hospitalizations.

Despite the low prevalence of cardiac amyloidosis, the algorithm demonstrated the ability to identify the disease effectively, suggesting it could be a valuable tool for screening patients with heart failure.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-World Data and Machine Learning to Predict Cardiac Amyloidosis. García-García, E., González-Romero, GM., Martín-Pérez, EM., et al.[2021]

A study involving 138 patients (74 with cardiac amyloidosis and 64 with hypertrophic cardiomyopathy) demonstrated that machine learning models, particularly random forest and gradient boosting, can effectively differentiate between these two conditions with high accuracy (AUC up to 0.98).

The use of machine learning combined with speckle tracking echocardiography shows promise in improving the timely diagnosis of cardiac amyloidosis, which is often misdiagnosed, potentially leading to better patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic cardiomyopathy. Wu, ZW., Zheng, JL., Kuang, L., et al.[2023]

This proof of concept study suggests that new biomarkers derived from mathematical models of the left ventricle can effectively predict the progression of cardiac amyloidosis, based on routine clinical MRI scans.

The study found that a combination of mechanical, geometrical, and shape features is necessary for accurate predictions, highlighting the complexity of heart disease progression; however, results should be interpreted cautiously due to the small sample size of only seven cases.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Analysis of Cardiac Amyloidosis Progression Using Model-Based Markers. Li, W., Lazarus, A., Gao, H., et al.[2021]

A convolutional neural network (CNN) demonstrated superior accuracy in diagnosing cardiac amyloidosis from cine-CMR images compared to experienced human operators, achieving an accuracy of 82.5% versus 66.0% for patients.

The CNN also showed a higher area under the curve (AUC) of 0.895 compared to 0.727 for human analysis, indicating its enhanced ability to distinguish cardiac amyloidosis from left ventricular hypertrophy of other origins.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Deep Learning Supplants Visual Analysis by Experienced Operators for the Diagnosis of Cardiac Amyloidosis by Cine-CMR. Germain, P., Vardazaryan, A., Padoy, N., et al.[2022]

A machine learning algorithm, originally trained on heart failure data from the USA, effectively identified patients with undiagnosed wild type cardiac amyloidosis (ATTRwt) in the UK, achieving an area under the receiver operating curve (AUROC) of 0.84 for definitive cases and 0.86 for possible cases when using combined data from primary and secondary care.

The algorithm's performance was notably lower when using only primary or secondary care data alone (AUROC ranging from 0.68 to 0.78), suggesting that integrating data from both sources enhances its predictive ability, which could lead to earlier diagnosis and better patient outcomes.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Detecting transthyretin amyloid cardiomyopathy (ATTR-CM) using machine learning: an evaluation of the performance of an algorithm in a UK setting. Tsang, C., Huda, A., Norman, M., et al.[2023]

A machine learning model was developed using medical claims data from 1071 cases of transthyretin amyloid cardiomyopathy and 1071 heart failure controls, successfully identifying at-risk patients for earlier diagnosis and treatment.

The model was validated across three large cohorts (totaling 18,825 cases and controls) and demonstrated strong performance in detecting cardiac amyloidosis, highlighting its potential to improve early diagnosis and management of this often overlooked condition.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy. Huda, A., Castaño, A., Niyogi, A., et al.[2022]

The study utilized machine learning algorithms to analyze cardiac strain and function data from 43 patients with cardiac amyloidosis, achieving a high diagnostic accuracy of 90.9% using the SVM RBF kernel, indicating its potential for improving CA diagnostics.

Key predictors for cardiac amyloidosis were identified, including bi-atrial longitudinal strain and left and right atrial ejection fraction, suggesting that these parameters could enhance non-invasive diagnostic approaches in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function. Eckstein, J., Moghadasi, N., Körperich, H., et al.[2022]

Citations

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The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 ...

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Privacy and data risks are among the most critical concerns, with advanced AI models raising heightened privacy and security issues. The need to ...