AI detection of deep surgical site infections after elective colorectal surgery
Official title: A Novel AI Algorithm With Enhanced Accuracy for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery. A Diagnostic Accuracy Study.
Run by Hospital de Granollers · NCT07130656 · Data updated 2026-11-12
It will test an AI algorithm that scans medical records to find surgical site infections in people who have planned colorectal surgery.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 1200 (estimated) |
| Sponsor | Hospital de Granollers Academic / other |
| Locations | 1 site (Granollers, Barcelona) |
| Status | Recruiting |
| Started | January 15, 2025 |
| Main results expected | August 30, 2025 |
| Study ends | October 30, 2025 |
| Registry record updated | August 24, 2025 |
| Trial ID | NCT07130656 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are people who undergo elective colorectal resection with no infection at the time of surgery and no prior intestinal stoma.
✕Probably not a fit
Patients who had emergency surgery, had an infection present at the time of operation, have a previous intestinal stoma, are treated outside the participating hospital, or lack adequate follow-up are unlikely to benefit.
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 used a cohort of elective colorectal resection patients previously screened within a nationwide healthcare-associated infection surveillance system to measure algorithm performance. The algorithm searches clinical notes, microbiology reports, diagnosis and procedure codes to detect surgical site infections at three anatomical levels. Surveillance covered the typical post-operative window (up to 30–90 days) to capture infections after discharge. Performance was compared to conventional manual surveillance using standard diagnostic criteria to report sensitivity, specificity, and predictive values.
Why it matters
Potential benefit: If successful, the algorithm could speed up surveillance, reduce manual workload for infection control teams, and help identify infections earlier to improve patient safety.
How similar studies have gone: Previous efforts using natural language processing and machine learning for SSI surveillance have shown promising but variable accuracy, so this approach builds on emerging but not yet universally proven methods.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Elective colorectal resection Exclusion Criteria: * Emergency surgery * Infection present at operation * Previous intestinal stoma
Where it's running
-
Hospital General de Granollers
Granollers, Barcelona, Spain Recruiting
Study contacts
- Study coordinator: Josep M Badia, MD, PhD
- Email: jmbadiaperez@gmail.com
- Phone: 670702099
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.