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September 16, 2025

Patient Stratification for Improving Acute Chest Pain Management and Mitigate ED Crowding (Preprint)

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Authors

JLJung-Ting LeeCHChih‐Chia HsiehSCShi‐Wei Chu

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Overview

Retrospective study demonstrates AI effectively stratifies risk in patients with acute chest pain, alleviating ED crowding.

Key Points

  • AI models significantly improve risk stratification for acute chest pain patients, enhancing overall management in the ED.
  • The best ANN model achieved high sensitivity of 0.917 for identifying ACS patients, and a PPV of 0.901 for non-critical patients.
  • Data from 17,935 ED visits were analyzed, showing strong AUROC performance and supporting hs-TnT protocol expansion.
  • These models may lower medical expenditures by optimizing patient management and alleviating emergency department congestion.

Cite This Study

Lee et al. (2025) studied this question.

synapsesocial.com/papers/68d42336713b0b5dfea6b7echttps://doi.org/10.2196/preprints.83099
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Risk stratification of chest pain in the emergency department using artificial intelligence applied to electrocardiograms2025
  2. 2AI-guided refinement of coronary revascularization need in patients suspected of acute coronary syndrome2025 · 1 citations
  3. 3Early auxiliary diagnosis model for chest pain triad based on artificial intelligence multimodal fusion2025
  4. 4Validation of a 0‐/2‐Hour High‐Sensitivity Cardiac Troponin Algorithm for Suspected Acute Coronary Syndrome in the Emergency Department2025 · 3 citations
  5. 5Clinical and Economic Implications of High-Sensitivity Troponin-Informed Admission Strategies in Non-AMI Chest Pain2026