{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T22:55:24Z","timestamp":1787871324058,"version":"build-2784847793"},"reference-count":20,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T00:00:00Z","timestamp":1726185600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\">\n<jats:title>Objective<\/jats:title>\n<jats:p>Mean arterial pressure is widely used as the variable to monitor during anesthesia. But there are many other variables proposed to define intraoperative arterial hypotension. The goal of the present study was to search arterial pressure variables linked with prolonged postoperative length of stay (pLOS).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Design<\/jats:title>\n<jats:p>Retrospective cohort study of adult patients having received general anesthesia for a scheduled non-cardiac surgical procedure between 15<jats:sup>th<\/jats:sup> July 2017 and 31st December 2019.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Methods<\/jats:title>\n<jats:p>pLOS was defined as a stay longer than the median (main outcome), adjusted for surgery type and duration. 330 arterial pressure variables were analyzed and organized through a clustering approach. An unsupervised hierarchical aggregation method for optimal cluster determination, employing Kendall\u2019s tau coefficients and a penalized Bayes information criterion was used. Variables were ranked using the absolute standardized mean distance (aSMD) to measure their effect on pLOS. Finally, after multivariate independence analysis, the number of variables was reduced to three.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Results<\/jats:title>\n<jats:p>Our study examined 9,516 patients. When LOS is defined as strictly greater than the median, 34% of patients experienced pLOS. Key arterial pressure variables linked with this definition of pLOS included the difference between the highest and lowest pulse pressure values computed throughout the surgery (aSMD[95%CI] = 0.39[0.31\u20130.40], p&lt;0.001), the accumulated time pulse pressure above 61mmHg (aSMD = 0.21[0.17\u20130.25], p&lt;0.001), and the lowest MAP during surgery (aSMD = 0.20[0.16\u20130.24], p&lt;0.001).<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec005\">\n<jats:title>Conclusions<\/jats:title>\n<jats:p>By applying a clustering approach, three arterial pressure variables were associated with pLOS. This scalable method can be applied to various dichotomized outcomes.<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0308910","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T17:51:32Z","timestamp":1726249892000},"page":"e0308910","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deciphering the explanatory potential of blood pressure variables on post-operative length of stay through hierarchical clustering: A retrospective monocentric study"],"prefix":"10.1371","volume":"19","author":[{"given":"J\u00e9r\u00f4me","family":"Cartailler","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor","family":"Beaucote","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2887-3822","authenticated-orcid":true,"given":"Bernard","family":"Trillat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Etienne","family":"Gayat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6789-1237","authenticated-orcid":true,"given":"Morgan","family":"Le Guen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandre","family":"Vallee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0729-5430","authenticated-orcid":true,"given":"Marc","family":"Fischler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2024,9,13]]},"reference":[{"key":"pone.0308910.ref001","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1093\/bja\/aeu458","article-title":"Impact of intraoperative hypotension and blood pressure fluctuations on early postoperative delirium after non-cardiac surgery","volume":"115","author":"J Hirsch","year":"2015","journal-title":"Br J Anaesth"},{"key":"pone.0308910.ref002","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.bja.2018.04.036","article-title":"Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review","volume":"121","author":"EM Wesselink","year":"2018","journal-title":"Br J Anaesth"},{"key":"pone.0308910.ref003","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1097\/01.anes.0000270724.40897.8e","article-title":"Incidence of intraoperative hypotension as a function of the chosen definition: literature definitions applied to a retrospective cohort using automated data collection","volume":"107","author":"JB Bijker","year":"2007","journal-title":"Anesthesiology"},{"key":"pone.0308910.ref004","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1097\/ALN.0b013e31825683dc","article-title":"Hospital stay and mortality are increased in patients having a \"triple low\" of low blood pressure, low bispectral index, and low minimum alveolar concentration of volatile anesthesia.","volume":"116","author":"DI Sessler","year":"2012","journal-title":"Anesthesiology"},{"key":"pone.0308910.ref005","first-page":"51","article-title":"Data structures for statistical computing in Python.","volume":"445","author":"W. 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