{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T13:34:39Z","timestamp":1786714479076,"version":"build-2736575974"},"reference-count":28,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cardiovasc electrophysiol"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Heart failure (HF) is a leading cause of hospitalization and readmission. Cardiac implantable electronic devices (CIEDs) continuously capture physiologic diagnostics that change during and after HF decompensation and may enable early post\u2010discharge risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To predict 60\u2010day HF readmission using deep learning models trained on post\u2010discharge temporal behavior of CIED diagnostic parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed a retrospective analysis of patients with implantable cardioverter defibrillators (ICDs) or cardiac resynchronization therapy defibrillators (CRT\u2010Ds) devices enrolled in remote monitoring (2007\u20132021) with HF hospitalizations identified from linked electronic health record and remote monitoring databases. Five daily device diagnostics (activity, impedance, heart rate variability, and night\/day heart rates) were aligned to the index HF discharge (day 0) and analyzed over the subsequent 30 days. Patients readmitted within 7 days and those with substantial missing device data were excluded; remaining recovery windows were labeled by the occurrence of HF readmission 8\u201360 days post\u2010discharge. We trained and evaluated two architectures of neural networks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The cohort included 5734 patients with 12\u2009369 HF hospitalization events; 2489 index hospitalizations were followed by HF readmission within 60 days. Across the 30\u2010day recovery window of device parameters, temporal characteristics of CIED cardiac compass parameters differed significantly between readmitted and non\u2010readmitted patients (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), consistent with slower physiologic recovery among readmitted events. On an independent data set, the 1\u2010dimensional convolutional neural network architecture (1D CNN) achieved 0.77 sensitivity, 0.98 specificity, and area under the ROC curve (AUROC) of 0.89.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning models leveraging post\u2010discharge trajectories of routinely captured CIED diagnostics can identify patients at higher risk of 60\u2010day HF readmission and may support targeted early follow\u2013up after HF hospitalization.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jce.70406","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T18:48:31Z","timestamp":1781549311000},"page":"1755-1764","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning\u2010Based Prediction of Heart Failure Readmission Using Longitudinal Cardiac Device Data"],"prefix":"10.1111","volume":"37","author":[{"given":"Veronica","family":"Ramos","sequence":"first","affiliation":[{"name":"Medtronic Inc. Mounds View Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shantanu","family":"Sarkar","sequence":"additional","affiliation":[{"name":"Medtronic Inc. Mounds View Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jodi","family":"Koehler","sequence":"additional","affiliation":[{"name":"Medtronic Inc. 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Mounds View Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Cho","sequence":"additional","affiliation":[{"name":"Medtronic Inc. Mounds View Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javed","family":"Butler","sequence":"additional","affiliation":[{"name":"Baylor Scott and White Research Institute Dallas Texas USA"},{"name":"University of Mississippi Jackson Mississippi USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander T.","family":"Sandhu","sequence":"additional","affiliation":[{"name":"Stanford University School of Medicine Stanford California USA"},{"name":"Veterans Affairs Palo Alto Health Care System Palo Alto California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tina","family":"Baykaner","sequence":"additional","affiliation":[{"name":"Stanford University School of Medicine Stanford California 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