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Study identifier: NCT05789953 Synced from ClinicalTrials.gov · July 28, 2026
● Recruiting

PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach

Condition: Postoperative Pulmonary Complications  ·  Sponsor: Britta Trautwein

PhaseN/A
Planned participants512
Who can joinAll sexes, 18 Years to no upper limit
Healthy volunteersNo

About this study

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.

This description comes directly from the study's public registry record.

Talk to the study team

Britta Trautwein, MD  ·  00731 500 60227  ·  britta.trautwein@uniklinik-ulm.de

Simone Kagerbauer, PD  ·  00731 500 60254  ·  simone.kagerbauer@uni-ulm.de

Always discuss trial participation with your own doctor first.

Locations (1)

University Hospital UlmUlm, GermanyRecruiting

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Source record: clinicaltrials.gov/study/NCT05789953