{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:05:08Z","timestamp":1773050708050,"version":"3.50.1"},"posted":{"date-parts":[[2026,3,5]]},"group-title":"Cardiovascular Medicine","reference-count":37,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,3,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>\n                    To identify unique echocardiographic signatures associated with\n                    <jats:italic>TTR<\/jats:italic>\n                    + carrier status preceding onset of cardiac amyloidosis.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>\n                    Carrier status for the most common pathogenic\n                    <jats:italic>TTR<\/jats:italic>\n                    variant in the United States, Val142Ile (V142I), found in 4% of African Americans (AA) and 1% of Hispanic\/Latino (H\/L) individuals, confers a 40-60% lifetime risk of developing variant transthyretin amyloidosis (ATTRv), including cardiac amyloidosis (CA) and heart failure (HF). Myocardial amyloid deposition is believed to progress over many years. Genomic screening programs and familial cascade genetic testing are increasingly uncovering pre-symptomatic\n                    <jats:italic>TTR<\/jats:italic>\n                    + carriers, yet no guidelines exist to pragmatically risk stratify these individuals for CA.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>\n                    V142I+ carriers (cases) without prior diagnoses of amyloidosis or HF were identified among Bio\n                    <jats:italic>Me<\/jats:italic>\n                    biobank participants with available exome sequencing data linked to electronic health records (EHRs) including at least one available echocardiogram. Controls were biobank participants with normal\n                    <jats:italic>TTR<\/jats:italic>\n                    sequencing who were age-, sex- and ancestry-matched to cases. Speckle-tracking echocardiography (STE) was applied to images and conventional and strain measurements were evaluated by univariate analyses. A random forest model was trained using a minimal redundancy maximal relevance (mRMR, applied to mitigate overfitting) feature set and evaluated by 5-fold cross-validation to minimize optimism bias. Discriminatory performance was assessed using the area under the receiver operating characteristic curve (AUC).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    49\n                    <jats:italic>TTR<\/jats:italic>\n                    + (100% V142I, median age 61 years, 69.4% female) and 45 matched\n                    <jats:italic>TTR<\/jats:italic>\n                    -biobank participants were included in the model development cohort. STE generated approximately 200 features. Univariate analyses revealed no significant differences between carriers and controls on any individual strain or conventional echocardiographic measurements including global longitudinal, right ventricular and left atrial strain. mRMR feature selection resulted in a set of 15 features retained for all downstream modeling, integrating global amyloid signatures, regional inferolateral strain abnormalities, layer-specific deformation, and mechanical timing heterogeneity. Using this feature set, the model achieved good discrimination (AUC=0.76). Feature importance analysis highlighted relative apical sparing, inferolateral strain reduction, and basal-apical timing gradients as key contributors to model performance. External validation (n=115) confirmed good model discrimination (AUC=0.781, 95% CI: 0.688-0.869, sensitivity 0.983).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>\n                    Machine learning applied to routinely acquired echocardiographic data can identify subtle myocardial abnormalities associated with\n                    <jats:italic>TTR<\/jats:italic>\n                    V142I carrier status prior to development of CA. Key model features are physiologically relevant to known echocardiographic characteristics of overt CA. Genotype-guided echocardiographic surveillance may be a scalable strategy for early detection of CA risk.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.03.04.26347545","type":"posted-content","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T22:30:17Z","timestamp":1772749817000},"source":"Crossref","is-referenced-by-count":0,"title":["Automated machine learning of echocardiographic strain enables identification of early myocardial changes in pre-symptomatic\n                  <i>TTR<\/i>\n                  carriers"],"prefix":"10.64898","author":[{"given":"Amit","family":"Weigman","sequence":"first","affiliation":[{"name":"Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenli","family":"Zhao","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steve L.","family":"Liao","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6601-3267","authenticated-orcid":false,"given":"Maria Giovanna","family":"Trivieri","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"},{"name":"The BioMedical Engineering and Imaging Institute (BMEII), Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"The Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Madiman","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stamatios","family":"Lerakis","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9198-759X","authenticated-orcid":false,"given":"Eimear E.","family":"Kenny","sequence":"additional","affiliation":[{"name":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Medicine, Division of Genomic Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5179-1944","authenticated-orcid":false,"given":"Noura S.","family":"Abul-Husn","sequence":"additional","affiliation":[{"name":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Medicine, Division of Genomic Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1943-0284","authenticated-orcid":false,"given":"Vikas","family":"Pejaver","sequence":"additional","affiliation":[{"name":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7400-5507","authenticated-orcid":false,"given":"Amy R.","family":"Kontorovich","sequence":"additional","affiliation":[{"name":"Fuster Heart Hospital, New York, NY"},{"name":"The Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"The Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY"},{"name":"Department of Medicine, Division of Genomic Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026030901401157000_2026.03.04.26347545v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/S0735-1097(19)33478-3"},{"key":"2026030901401157000_2026.03.04.26347545v1.2","doi-asserted-by":"publisher","DOI":"10.1002\/humu.22619"},{"key":"2026030901401157000_2026.03.04.26347545v1.3","doi-asserted-by":"publisher","DOI":"10.1038\/s41431-019-0337-1"},{"key":"2026030901401157000_2026.03.04.26347545v1.4","doi-asserted-by":"publisher","DOI":"10.1080\/13506120500107162"},{"key":"2026030901401157000_2026.03.04.26347545v1.5","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.17935"},{"key":"2026030901401157000_2026.03.04.26347545v1.6","doi-asserted-by":"crossref","unstructured":"Parker MM , Damrauer SM , Tcheandjieu C et al. 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