Using machine learning to prevent lung complications after surgery

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to identify patients at high risk for postoperative pulmonary complications (POPC) using a tailored machine learning algorithm that analyzes perioperative clinical data and lung ultrasound images. By collecting data from 512 patients undergoing elective surgery with general anesthesia, the study seeks to detect POPC before they manifest clinically. The research builds on existing clinical scoring systems, enhancing their predictive quality through advanced machine learning techniques and non-invasive sonographic assessments.

Why it matters

Who can join: the full rules

Inclusion Criteria:

* adult patients
* elective, surgical procedure
* general anaesthesia

Exclusion Criteria:

* patients younger than 18 years of age
* outpatient surgery
* postoperative admission to intensive care unit

Where it's running

• University Hospital Ulm
  Ulm, Germany Recruiting

Study contacts

• Study coordinator: Britta Trautwein, MD
• Email: britta.trautwein@uniklinik-ulm.de
• Phone: 00731 500 60227

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.