{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T19:38:39Z","timestamp":1792093119843,"version":"4.4.0"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Cardiovascular Imaging"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Stress perfusion cardiovascular magnetic resonance (CMR) is widely used to detect myocardial ischemia, mostly through visual assessment. Recent studies suggest that strain imaging at rest and during stress can also help in prognostic stratification. However, the additional prognostic value of combining both rest and stress strain imaging has not been fully established. This study examined the incremental benefit of combining these strain measures with traditional risk prognosticators and CMR findings to predict major adverse clinical events (MACE) in a cohort of consecutive patients referred for stress CMR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>This retrospective, single-center observational study included all consecutive patients with known or suspected coronary artery disease referred for stress CMR between 2016 and 2018. Fully automated machine learning was used to obtain global longitudinal strain at rest (rest-GLS) and global circumferential strain at stress (stress-GCS). The primary outcome was MACE, including cardiovascular death or hospitalization for heart failure. Cox models were used to assess the incremental prognostic value of combining these strain features with traditional prognosticators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Of 2778 patients (age 65\u00b112 years, 68% men), 96% had feasible, fully automated rest-GLS and stress-GCS measurements. After a median follow-up of 5.2 (4.8\u20135.5) years, 316 (11.1%) patients experienced MACE. After adjustment for traditional prognosticators, both rest-GLS (hazard ratio, 1.09 [95% CI, 1.05\u20131.13];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001) and stress-GCS (hazard ratio, 1.08 [95% CI, 1.03\u20131.12];\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001) were independently associated with MACE. The best cutoffs for MACE prediction were &gt;\u201310% for rest-GLS and stress-GCS, with a C-index improvement of 0.02, continuous net reclassification improvement of 15.6%, and integrative discrimination index of 2.2% (all\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>The combination of rest-GLS and stress-GCS, with a cutoff of &gt;\u201310% provided an incremental prognostic value over and above traditional prognosticators, including CMR parameters, for predicting MACE in patients undergoing stress CMR.<\/jats:p>\n                  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Inserm MASCOT \u2013 UMRS 942, France (A.S.A., T.G., J.F., A.U., T.P., S.T.)."},{"name":"MIRACL.ai Laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere (AP-HP), Paris, France (A.S.A., J.G., T.G., J.F., A.U., T.P., S.T.)."},{"name":"Institut Cardiovasculaire Paris Sud, Cardiovascular Magnetic Resonance Laboratory, Ho\u0302pital Prive\u0301 Jacques CARTIER, Ramsay Sante\u0301, Massy, France (A.S.A., J.G., S.D., T.H., S.C., T.U., P.G., M.A., T.G., J.F., A.U., F.S., T.P.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3714-1256","authenticated-orcid":true,"given":"Je\u0301ro\u0302me","family":"Garot","sequence":"additional","affiliation":[{"name":"MIRACL.ai Laboratory, Multimodality Imaging for Research and Analysis Core Laboratory and Artificial Intelligence, University Hospital of Lariboisiere (AP-HP), Paris, France (A.S.A., J.G., T.G., J.F., A.U., 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