Using Machine Learning for Early Prediction of Cardiogenic Shock in Patients With Acute Heart Failure.

ABSTRACT:

Background

Despite technological and treatment advancements over the past 2 ​decades, cardiogenic shock (CS) mortality has remained between 40% and 60%. Our objective was to develop an algorithm that can continuously monitor heart failure patients and partition them into cohorts of high and low risk for CS.

Methods

We retrospectively studied 24,461 patients hospitalized with acute decompensated heart failure, 265 of whom developed CS, in the Johns Hopkins Health System. Our cohort identification approach is based on logistic regression and makes use of vital signs, lab values, and medication administrations recorded during the normal course of care.

Results

Our algorithm identified patients at high risk of CS. Patients in the high-risk cohort had 10.2 times (95% confi

SUBMITTER: Rahman F 

PROVIDER: S-EPMC11307874 | biostudies-literature | 2022 May-Jun

REPOSITORIES: biostudies-literature