{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T02:07:44Z","timestamp":1786586864493,"version":"build-2736575974"},"reference-count":20,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":344,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2024,10,27]],"date-time":"2024-10-27T00:00:00Z","timestamp":1729987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Hebei Province Health Commission for Medical Scientific Research","award":["20240192"],"award-info":[{"award-number":["20240192"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Heart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>A cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network-long short-term memory (CNN-LSTM) DLM was employed. HFpEF risk was determined by left ventricular end-diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP &amp;gt; 12 mmHg) and low risk for HFpEF (LVEDP \u2264 12 mmHg). The model was trained on Cohort A and prospectively validated on Cohort B.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM-identified high-risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%\u201311.86%, P &amp;lt; 0.01), higher BMI indices (25.92\u201324.22 kg\/cm2, P &amp;lt; 0.01), and lower usage history of calcium channel blockers (CCB) (11.76%\u201328.81%, P &amp;lt; 0.01) compared with the DLM-identified low-risk HFpEF group. Traditional HFpEF indicators, including B-type natriuretic peptide (BNP) (22\u201320 pg\/mL, P = 0.71) and E\/E\u2032 (8.25\u20138.5, P = 0.66), did not exhibit significant differences between the two groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DLM offers an accurate, cost-effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ehf2.15120","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T02:16:47Z","timestamp":1730081807000},"page":"631-639","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Electrocardiograph Analysis for Risk Assessment of Heart Failure with Preserved Ejection Fraction: A Deep Learning Model"],"prefix":"10.1093","volume":"12","author":[{"given":"Zheng","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Cardiology, The First Hospital of Hebei Medical University , Shijiazhuang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The First Hospital of Hebei Medical University , Shijiazhuang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Cangzhou Central Hospital , Cangzhou ,","place":["China"]},{"name":"Diagnostic Department, Faculty of Medicine, Cangzhou Medical College , Cangzhou ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The First Hospital of Hebei Medical University , Shijiazhuang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5323-6733","authenticated-orcid":false,"given":"Chao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The First Hospital of Hebei Medical University , Shijiazhuang ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,27]]},"reference":[{"key":"2026012113061872800_ehf215120-bib-0001","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s10741-023-10352-z","article-title":"Myofilament dysfunction in diastolic heart failure","volume":"29","author":"Aboonabi","year":"2023","journal-title":"Heart Fail Rev"},{"key":"2026012113061872800_ehf215120-bib-0002","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis","year":"2021","journal-title":"Nat Rev Cardiol"},{"key":"2026012113061872800_ehf215120-bib-0003","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1016\/j.jacc.2022.05.034","article-title":"The future of AI-enhanced ECG interpretation for valvular heart disease screening","volume":"80","author":"Pandey","year":"2022","journal-title":"J Am Coll Cardiol"},{"key":"2026012113061872800_ehf215120-bib-0004","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1093\/ehjdh\/ztaa015","article-title":"Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features","volume":"2","author":"Kwon","year":"2021","journal-title":"Eur Heart J Digit Health"},{"key":"2026012113061872800_ehf215120-bib-0005","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/ehf2.14562","article-title":"Advances in device-based treatment of heart failure with preserved ejection fraction: evidence from clinical trials. 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