Outcomes of European Society of Cardiology 0/1‐h algorithm with high‐sensitivity cardiac troponin T among patients with coronary artery disease

• Published in: Catheterization and cardiovascular interventions Vol. 102; no. 7; pp. 1155 - 1161
• Main Authors: Rana, Muhammad Omer Rehman, Habib, Aatika, Sheikh, Muhammad Abdullah Amir, Ayub, Shayan, Zubair, Rafia, Rehman, Ayesha, Malik, Jahanzeb, Akhtar, Waheed, Awais, Muhammad
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.12.2023
• Subjects: Algorithms; cardiac biomarkers; Cardiology; Cardiovascular disease; Clinical significance; Comorbidity; Coronary artery disease; Coronary vessels; Developing countries; Diabetes mellitus; Gender; Heart diseases; ischemic heart disease; LDCs; Troponin; Troponin T; Vein & artery diseases; coronary artery disease; ischemic heart disease; cardiac biomarkers
• ISSN: 1522-1946; 1522-726X; 1522-726X
• DOI: 10.1002/ccd.30905

Objective The European Society of Cardiology (ESC) 0/1‐h Algorithm with high‐sensitivity cardiac troponin T (hs‐cTnT) has shown promising results in risk stratification and management of patients with coronary artery disease (CAD). However, its outcomes and clinical implications in the context of developing countries remain understudied. Methods This cohort study aimed to evaluate the outcomes and clinical significance of the ESC 0/1‐h Algorithm in a developing country setting. A total of 3534 patients with CAD were enrolled, with 1125 in the Rule‐Out group and 2409 in the Rule‐In group. Baseline characteristics, performance metrics, primary and secondary outcomes, and predictors of Rule‐In and Rule‐Out groups were assessed. Results The study enrolled 3534 patients with CAD, with 1125 in the Rule‐Out group and 2409 in the Rule‐In group. The 0/1‐h Algorithm with hs‐cTnT demonstrated improved performance compared to Troponin T at Presentation. It exhibited higher sensitivity, specificity, negative predictive value, positive predictive value, and area under the curve (AUC) for risk stratification in patients with CAD. Significant differences were observed in baseline characteristics between the Rule‐Out and Rule‐In groups, including age, gender, and comorbidities. The Rule‐In group had a higher incidence of adverse cardiac events and underwent more invasive procedures compared to the Rule‐Out group. Age, gender, hypertension, diabetes, and smoking were identified as significant predictors of Rule‐In and Rule‐Out. These findings highlight the clinical significance of implementing the 0/1‐h Algorithm in the management of patients with CAD in a developing country setting. Conclusion The algorithm's performance, along with its ability to identify high‐risk patients and predict outcomes, highlights its potential to enhance patient care and outcomes in resource‐limited settings.