[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100499953":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":12,"locations":22,"responsibleParty":43,"collaborators":10,"id":46,"slug":47,"hasResults":48,"nctId":49,"briefTitle":50,"officialTitle":50,"acronym":51,"eligibilityCriteria":52,"healthyVolunteers":48,"sex":53,"minAge":54,"maxAge":10,"enrollmentInfo":55,"targetDuration":10,"studyType":58,"phases":10,"briefSummary":59,"conditions":60,"keywords":62,"overallStatus":24,"whyStopped":10,"lastUpdateSubmitDate":65,"lastUpdatePostDateStruct":66,"startDateStruct":69,"completionDateStruct":71,"leadSponsor":73,"locationsCount":74},{"fullName":5,"class":6},"University Hospital Ulm","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"Development of the machine learning model",null,"Perioperative clinical routine data are going to be assessed as per standard. Postoperatively, a standardized lung sonography is going to be performed in the recovery room. Patients will then be visited on the ward on postoperative day 1, 3 and 7 for clinical examination to detect postoperative pulmonary complications according to the criteria elaborated by the StEP- collaboration.",[13,18],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},"Britta Trautwein, MD","CONTACT","00731 500 60227","britta.trautwein@uniklinik-ulm.de",{"name":19,"role":15,"phone":20,"phoneExt":10,"email":21},"Simone Kagerbauer, PD","00731 500 60254","simone.kagerbauer@uni-ulm.de",[23],{"facility":5,"status":24,"city":25,"state":10,"zip":26,"country":27,"countryCode":28,"cosmosGeoPoint":29,"geoPoint":34,"contacts":35},"RECRUITING","Ulm","89081","Germany","DE",{"type":30,"coordinates":31},"Point",[32,33],9.99155,48.39841,{"lat":33,"lon":32},[36,39],{"name":37,"role":15,"phone":38,"phoneExt":10,"email":17},"Britta Trautwein","004973150060227",{"name":40,"role":15,"phone":41,"phoneExt":10,"email":42},"Simone Kagerbauer","004973150060254","simone.kagerbauer@uniklinik-ulm.de",{"type":44,"investigatorFullName":37,"investigatorTitle":45,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"SPONSOR_INVESTIGATOR","Resident doctor","100499953","preventing-postoperative-pulmonary-complications-by-establishing-a-machine-learning-assisted-approach-100499953",false,"NCT05789953","PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach","PEPPERMINT","Inclusion Criteria:\n\n* adult patients\n* elective, surgical procedure\n* general anaesthesia\n\nExclusion Criteria:\n\n* patients younger than 18 years of age\n* outpatient surgery\n* postoperative admission to intensive care unit","ALL","18 Years",{"count":56,"type":57},512,"ESTIMATED","OBSERVATIONAL","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. The aim of the planned research project is therefore to enable reliable identification of high-risk patients on the basis of a tailored machine learning algorithm using perioperative clinical routine data and sonographic imaging data collected in the recovery room. The randomized clinical trial will include 512 patients undergoing elective surgery in general anaesthesia. The primary outcome will be the development of POPC. The goal of the study is to detect postoperative pulmonary complications before they become clinically manifest.",[61],"Postoperative Pulmonary Complications",[63,64],"machine learning","lung ultrasound","2026-05-04",{"date":67,"type":68},"2026-05-08","ACTUAL",{"date":70,"type":68},"2023-04-25",{"date":72,"type":57},"2026-12",{"name":37,"class":6},1]