Using higher cut-off values to diagnose acute myocardial infarction in patients with elevated hs-cTnT

Abstract

It is often challenging to diagnose acute myocardial infarction (AMI) in patients with elevated high-sensitivity cardiac troponin T (hs-cTnT) before observing a significant rise and/or fall in hs-cTnT. The current study aimed to identify an optimal cut-off to rule in AMI. A total of 76411 patients with elevated hs-cTnT were included. The predictive cut-off values for diagnosing ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) were assessed using the area under the receiver operating characteristic curve (AUC). Among the patients, 50466 (66.0%) had non-cardiac diseases, 25945 (34.0%) had cardiac diseases, and 15502 (20.3%) had AMI, including 816 (1.1%) with STEMI and 14686 (19.2%) with NSTEMI. The median hs-cTnT level was 3788.0 ng/L in STEMI patients and 67.2 ng/L in NSTEMI patients. The optimal cut-off for diagnosing STEMI was 251.9 ng/L, with a sensitivity of 90.7%, specificity of 86.5%, and an AUC of 0.942; the optimal cut-off for diagnosing NSTEMI was 130.5 ng/L, with a sensitivity of 40.9%, specificity of 83.8%, and an AUC of 0.638. Collectively, optimizing the cut-off values for diagnosing STEMI and NSTEMI to 251.9 ng/L and 130.5 ng/L, respectively, demonstrated high accuracy in a large cohort of Chinese patients with elevated hs-cTnT.

Keywords

acute myocardial infarction / high sensitivity cardiac troponin T / electrocardiogram / ST-segment elevation myocardial infarction / renal dysfunction

Fundings

This study was funded in part by the National Key R&D Program of China (Grant No. 2022YFC2402404), the National Natural Science Foundation of China (Grant Nos. 82170351 and 82370342), the Natural Science Foundation of Jiangsu Province (Grant Nos. BK20222002 and BK20231145) and the Clinical Capacity Enhancement Project of Jiangsu Province Hospital (the First Affiliated Hospital of Nanjing Medical University) (Grant No. JSPH-MA-2022-3).

Acknowledgments

The authors thank all staff at the Data Management Center of the First Affiliated Hospital of Nanjing Medical University and Shanghai Synyi Medical Technology Co., Ltd. for providing data governance.

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