Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals.

ABSTRACT:

Background

Severe sepsis and septic shock are among the leading causes of death in the USA. While early prediction of severe sepsis can reduce adverse patient outcomes, sepsis remains one of the most expensive conditions to diagnose and treat.

Objective

The purpose of this study was to evaluate the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay and 30-day readmission.

Design

Prospective clinical outcomes evaluation.

Setting

Evaluation was performed on a multiyear, multicentre clinical data set of real-world data containing 75 147 patient encounters from nine hospitals across the continental USA, ranging from community hospitals to large academic medical centres.

Participants

Analyses

SUBMITTER: Burdick H 

PROVIDER: S-EPMC7245419 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature