AI Algorithm for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery.

Официальное название

A Novel AI Algorithm With Enhanced Accuracy for Surveillance of Deep Surgical Site Infections After Elective Colorectal Surgery. A Diagnostic Accuracy Study.

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест Diagnosis of SSI
  Diagnosis of SSI by manual system in colorectal surgery procedures enrolled in the SSI surveillance programme.

Первичные конечные точки

• Rate of surgical site infection [Срок оценки: 30 days]

Критерии участия

Критерии включения

• Elective colorectal resection

Критерии исключения

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

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Испания · 1 центр

• Hospital General de Granollers — Granollers

Публикации

• Casanova-Portoles D, Badia JM, Forero CG, Sanchez-Martinez N, Romero M, Alonso-Solis T, Limon E, Pujol M, Sancho J. A structured-data algorithm for semiautomated surveillance of surgical site infection after colorectal surgery: A diagnostic accuracy study. J Infect Public Health. 2026 Apr;19(4):103151. doi: 10.1016/j.jiph.2026.103151. Epub 2026 Jan 15. PMID 41637931

Идентификаторы

NCT: NCT07130656 · Infect-IA-2

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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