Use of Coronary CT Angiography to Predict Obstructive Lesions in Patients with Chest Pain without Enzyme and...

Area of Science:

• Cardiology
• Medical Imaging
• Predictive Modeling

Background:

• Diagnosing acute coronary syndrome (ACS) is challenging in emergency departments (ED) for patients presenting with chest pain but lacking ST-segment elevation and troponin elevation.
• Coronary computed tomography angiography (CCTA) is a key diagnostic tool, but its selective use is important.

Purpose of the Study:

• To develop a predictive model for rapidly identifying significant coronary artery stenosis in patients with chest pain, normal electrocardiography (ECG), and negative troponin levels.
• To improve the selection of appropriate patients for CCTA.

Main Methods:

• A retrospective observational cohort study of 904 patients who underwent CCTA between January 2017 and December 2018.
• Logistic regression analysis was used to develop a predictive model based on clinical factors.
• Significant stenosis was defined as >70% diameter narrowing on CCTA.

Main Results:

• 14.8% of patients (134/904) had significant coronary artery stenosis.
• Independent predictors for stenosis included age ≥65 years, male sex, diabetes, history of ACS, and typical chest pain.
• The developed prediction model demonstrated an area under the curve (AUC) of 0.782.
• A cut-off score of ≥5 yielded 86.6% sensitivity and 56.4% specificity for identifying significant stenosis.

Conclusions:

• A novel prediction score can effectively identify chest pain patients without ECG or troponin abnormalities who are likely to have significant coronary artery stenosis.
• This score can assist clinicians in determining the need for CCTA in this patient population.
• The model offers a valuable tool for risk stratification and optimizing CCTA utilization in the emergency department setting.