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Predicting In-Hospital Cardiac Arrest Using Machine Learning Models: Protocol for a Scoping Review.

Mina Attin1, Bryar Shareef1, Nelson Appiah-Agyei1

  • 1University of Nevada, Las Vegas, Las Vegas, NV, United States.

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|September 9, 2025
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Summary

This scoping review synthesizes machine learning models for predicting in-hospital cardiac arrest (IHCA). It evaluates clinical features and model performance to improve early identification and patient outcomes.

Keywords:
AIartificial intelligencecardiac arrestelectronic health recordsmachine learningpredictive valueresuscitation

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Area of Science:

  • Medical Informatics
  • Clinical Prediction Models
  • Public Health

Background:

  • In-hospital cardiac arrest (IHCA) presents significant morbidity and mortality challenges.
  • Current prediction methods for IHCA lack conclusive evidence on effectiveness.
  • There is a need to synthesize predictive methodologies for IHCA, focusing on prearrest factors.

Purpose of the Study:

  • To conduct a scoping review of machine learning (ML) models for IHCA prediction.
  • To critically evaluate the quality and quantity of clinical features used in these models.
  • To assess temporal characteristics, predictive/prognostic values, and performance metrics of IHCA prediction models.

Main Methods:

  • Adherence to PRISMA-ScR guidelines for scoping reviews.
  • Comprehensive search of PubMed, Web of Science, IEEE Xplore, and Embase (April 2009-April 2024).
  • Inclusion of peer-reviewed, English-language studies on ML for adult IHCA prediction; exclusion of reviews, preprints, and non-ML studies.

Main Results:

  • 16 studies were included from 2479 identified records.
  • Data extraction and synthesis are ongoing, expected completion by June 2025.
  • Anticipated results include an overview of clinical predictors (vital signs, biomarkers, comorbidities) and variations in data quality affecting model performance.

Conclusions:

  • This review will advance ML applications for IHCA prediction by addressing data challenges.
  • Promoting standardization in reporting clinical features is expected to improve model consistency.
  • The findings aim to enhance clinical decision-making and improve outcomes for patients at risk of IHCA.