Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: a multicenter cohort study.

ABSTRACT:

Background

Population based surveillance of surgical site infections (SSIs) requires precise case-finding strategies. We sought to develop and validate machine learning models to automate the process of complex (deep incisional/organ space) SSIs case detection.

Methods

This retrospective cohort study included adult patients (age ≥ 18 years) admitted to Calgary, Canada acute care hospitals who underwent primary total elective hip (THA) or knee (TKA) arthroplasty between Jan 1st, 2013 and Aug 31st, 2020. True SSI conditions were judged by the Alberta Health Services Infection Prevention and Control (IPC) program staff. Using the IPC cases as labels, we developed and validated nine XGBoost models to identify deep incisional SSIs, organ space SSIs and complex SSIs using administ

SUBMITTER: Wu G 

PROVIDER: S-EPMC10474760 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature