Ignite Creation Date:
2025-12-24 @ 11:35 PM
Ignite Modification Date:
2026-01-01 @ 1:21 AM
Study NCT ID:
NCT07130656
Status:
RECRUITING
Last Update Posted:
2025-08-24
First Post:
2025-08-12
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
AI Algorithm for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery.
Sponsor:
Hospital de Granollers
Organization:
Study Overview
Official Title:
A Novel AI Algorithm With Enhanced Accuracy for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery. A Diagnostic Accuracy Study.
Status:
RECRUITING
Status Verified Date:
2025-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
Infect-IA-2
Brief Summary:
Epidemiological surveillance is one of the eight core components of the World Health Organization Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI).
At present, for SSI surveillance, infection control teams perform a manual time-consuming work, which could make a transition to automated surveillance leveraging the new information technology.
The aim of this study was to evaluate the performance of a novel algorithm to detect SSI in a cohort of elective colorectal surgery patients who have been previously screened within a nationwide healthcare-associated infection surveillance system.
At present, for SSI surveillance, infection control teams perform a manual time-consuming work, which could make a transition to automated surveillance leveraging the new information technology.
The aim of this study was to evaluate the performance of a novel algorithm to detect SSI in a cohort of elective colorectal surgery patients who have been previously screened within a nationwide healthcare-associated infection surveillance system.
Detailed Description:
Healthcare-associated infections (HAIs) have a negative impact on patient health, represent a significant healthcare and economic burden on healthcare systems and are considered the most preventable cause of serious adverse events in hospitalised patients.
Epidemiological surveillance is one of the eight core components of the World Health Organization (WHO) Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI), which have proven to be effective in all types of surgery and in a variety of settings.
For a programme to be effective, surveillance for HCAIs must be active, prospective and continuous, comprising a surveillance period up to 30-90 days post-intervention, to cover the high rate of SSIs detected after discharge.
At present, infection control teams perform a manual, prospective, time-consuming and almost artisanal work, which should make a transition to automated or semi-automated surveillance that leverages the possibilities offered by today\'s information technology.
The evolution of surveillance systems should benefit from this new possibilities offered by artificial intelligence, allowing automated detection of suspected SSI adverse events from clinical course text, microbiology reports or coding of diagnoses, procedures, complications and readmissions.
The aim of this study was to evaluate the performance of a novel algorithm to detect to detect SSI at its three anatomical levels, in a cohort of elective colorectal surgery patients who have been previously screened within a nationwide healthcare-associated infection surveillance system.
Epidemiological surveillance is one of the eight core components of the World Health Organization (WHO) Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI), which have proven to be effective in all types of surgery and in a variety of settings.
For a programme to be effective, surveillance for HCAIs must be active, prospective and continuous, comprising a surveillance period up to 30-90 days post-intervention, to cover the high rate of SSIs detected after discharge.
At present, infection control teams perform a manual, prospective, time-consuming and almost artisanal work, which should make a transition to automated or semi-automated surveillance that leverages the possibilities offered by today\'s information technology.
The evolution of surveillance systems should benefit from this new possibilities offered by artificial intelligence, allowing automated detection of suspected SSI adverse events from clinical course text, microbiology reports or coding of diagnoses, procedures, complications and readmissions.
The aim of this study was to evaluate the performance of a novel algorithm to detect to detect SSI at its three anatomical levels, in a cohort of elective colorectal surgery patients who have been previously screened within a nationwide healthcare-associated infection surveillance system.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: