{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T14:48:46Z","timestamp":1786373326008,"version":"build-2736575974"},"reference-count":27,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"7","funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["R01HL155915"],"award-info":[{"award-number":["R01HL155915"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["R01DK127139"],"award-info":[{"award-number":["R01DK127139"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000062","name":"National Institute of Diabetes and Digestive and Kidney Diseases","doi-asserted-by":"publisher","award":["K23DK124645"],"award-info":[{"award-number":["K23DK124645"]}],"id":[{"id":"10.13039\/100000062","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["CJASN"],"published-print":{"date-parts":[[2022,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background and objectives<\/jats:title>\n                    <jats:p>Left ventricular ejection fraction is disrupted in patients on maintenance hemodialysis and can be estimated using deep learning models on electrocardiograms. Smaller sample sizes within this population may be mitigated using transfer learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting, participants, &amp; measurements<\/jats:title>\n                    <jats:p>\n                      We identified patients on hemodialysis with transthoracic echocardiograms within 7 days of electrocardiogram using diagnostic\/procedure codes. We developed four models: (\n                      <jats:italic toggle=\"yes\">1<\/jats:italic>\n                      ) trained from scratch in patients on hemodialysis, (\n                      <jats:italic toggle=\"yes\">2<\/jats:italic>\n                      ) pretrained on a publicly available set of natural images (ImageNet), (\n                      <jats:italic toggle=\"yes\">3<\/jats:italic>\n                      ) pretrained on all patients not on hemodialysis, and (\n                      <jats:italic toggle=\"yes\">4<\/jats:italic>\n                      ) pretrained on patients not on hemodialysis and fine-tuned on patients on hemodialysis. We assessed the ability of the models to classify left ventricular ejection fraction into clinically relevant categories of \u226440%, 41% to \u226450%, and &gt;50%. We compared performance by area under the receiver operating characteristic curve.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We extracted 705,075 electrocardiogram:echocardiogram pairs for 158,840 patients not on hemodialysis used for development of models 3 and 4 and\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =18,626 electrocardiogram:echocardiogram pairs for 2168 patients on hemodialysis for models 1, 2, and 4. The transfer learning model achieved area under the receiver operating characteristic curves of 0.86, 0.63, and 0.83 in predicting left ventricular ejection fraction categories of \u226440% (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =461), 41%\u201350% (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =398), and &gt;50% (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      =1309), respectively. For the same tasks, model 1 achieved area under the receiver operating characteristic curves of 0.74, 0.55, and 0.71, respectively; model 2 achieved area under the receiver operating characteristic curves of 0.71, 0.55, and 0.69, respectively, and model 3 achieved area under the receiver operating characteristic curves of 0.80, 0.51, and 0.77, respectively. We found that predictions of left ventricular ejection fraction by the transfer learning model were associated with mortality in a Cox regression with an adjusted hazard ratio of 1.29 (95% confidence interval, 1.04 to 1.59).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A deep learning model can determine left ventricular ejection fraction for patients on hemodialysis following pretraining on electrocardiograms of patients not on hemodialysis. Predictions of low ejection fraction from this model were associated with mortality over a 5-year follow-up period.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Podcast<\/jats:title>\n                    <jats:p>This article contains a podcast at https:\/\/www.asn-online.org\/media\/podcast\/CJASN\/2022_06_06_CJN16481221.mp3<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2215\/cjn.16481221","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T13:40:13Z","timestamp":1654522813000},"page":"1017-1025","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":9,"title":["Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis"],"prefix":"10.2215","volume":"17","author":[{"given":"Akhil","family":"Vaid","sequence":"first","affiliation":[{"name":"Mount Sinai Clinical 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