A Prognostic Model of Surgical Site Infection Using Daily Clinical Wound Assessment.

ABSTRACT:

Background

Surgical site infection (SSI) remains a common, costly, and morbid health care-associated infection. Early detection can improve outcomes, yet previous risk models consider only baseline risk factors (BF) not incorporating a proximate and timely data source-the wound itself. We hypothesize that incorporation of daily wound assessment improves the accuracy of SSI identification compared with traditional BF alone.

Study design

A prospective cohort of 1,000 post open abdominal surgery patients at an academic teaching hospital were examined daily for serial features (SF), for example, wound characteristics and vital signs, in addition to standard BF, for example, wound class. Using supervised machine learning, we trained 3 Naïve Bayes classifiers (BF, SF, and BF+SF) u

SUBMITTER: Sanger PC 

PROVIDER: S-EPMC4961603 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature