Comparison of the T-MACS score with the TIMI score in patients presenting to the emergency department with chest pain

Comparative evaluation of HEART, T-MACS, and HE-MACS scores for risk stratification and management of patients with chest pain in the emergency department

This study evaluated the effectiveness of history, electrocardiogram, age, risk factors, and troponin (HEART), troponin-only Manchester acute coronary syndromes (T-MACS), and history and electrocardiogram-only Manchester acute coronary syndromes (HE-MACS) in diagnosing and managing acute coronary syndrome in patients presenting with chest pain in the emergency department. These scoring systems are crucial for risk stratification and the prediction of major adverse cardiac events (MACEs) and mortality within 30 days. A single-center prospective analytical study was conducted following the STROBE guidelines, with 560 patients presenting with chest pain or ischemic equivalent symptoms at the Ege University Faculty of Medicine Hospital from August 2020 to March 2021. The HEART, T-MACS, and HE-MACS scores were calculated for each patient, and their predictive values for MACE and mortality were analyzed using receiver operating characteristic analysis. The HEART score demonstrated an area under the curve (AUC) of 0.929 for predicting mortality, with 100% sensitivity and 81% specificity. It has been identified as the most reliable predictor of mortality. The T-MACS score showed an AUC of 0.875 for mortality prediction with 85.7% sensitivity and 83.9% specificity. It is particularly effective for high-risk patients, predicting 30-day MACE development rates, which is consistent with the literature. The HE-MACS score yielded an AUC of 0.729 for mortality prediction, with 71.4% sensitivity and 80.7% specificity. Although it effectively excludes MACE in very-low-risk patients, it is limited by its application to a highly isolated group. The discussion interprets the results and compares them with existing literature. The study confirms the high effectiveness of the HEART score in mortality risk assessment, the specificity of the T-MACS score for high-risk patients, and the utility of the HE-MACS score for excluding very-low-risk cases. The limitations of each scoring system are discussed and recommendations for their application in clinical practice are provided. The study concluded that selecting the most appropriate scoring system based on individual patient characteristics is essential for optimal patient management in the emergency department. For optimal patient management, it is essential to select the most appropriate scoring system based on the individual patient characteristics.

Performance of Manchester Acute Coronary Syndromes decision rules in acute coronary syndrome: a systematic review and meta-analysis

Multiple decision-aiding models are available to help physicians identify acute coronary syndrome (ACS) and accelerate the decision-making process in emergency departments (EDs). This study evaluated the diagnostic performance of the Manchester Acute Coronary Syndrome (MACS) rule and its derivations, enhancing the evidence for their clinical use. A systematic review and meta-analysis was performed. Medline, Embase, Scopus, and Web of Science were searched from inception until October 2023 for studies including adult ED patients with suspected cardiac chest pain and inconclusive findings requiring ACS risk-stratification. The predictive value of MACS, Troponin-only MACS (T-MACS), or History and Electrocardiogram-only MACS (HE-MACS) decision aids for diagnosing acute myocardial infarction (AMI) and 30-day major adverse cardiac outcomes (MACEs) among patients admitted to ED with chest pain suspected of ACS. Overall sensitivity and specificity were synthesized using the 'Diagma' package in STATA statistical software. Applicability and risk of bias assessment were performed using the QUADAS-2 tool. For AMI detection, MACS has a sensitivity of 99% [confidence interval (CI): 97-100], specificity of 19% (CI: 10-32), and AUC of 0.816 (CI: 0.720-0.885). T-MACS shows a sensitivity of 98% (CI: 98-99), specificity of 35% (CI: 29-42), and AUC of 0.859 (CI: 0.824-0.887). HE-MACS exhibits a sensitivity of 99% (CI: 98-100), specificity of 9% (CI: 3-21), and AUC of 0.787 (CI: 0.647-0.882). For MACE detection, MACS demonstrates a sensitivity of 98% (CI: 94-100), specificity of 22% (CI: 10-42), and AUC of 0.804 (CI: 0.659-0.897). T-MACS displays a sensitivity of 96% (CI: 94-98), specificity of 36% (CI: 30-43), and AUC of 0.792 (CI: 0.748-0.830). HE-MACS maintains a sensitivity of 99% (CI: 97-99), specificity of 10% (CI 6-16), and AUC of 0.713 (CI: 0.625-0.787). Of all the MACS models, T-MACS displayed the highest overall accuracy due to its high sensitivity and significantly superior specificity. T-MACS exhibits very good diagnostic performance in predicting both AMI and MACE. This makes it a highly promising tool for managing patients with acute chest pain.

Pediatric Chest Pain: A Review of Diagnostic Tools in the Pediatric Emergency Department

Pediatric chest pain is a common chief complaint in the emergency department. Not surprisingly, children with chest pain are usually brought to the emergency department by their parents out of fear of heart disease. However, chest pain in the pediatric population is generally a benign disease. In this review, we have identified musculoskeletal pain as the most prevalent etiology of chest pain in the pediatric population, accounting for 38.7-86.3% of cases, followed by pulmonary (1.8-12.8%), gastrointestinal (0.3-9.3%), psychogenic (5.1-83.6%), and cardiac chest pain (0.3-8.0%). Various diagnostic procedures are commonly used in the emergency department for cardiac chest pain, including electrocardiogram (ECG), chest radiography, cardiac troponin examination, and echocardiography. However, these examinations demonstrate limited sensitivity in identifying cardiac etiologies, with sensitivities ranging from 0 to 17.8% for ECG and 11.0 to 17.2% for chest radiography. To avoid the overuse of these diagnostic tools, a well-designed standardized algorithm for pediatric chest pain could decrease unnecessary examination without missing severe diseases.