{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:03:16Z","timestamp":1774983796191,"version":"3.50.1"},"reference-count":33,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circ: Population Health and Outcomes"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Mental stress-induced myocardial ischemia is often clinically silent and associated with increased cardiovascular risk, particularly in women. Conventional ECG-based detection is limited, while the reference standard, positron emission tomography\/computed tomography, is costly and less accessible. We aimed to characterize mental stress-induced myocardial ischemia using multistage ECG analysis and machine learning to improve early, noninvasive detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We enrolled 2 female cohorts (18\u201375 years) from a single-center tertiary hospital in Guangzhou, China: a study cohort (2020\u20132021) and an independent test cohort (2022\u20132023). Participants included women with angina, nonobstructive coronary artery disease, and age-matched healthy controls. Multistage ECGs were recorded during 3 mental stress tasks and segmented into Rest, Stress, and Recovery phases. A total of 88 interpretable ECG variables, including morphological indices and heart rate variability, were extracted from each stage, and interstage differences were then calculated. Mental stress-induced myocardial ischemia labeling was obtained from positron emission tomography\/computed tomography. Machine learning classifiers (K-nearest neighbors, logistic regression, random forest, support vector machine, extreme gradient boosting) were trained on the study cohort and evaluated in the test cohort using single-stage, multistage, and interstage features.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      The study cohort (n=119, mean age 53\u00b18 years) showed significant heart rate variability, heart rate asymmetry, and prognostic-related changes, with 103 of 264 intrastage features differing across Rest, Stress, and Recovery (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt;0.05). Interstage differences provided complementary information, with 73 of 264 interstage features showing significant group differences. In the test cohort (n=53, mean age 53\u00b17 years), models using multistage and interstage features all outperformed single-stage models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>Multistage ECG analysis, particularly heart rate variability, captures electrophysiological signatures of mental stress-induced myocardial ischemia and enhances noninvasive detection. These findings support a paradigm shift from single-phase, ischemia-focused ECG interpretation to dynamic, multivariable assessment, providing a foundation for early identification and personalized risk stratification in women with nonobstructive coronary disease.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/circoutcomes.125.012750","type":"journal-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T10:00:12Z","timestamp":1771236012000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":1,"title":["Multistage Electrocardiographic Profiling for Mental Stress-Induced Myocardial Ischemia in Women: A Prospective, Age-Matched Cohort Study in China"],"prefix":"10.1161","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0187-9991","authenticated-orcid":true,"given":"Xiaoting","family":"Peng","sequence":"first","affiliation":[{"name":"School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China (X.P., H. Li, H. Liang)."},{"name":"Medical Big Data Center, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (X.P., H. Li, Y.N., S.H., H. Liang, D.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0863-9207","authenticated-orcid":true,"given":"Chao","family":"Wu","sequence":"additional","affiliation":[{"name":"Stanley and Judith Frankel Institute for Heart and Brain Health, University of Michigan Medical Center, Ann Arbor (C.W.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5722-8602","authenticated-orcid":true,"given":"Huixian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China (X.P., H. Li, H. Liang)."},{"name":"Medical Big Data Center, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (X.P., H. Li, Y.N., S.H., H. Liang, D.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4573-3809","authenticated-orcid":true,"given":"Lianting","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan Children\u2019s Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science &amp; Technology, Hunan Province, China (L.H.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8507-8533","authenticated-orcid":true,"given":"Yilin","family":"Niu","sequence":"additional","affiliation":[{"name":"Medical Big Data Center, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (X.P., H. Li, Y.N., S.H., H. Liang, D.L.)."},{"name":"JC School of Public Health and Primary Care, The Chinese University of Hong Kong, China (Y.N.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software Engineering, South China University of Technology, Guangzhou, Guangdong Province, China (Y.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0387-8404","authenticated-orcid":true,"given":"Shuai","family":"Huang","sequence":"additional","affiliation":[{"name":"Medical Big Data Center, Guangdong Provincial People\u2019s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China (X.P., H. Li, Y.N., S.H., H. Liang, D.L.)."},{"name":"Guangdong Provincial Cardiovascular Institute, Guangzhou, China (S.H., H.M., D.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Scientific Instrumentation, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, PR China (X.Z.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0388-5540","authenticated-orcid":true,"given":"Entao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Guangdong Provincial People\u2019s Hospital, Guangzhou, China (E.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4150-9201","authenticated-orcid":true,"given":"Huan","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Cardiovascular Institute, Guangzhou, China (S.H., H.M., D.L.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingshan","family":"Geng","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Shenzhen People\u2019s Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Guangdong Province, China (Q.G.)."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiying","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Public Health, Southern Medical University, Guangzhou, Guangdong Province, China (X.P., H. 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