{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T04:57:42Z","timestamp":1781153862089,"version":"3.54.1"},"reference-count":36,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:00:00Z","timestamp":1773100800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82170685"],"award-info":[{"award-number":["82170685"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82502291"],"award-info":[{"award-number":["82502291"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U24A20666"],"award-info":[{"award-number":["U24A20666"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017950","name":"Shanghai Municipal Health Commission","doi-asserted-by":"publisher","award":["2025BZ019"],"award-info":[{"award-number":["2025BZ019"]}],"id":[{"id":"10.13039\/100017950","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":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Major adverse cardiovascular events (MACE) are a leading cause of morbidity and mortality in patients with end\u2010stage renal disease (ESRD). However, risk stratification and prognostic prediction remain limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To assess the incremental prognostic value of combined left atrial (LA) and left ventricular (LV) strain in predicting MACE among ESRD patients receiving renal replacement therapy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Prospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>Three hundred thirteen ESRD patients (mean age: 53.8\u2009\u00b1\u200914.0\u2009years; 202 males) undergoing maintenance dialysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>Balanced steady\u2010state free precession (bSSFP) cine sequence at 3.0\u2009T.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Myocardial strain was analyzed from bSSFP cine images using feature\u2010tracking software (CVI42). LA strain components were reservoir (LARS), conduit (LAScd), and contractile (LASct) strain, and LV strain included global longitudinal (GLS), radial (GRS), and circumferential (GCS) strain. Patients were followed up via clinical records and MACE were documented. Prognostic models were constructed using multivariable Cox proportional hazards regression. The baseline prediction model of conventional cardiovascular risk factors was then compared with models incorporating LARS and GLS to assess incremental prognostic value.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Cox proportional hazards regression identified predictors of MACE, and model performance was evaluated using C\u2010index, Akaike and Bayesian information criteria (AIC\/BIC), and Kaplan\u2013Meier analysis.\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05 was considered significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      During a median follow\u2010up of 16.93\u2009months, 61 patients developed MACE. LARS (hazard ratio (HR) 0.90, 95% confidence interval (CI) 0.87\u20130.94) and LV GLS (HR 1.21, 95% CI 1.08\u20131.36) were independent predictors. The Cox model incorporating both LARS and LV GLS showed improved discrimination compared with the clinical risk factor model (C\u2010index 0.79 vs. 0.70). Stratification by both LA and LV strain markers significantly improved MACE prediction (log\u2010rank:\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>The integration of LA and LV strain offered superior prognostic value for MACE prediction in ESRD patients, enabling refined risk stratification beyond traditional measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 3.<\/jats:p>\n                  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