Early Prediction of Cardiogenic Shock Using Machine Learning.

Area of Science:

• * Cardiology and Artificial Intelligence in Healthcare
• * Clinical Decision Support Systems
• * Predictive Analytics in Critical Care

Background:

• * Cardiogenic shock (CS) presents a significant clinical challenge with high in-hospital mortality rates.
• * Timely identification and management of CS are often hindered by varied patient presentations and settings (ED, WARD, CC).
• * Existing diagnostic and management pathways face challenges in early detection.

Purpose of the Study:

• * To develop and retrospectively evaluate a machine learning (ML) model for predicting cardiogenic shock.
• * To enable early identification of patients at risk for CS, facilitating timely interventions.
• * To assess the model's predictive performance across different clinical settings.

Main Methods:

• * Development of an XGBoost (XGB) based ML model utilizing de-identified electronic health record (EHR) data from 2010-2017.
• * Input variables included demographics, vital signs, lab values, orders, and pre-existing diagnoses.
• * Model designed to predict CS need 2 hours prior to intervention (inotropes, vasopressors, mechanical support).

Main Results:

• * The ML model achieved a high overall area under the curve (AUC) of 0.87.
• * Performance varied by setting: AUC of 0.81 in Critical Care (CC), 0.84 in Emergency Department (ED), and 0.97 in General Wards (WARD).
• * Model precision increased when refined for specific subpopulations like acute myocardial infarction (AMI) or congestive heart failure (CHF).

Conclusions:

• * A machine learning model utilizing EHR data can effectively predict the development of cardiogenic shock.
• * The developed XGBoost model demonstrates significant clinical utility for early CS detection.
• * Further refinement of the model can enhance its precision for specific patient groups, supporting proactive clinical management.