AI Algorithm for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery. (Infect-IA-2)

Hospital de Granollers

Status: Enrolling

Conditions: Surgical Site Infection

Treatments: Diagnostic Test: Diagnosis of SSI

Study type: Observational

Funder types: Other

Identifiers

NCT07130656

Infect-IA-2

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,200 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Elective colorectal resection

Exclusion criteria

• Emergency surgery
• Infection present at operation
• Previous intestinal stoma

Trial design

1,200 participants in 2 patient groups

Patients assessed for SSI using the standard manual surveillance method

Description:

Patients undergoing colorectal surgery enrolled in the nationwide SSI surveillance programme and assessed for SSI using the standard manual surveillance method.

Treatment:

Diagnostic Test: Diagnosis of SSI

Patients assessed for SSI by an algorithm

Description:

Patients undergoing colorectal surgery enrolled in the nationwide SSI surveillance programme and assessed for SSI using the new algorithm

Treatment:

Diagnostic Test: Diagnosis of SSI