Sepsis Prediction for the General Ward Setting.

ABSTRACT:

Objective

To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design.

Design

Retrospective analysis of data extracted from electronic health records (EHR).

Setting

Single, tertiary-care academic medical center in St. Louis, MO, USA.

Patients

Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019.

Interventions

None.

Measurements and main results

Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating

SUBMITTER: Yu SC 

PROVIDER: S-EPMC8957791 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature