ArticleNature communications2021
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 105 citations in OpenAlex.
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort.Scientific reports · 2021Trial
- A trial emulation study indirectly comparing tafamidis and acoramidis in transthyretin amyloid cardiomyopathy: the ReplicATTR study.Journal of comparative effectiveness research · 2026Article
- Screening for patients at risk for cardiac amyloidosis via electronic health records: A multicenter machine learning development and validation study.PLOS digital health · 2026Article
- Established machine learning matches tabular foundation models in clinical predictions.BMC medical informatics and decision making · 2026Article
- Cardio amyloid-artificial intelligence: advanced multi-modal screening for transthyretin cardiac amyloidosis in severe aortic stenosis patients.European heart journal. Digital health · 2026Article
- Pre-Amyloidosis Red-Flag Clinical Diagnoses in Light Chain (AL) Versus Age-Related Transthyretin (ATTRwt) Amyloidosis: Electronic Health Record-Based Descriptive Study.JMIR medical informatics · 2026Article
- Diagnostic Trends and Geographic Health Care Disparities Among Patients With Transthyretin Amyloid Cardiomyopathy.Journal of the American Heart Association · 2026Article
- Natural Language Processing Framework in Early Detection of Amyloidosis: The ALARM Study.JACC. Advances · 2026Article
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- Transferability of a US claims-based machine learning model for ATTRwt-CM identification: a retrospective evaluation in a German setting.Scientific reports · 2026Article
- Machine-learning approach on echocardiography to improve the detection of transthyretin amyloid cardiomyopathy: GRAAL algorithm.European heart journal. Digital health · 2026Article
- Prevalence and incidence of amyloid transthyretin amyloidosis in the USA: insights from claims databases and electronic health records.Open heart · 2025Article
- International Validation of Echocardiographic Artificial Intelligence Amyloid Detection Algorithm.JACC. Advances · 2025Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- Risk for Heart Failure and Atrial Fibrillation Across the Lifespan for Carriers of the Amyloidogenic p.V142ICirculation. Genomic and precision medicine · 2025Article
- Transthyretin Amyloid Cardiomyopathy-2025 Update: Current Diagnostic Approaches and Emerging Therapeutic Options.Journal of clinical medicine · 2025Review
- Spinal amyloid deposits are common among older patients undergoing spinal stenosis decompression surgery.Journal of general and family medicine · 2025Article
- Echocardiographic findings of patients with transthyretin amyloid cardiomyopathy.Journal of echocardiography · 2025Review
- Multi-modality artificial intelligence-based transthyretin amyloid cardiomyopathy detection in patients with severe aortic stenosis.European journal of nuclear medicine and molecular imaging · 2025Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.