A Machine Learning Approach for Predicting In-Hospital Cardiac Arrest Using Single-Day Vital Signs, Laboratory Test Results, and International Classification of Disease-10 Block for Diagnosis.

ABSTRACT:

Background

Predicting in-hospital cardiac arrest (IHCA) is crucial for potentially reducing mortality and improving patient outcomes. However, most models, which rely solely on vital signs, may not comprehensively capture the patients' risk profiles. We aimed to improve IHCA predictions by combining vital sign indicators with laboratory test results and, optionally, International Classification of Disease-10 block for diagnosis (ICD10BD).

Methods

We conducted a retrospective cohort study in the general ward (GW) and intensive care unit (ICU) of a 680-bed secondary healthcare institution. We included 62,061 adults admitted to the Department of Internal Medicine from January 2010 to August 2022. IHCAs were identified based on cardiopulmonary resuscitation prescriptions. Patien

SUBMITTER: Park H 

PROVIDER: S-EPMC11788698 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature