Abstract
Acute nontraumatic chest pain is a frequent reaso n for consultation in emergency departments and represents a diagnostic challenge. The objective is to estimate the risk of significant coronary artery disease (CAD) in patients with cardiogenic acute chest pain for whom the diagnosis of infarction was ruled out in the emergency department with a nondiagnostic ECG and negative high-sensitivity troponins. We prospectively recruited 1625 patients from emergency departments of seven Spanish hospitals. The outcome was presence of significant CAD determined by presence of ischaemia in functional tests or more than 70% stenosis in imaging tests. In this study, we developed a predictive model and evaluated its performance and clinical utility. The prevalence of significant CAD was 14% [227/1625; 95% confidence interval (CI), 12-16]. MAPAC Cardio-PreTest model included seven predictors: age, sex, smoking, history of hypertension, family history of CAD, history of hyperuricaemia, and type of chest pain. The optimism-adjusted model discrimination was C-statistic 0.654 (95% CI, 0.618-0.693). Calibration plot showed good agreement between the predicted and observed risks, and calibration slope was 0.880 (95% CI, 0.731-1.108) and calibration-in-the-large -0.001 (95% CI, -0.141 to 0.132). The model increased net benefit and improved risk classification over the recommended approach by the European Society of Cardiology [Net Reclassification Index (NRI) of events = 5.3%, NRI of nonevents = 7.0%]. MAPAC Cardio-PreTest model is an online prediction tool to estimate the individualised probability of significant CAD in patients with acute chest pain without a diagnosis of infarction in emergency department. The model was more useful than the current alternatives in helping patients and clinicians make individually tailored choices about the intensity of monitoring or additional coronary tests.
| Original language | English |
|---|---|
| Pages (from-to) | 40-46 |
| Number of pages | 7 |
| Journal | European Journal of Emergency Medicine |
| Volume | 30 |
| Issue number | 1 |
| Early online date | 19 Dec 2022 |
| DOIs | |
| Publication status | Published - Feb 2023 |
Bibliographical note
Publisher Copyright:© 2023 Lippincott Williams and Wilkins. All rights reserved.
Keywords
- coronary artery disease
- emergency department
- predictive model
ASJC Scopus subject areas
- General Medicine
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