Using machine learning to prevent lung complications after surgery
PrEventing PostoPERative Pulmonary Complications by Establishing a MachINe-learning assisTed Approach
This study is testing a new way to use machine learning and lung ultrasound images to find out which patients are at high risk for lung problems after surgery.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 512 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | University Hospital Ulm Academic / other |
| Locations | 1 site (Ulm) |
| Trial ID | NCT05789953 on ClinicalTrials.gov |
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.
Who should consider this trial
Good fit: Ideal candidates for this study are adult patients undergoing elective surgical procedures that require general anesthesia.
Not a fit: Patients who are younger than 18 years, undergoing outpatient surgery, or requiring immediate postoperative admission to intensive care may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could significantly reduce the incidence of postoperative pulmonary complications, improving patient outcomes and reducing healthcare costs.
How similar studies have performed: While machine learning applications in medical diagnostics are gaining traction, this specific approach to predicting postoperative pulmonary complications is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion criteria
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 this trial is running
Ulm
- University Hospital Ulm — Ulm, Germany (Recruiting)
Study contacts
- Study coordinator: Britta Trautwein, MD
- Email: britta.trautwein@uniklinik-ulm.de
- Phone: 00731 500 60227
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.