{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T22:00:20Z","timestamp":1792620020132,"version":"4.5.0"},"reference-count":32,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"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>Background<\/jats:title>\n<jats:p>Postoperative pulmonary complications (POPC) are common after general anaesthesia and are a major cause of increased morbidity and mortality in surgical patients. However, prevention and treatment methods for POPC that are considered effective tie up human and technical resources. Therefore, the planned research project aims to create a prediction model that enables the reliable identification of high-risk patients immediately after surgery based on a tailored machine learning algorithm.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec002\">\n<jats:title>Methods<\/jats:title>\n<jats:p>This clinical cohort study will follow the TRIPOD statement for multivariable prediction model development. Development of the prognostic model will require 512 patients undergoing elective surgery under general anaesthesia. Besides the collection of perioperative routine data, standardised lung sonography will be performed postoperatively in the recovery room on each patient. During the postoperative course, patients will be examined in a structured manner on postoperative days 1,3 and 7 to detect POPC. The endpoints determined in this way, together with the clinical and imaging data collected, are then used to train a machine learning model based on neural networks and ensemble methods to predict POPC in the early postoperative phase.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec003\">\n<jats:title>Discussion<\/jats:title>\n<jats:p>In the perioperative setting, detecting POPC before they become clinically manifest is desirable. This would ensure optimal patient care and resource allocation and help initiate adequate patient treatment after being transferred from the recovery room to the ward. A reliable prediction algorithm based on machine learning holds great potential to improve postoperative outcomes.<\/jats:p>\n<\/jats:sec>\n<jats:sec id=\"sec004\">\n<jats:title>Trial registration<\/jats:title>\n<jats:p>ClinicalTrials.gov ID: NCT05789953 (29th of March 2023)<\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0329076","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T17:40:48Z","timestamp":1755625248000},"page":"e0329076","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Preventing postoperative pulmonary complications by establishing a machine-learning assisted approach (PEPPERMINT): Study protocol for the creation of a risk prediction model"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-1894-4751","authenticated-orcid":true,"given":"Britta","family":"Trautwein","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meinrad","family":"Beer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manfred","family":"Blobner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bettina","family":"Jungwirth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone Maria","family":"Kagerbauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0984-224X","authenticated-orcid":true,"given":"Michael","family":"G\u00f6tz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"issue":"4","key":"pone.0329076.ref001","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.bja.2017.12.037","article-title":"A systematic review and consensus definitions for standardised end-points in perioperative medicine: pulmonary complications","volume":"120","author":"TEF Abbott","year":"2018","journal-title":"Br J Anaesth"},{"issue":"2","key":"pone.0329076.ref002","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1001\/jamasurg.2016.4065","article-title":"Postoperative Pulmonary Complications, Early Mortality, and Hospital Stay Following Noncardiothoracic Surgery: A Multicenter Study by the Perioperative Research Network Investigators","volume":"152","author":"A Fernandez-Bustamante","year":"2017","journal-title":"JAMA Surg"},{"issue":"14","key":"pone.0329076.ref003","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1056\/NEJMsa0903048","article-title":"Variation in hospital mortality associated with inpatient surgery","volume":"361","author":"AA Ghaferi","year":"2009","journal-title":"N Engl J Med"},{"issue":"3","key":"pone.0329076.ref004","first-page":"265","article-title":"Predictive scores for postoperative pulmonary complications: time to move towards clinical practice","volume":"82","author":"L Ball","year":"2016","journal-title":"Minerva Anestesiol"},{"issue":"1","key":"pone.0329076.ref005","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/s13741-021-00214-3","article-title":"Do ARISCAT scores help to predict the incidence of postoperative pulmonary complications in elderly patients after upper abdominal surgery? 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