{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T01:48:25Z","timestamp":1785462505590,"version":"3.56.0"},"reference-count":11,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:00:00Z","timestamp":1768521600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T00:00:00Z","timestamp":1769558400000},"content-version":"vor","delay-in-days":12,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"J\u00f6nk\u00f6ping University"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Induction of general anesthesia is often associated with significant hemodynamic changes, particularly in blood pressure (BP). These early postinduction fluctuations can vary widely among patients and contribute to perioperative complications. Current clinical approaches to managing postinduction BP changes are largely reactive and may not fully account for individual variability. This study aimed to identify distinct patterns of mean arterial pressure (MAP) response during the first 10\u00a0min following induction of general anesthesia, using a time-series clustering approach. We conducted a retrospective cohort study of 17,645 adult patients undergoing non-cardiac, non-obstetric inpatient surgery under general anesthesia at a tertiary medical center. BP was measured at 1 min intervals using either invasive arterial lines (8.3% of cases) or standard non-invasive oscillometric cuffs. An unsupervised X-means clustering algorithm with dynamic time warping was applied to identify recurring MAP trajectory patterns. Patient demographics, comorbidities, anesthetic drug doses, and other perioperative characteristics were compared across clusters. Five distinct MAP trajectories were identified: Initial Decline\u2014Plateau (31.8%), Gradual Moderate Decline (18.4%), Initial Decline\u2014Recovery (7.5%), Gradual Severe Decline (29.6%), and Initial Decline\u2014Low Plateau (12.7%). These patterns differed significantly in baseline MAP, comorbidity profiles and antihypertensive use, while differences in anesthetic agent doses were statistically but not clinically meaningful. Distinct postinduction BP trajectories were identified using a time-series clustering approach. These findings provide a framework for future validation in datasets with richer clinical context.<\/jats:p>","DOI":"10.1038\/s41598-025-33740-x","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T02:25:11Z","timestamp":1768530311000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A time-series clustering analysis of postinduction blood pressure trajectories"],"prefix":"10.1038","volume":"16","author":[{"given":"Maxim","family":"Glebov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maksim","family":"Katsin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haim","family":"Berkenstadt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina","family":"Orkin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yotam","family":"Portnoy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adi","family":"Schuchami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teddy","family":"Lazebnik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,1,16]]},"reference":[{"issue":"1","key":"33740_CR1","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1093\/bja\/aex127","volume":"119","author":"S S\u00fcdfeld","year":"2017","unstructured":"S\u00fcdfeld, S. et al. Post-induction hypotension and early intraoperative hypotension associated with general anaesthesia. Br J Anaesth. 119(1), 57\u201364. https:\/\/doi.org\/10.1093\/bja\/aex127 (2017).","journal-title":"Br J Anaesth."},{"issue":"3","key":"33740_CR2","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1097\/ALN.0b013e3182a10e26","volume":"119","author":"M Walsh","year":"2013","unstructured":"Walsh, M. et al. Relationship between intraoperative mean arterial pressure and clinical outcomes after noncardiac surgery: Toward an empirical definition of hypotension. Anesthesiology 119(3), 507\u2013515. https:\/\/doi.org\/10.1097\/ALN.0b013e3182a10e26 (2013).","journal-title":"Anesthesiology"},{"issue":"3","key":"33740_CR3","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1097\/ALN.0b013e3181979440","volume":"110","author":"S Kheterpal","year":"2009","unstructured":"Kheterpal, S. et al. Development and validation of an acute kidney injury risk index for patients undergoing general surgery: Results from a national data set. Anesthesiology 110(3), 505\u2013515. https:\/\/doi.org\/10.1097\/ALN.0b013e3181979440 (2009).","journal-title":"Anesthesiology"},{"issue":"2","key":"33740_CR4","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1097\/ALN.0000000000003610","volume":"134","author":"B Saugel","year":"2021","unstructured":"Saugel, B. & Sessler, D. I. Perioperative blood pressure management. Anesthesiology 134(2), 250\u2013261. https:\/\/doi.org\/10.1097\/ALN.0000000000003610 (2021).","journal-title":"Anesthesiology"},{"issue":"3","key":"33740_CR5","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/s00540-023-03191-7","volume":"37","author":"S Czajka","year":"2023","unstructured":"Czajka, S., Putowski, Z. & Krzych, \u0141J. Post-induction hypotension and intraoperative hypotension as potential separate risk factors for the adverse outcome: A cohort study. J Anesth. 37(3), 442\u2013450. https:\/\/doi.org\/10.1007\/s00540-023-03191-7 (2023).","journal-title":"J Anesth."},{"key":"33740_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-22475-2","author":"M Berry","year":"2020","unstructured":"Berry, M. & Mohamed, A. Supervised and unsupervised learning for data science. https:\/\/doi.org\/10.1007\/978-3-030-22475-2 (2020).","journal-title":"Supervised and Unsupervised Learning for Data Science."},{"key":"33740_CR7","unstructured":"Tavenard X, Faouzi J, Vandewiele G, Divo F, Androz G, Holtz C, Payne M, Yurchak R, Russwurm M, Kolar K, Woods E. Tslearn, A machine learning toolkit for time series data. J. Mach. Learn. Res. (2020)."},{"key":"33740_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2016.05.040","volume":"367\u2013368","author":"X Huang","year":"2016","unstructured":"Huang, X. et al. Time series k-means: A new k-means type smooth subspace clustering for time series data. Inf. Sci. 367\u2013368, 1\u201313. https:\/\/doi.org\/10.1016\/j.ins.2016.05.040 (2016).","journal-title":"Inf. Sci."},{"key":"33740_CR9","unstructured":"Pelleg D, Moore AW. X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers Inc.; 2000:727\u2013734."},{"key":"33740_CR10","doi-asserted-by":"publisher","unstructured":"Neath A, Cavanaugh J. The Bayesian information criterion: background, derivation, and applications. *WIREs Comput Stat*. 2011;4(2):199\u2013203. https:\/\/doi.org\/10.1002\/wics.199","DOI":"10.1002\/wics.199"},{"key":"33740_CR11","doi-asserted-by":"publisher","unstructured":"Thomsen KK, Kr\u00f6ker A, Krause L, et al. A bundle to prevent postinduction hypotension in high-risk noncardiac surgery patients: the Zero-hypotension single-arm interventional proof-of-concept study. BJA Open. 2025;14:100392. Published 2025 Apr 11. https:\/\/doi.org\/10.1016\/j.bjao.2025.100392","DOI":"10.1016\/j.bjao.2025.100392"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-33740-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-33740-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-33740-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:02:46Z","timestamp":1769637766000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-33740-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,16]]},"references-count":11,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["33740"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-33740-x","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,16]]},"assertion":[{"value":"4 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflicts of interest related to this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"During the preparation of this work, the authors used \u201cChatGPT\u201d (by OpenAI) for text and grammar correction. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Constent for publication"}}],"article-number":"3745"}}