{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T05:07:31Z","timestamp":1783832851652,"version":"3.55.0"},"reference-count":26,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2022D01C840"],"award-info":[{"award-number":["2022D01C840"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,8]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Accurate risk stratification in dilated cardiomyopathy (DCM) is challenging. Conventional markers like left ventricular ejection fraction (LVEF) and late gadolinium enhancement (LGE) fail to fully capture the pathophysiological interplay between left atrial (LA) and left ventricular (LV) remodeling.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To determine the prognostic value of a cardiac MRI\u2010derived left atrioventricular coupling index (LACI) to predict adverse outcomes in DCM and assess its incremental utility beyond conventional markers.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n \n 403 patients with DCM from two centers, randomly split into a training set (\n n<\/jats:italic>\n \u2009=\u2009282, 70%) for model development and an internal validation set (\n n<\/jats:italic>\n \u2009=\u2009121, 30%).\n <\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n Balanced steady\u2010state free precession cine and inversion\u2010recovery prepared fast gradient\u2010echo (LGE) sequences at 1.5T and 3.0T.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n LACI was quantified as (LA minimal volume\/LV end\u2010diastolic volume)\u2009\u00d7\u2009100% from routine cine images. LA\/LV volumes, function, LGE, and global longitudinal strain (GLS) were assessed by blinded radiologists. The primary endpoint was a composite of all\u2010cause mortality or heart failure hospitalization.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Student's\n t<\/jats:italic>\n \u2010test, Mann\u2013Whitney U test, chi\u2010square test, Spearman's correlation, Cox regression, Kaplan\u2013Meier analysis with log\u2010rank test, receiver operating characteristic analysis, C\u2010index, net reclassification improvement (NRI). A two\u2010sided\n p<\/jats:italic>\n value <\u20090.05 was significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Over a median 43.6\u2010month follow\u2010up, 97 patients (24.1%) reached the primary endpoint. In the training set, elevated LACI (\u2265\u200945.75%) was the strongest independent predictor (adjusted hazard ratio [HR]\u2009=\u20093.485, 95% CI 1.821\u20136.667). The LACI\u2010enhanced model (including NYHA class, LGE, GLS) achieved a C\u2010index of 0.804 (95% CI 0.774\u20130.835) in the training set, which remained robust in the validation set (C\u2010index\u2009=\u20090.824, 95% CI 0.751\u20130.897). Adding LACI to the baseline model significantly improved reclassification (continuous NRI\u2009=\u20090.102, 95% CI 0.043\u20130.161).<\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n LACI derived from routine cine images is strongly and independently associated with adverse outcomes in DCM and provides significant incremental prognostic value beyond established markers, supporting its potential as a practical risk stratification tool.<\/jats:p>\n <\/jats:sec>\n \n Evidence Level<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 5.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70326","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T06:50:00Z","timestamp":1779951000000},"page":"550-562","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prognostic Value of a Cardiac Magnetic Resonance\u2010Derived Left Atrioventricular Coupling Index From Routine Cine Images in Dilated Cardiomyopathy: A Multicenter Study"],"prefix":"10.1002","volume":"64","author":[{"given":"Yu\u2010Jie","family":"Jiang","sequence":"first","affiliation":[{"name":"Medical Imaging & Radiology Center, People's Hospital of Xinjiang Uygur Autonomous Region Xinjiang China"},{"name":"Xinjiang Key Laboratory of Cardiovascular Homeostasis and Regeneration Research Xinjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao\u2010Chao","family":"Hou","sequence":"additional","affiliation":[{"name":"Anesthesia and Surgery Center, People's Hospital of Xinjiang Uygur Autonomous Region, Xinjiang Clinical Research Center for Anesthesia Management Xinjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Tao","sequence":"additional","affiliation":[{"name":"Xinjiang Key Laboratory of Cardiovascular Homeostasis and Regeneration Research Xinjiang China"},{"name":"Department of Cardiology People's 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