Effect of a Sepsis Prediction Algorithm on Clinical Outcomes

Dascena75,147 enrolled

Overview

In this clinical outcomes analysis, the effect of a machine learning algorithm for severe sepsis prediction on in-hospital mortality, hospital length of stay, and 30-day readmission was evaluated.

Materials and Methods: Clinical outcomes evaluation performed on a multiyear, multicenter clinical data set of real-world data containing 75,147 patient encounters from nine hospitals. Mortality, hospital length of stay, and 30-day readmission analysis performed for 17,758 adult patients who met two or more Systemic Inflammatory Response Syndrome (SIRS) criteria at any point during their stay.

Study Type

INTERVENTIONAL

Purpose

DIAGNOSTIC

Masking

NONE

Enrollment

75,147

Conditions

Severe Sepsis

Interventions

InSightDIAGNOSTIC_TEST

Clinical decision support (CDS) system for severe sepsis detection and prediction

Eligibility

Sex: ALLMin age: 18 YearsHealthy volunteers:

Medical Language ↔ Plain English

Inclusion Criteria: * All patients over the age of 18 presenting to the emergency department or admitted to an inpatient unit at the participating facilities were automatically included for clinical outcomes analysis Exclusion Criteria: * Patients under the age of 18

Outcomes

Primary Outcomes

In-hospital mortality

Rate of in-hospital mortality based on SIRS criteria

Time frame: 1 year

Secondary Outcomes

Hospital length of stay

Duration of hospital length of stay in days based on SIRS criteria

Time frame: 1 year

30-day readmissions

Rate of patient readmissions within 30 days

Time frame: 1 year

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.