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

NCT05789953 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 512

Last updated 2026-05-08

No results posted yet for this study

Summary

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.

Conditions

  • Postoperative Pulmonary Complications

Sponsors & Collaborators

  • Britta Trautwein

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-04-25
Primary Completion
2026-09-30
Completion
2026-12-31

Countries

  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05789953 on ClinicalTrials.gov