Diagnostic accuracy of the emergency department assessment of chest pain score (EDACS) and EDACS-accelerated diagnostic pathway (ADP) for risk stratification in patients presenting with chest pain in the emergency department

Abstract

Background: Chest pain is a common emergency complaint requiring accurate tools to assess the risk of major adverse cardiac events (MACE). Traditional scoring systems often lack efficiency in emergency settings. The Emergency Department Assessment of Chest Pain Score (EDACS) and its accelerated diagnostic pathway (EDACS-ADP) offer streamlined risk stratification by integrating clinical and biomarker data. Objective: To evaluate the diagnostic accuracy of EDACS and EDACS-ADP in identifying low-risk chest pain patients in the emergency department. Materials and Methods: Prospective, observational, single-center study conducted at a tertiary hospital emergency department between April and June 2022. Adult patients aged 18 years or older with nontraumatic chest pain were included. Exclusions were ST-elevation myocardial infarction, pregnancy, noncardiac chest pain, referral from another hospital, incomplete evaluation, or lack of consent. Relevant clinical characteristics, ECG findings, and serial troponin values were analyzed to calculate EDACS and EDACS-ADP scores. Results: Of 883 patients analyzed for EDACS and 723 for EDACS-ADP, 639 (72.4%) and 392 (54.2%) were categorized as low-risk, respectively. The EDACS score sensitivity for predicting MACE over a 30-day follow-up period was 67.1%, with a negative predictive value of 95.8%. For EDACS-ADP, sensitivity was 94.9%, with a negative predictive value of 99.0%. A negative likelihood ratio of 0.08 demonstrated its strong ability to rule out MACE in low-risk patients over the same 30-day follow-up period. Conclusion: The EDACS-ADP score identified over 50% of chest pain patients as low-risk, supporting safe discharge and reducing the emergency service burden. Its high sensitivity and low likelihood ratio make it an essential tool for improving care and resource use. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.