Wu Junyi, Ge Yilin, Chen Ke, Chen Siyu, Yang Jiashu, Yuan Hui
Department of Clinical Laboratory in Beijing Anzhen Hospital, Affiliated Hospital of Capital Medical University, Beijing 100029, China.
Diagnostics (Basel). 2024 Oct 18;14(20):2322. doi: 10.3390/diagnostics14202322.
This study demonstrates differences in the distribution of multiple cardiovascular biomarkers between non-ST-segment elevation myocardial infarction (NSTEMI) and unstable angina (UA) patients. Diagnostic machine learning predictive models measured at the time of admission and 1/2 h post-admission, achieving competitive diagnostic predictive results.
This study aims to explore the diagnostic value of changes in high-sensitivity cardiac troponin I (hs-cTnI) levels in patients with suspected NSTEMI.
A total of 267 patients presented with chest pain, requiring confirmation of acute coronary syndrome (ACS) subtypes (NSTEMI vs. UA). Hs-cTnI and other cardiac markers, such as creatine kinase-MB (CK-MB) and Myoglobin (Myo), were analyzed. Machine learning techniques were employed to assess the application of hs-cTnI level changes in the clinical diagnosis of NSTEMI.
Levels of CK-MB, Myo, hs-cTnI measured at admission, hs-cTnI measured 1-2 h after admission, and NT-proBNP in NSTEMI patients were significantly higher than those in UA patients ( < 0.001). There was a positive correlation between hs-cTnI and CK-MB, as well as Myo (R = 0.72, R = 0.51, R = 0.60). The optimal diagnostic model, Hybiome_1/2h, demonstrated an F1-Score of 0.74, an AUROC of 0.96, and an AP of 0.89.
This study confirms the significant value of hs-cTnI as a sensitive marker of myocardial injury in the diagnosis of NSTEMI. Continuous monitoring of hs-cTnI levels enhances the accuracy of distinguishing NSTEMI from UA. The models indicate that the Hybiome hs-cTnI assays perform comparably well to the Beckman assays in predicting NSTEMI. Moreover, incorporating hs-cTnI measurements taken 1-2 h post-admission significantly enhances the model's effectiveness.
本研究显示了非ST段抬高型心肌梗死（NSTEMI）和不稳定型心绞痛（UA）患者多种心血管生物标志物分布的差异。在入院时和入院后1/2小时测量的诊断机器学习预测模型，取得了具有竞争力的诊断预测结果。
本研究旨在探讨高敏心肌肌钙蛋白I（hs-cTnI）水平变化对疑似NSTEMI患者的诊断价值。
共有267例胸痛患者，需要确诊急性冠状动脉综合征（ACS）亚型（NSTEMI与UA）。分析了hs-cTnI和其他心脏标志物，如肌酸激酶同工酶（CK-MB）和肌红蛋白（Myo）。采用机器学习技术评估hs-cTnI水平变化在NSTEMI临床诊断中的应用。
NSTEMI患者入院时测量的CK-MB、Myo、hs-cTnI水平，入院后1-2小时测量的hs-cTnI水平，以及N末端B型利钠肽原（NT-proBNP）均显著高于UA患者（<0.001）。hs-cTnI与CK-MB以及Myo之间存在正相关（R=0.72，R=0.51，R=0.60）。最佳诊断模型Hybiome_1/2h的F1分数为0.74，曲线下面积（AUROC）为0.96，平均精度（AP）为0.89。
本研究证实了hs-cTnI作为心肌损伤敏感标志物在NSTEMI诊断中的重要价值。持续监测hs-cTnI水平可提高区分NSTEMI与UA的准确性。模型表明，Hybiome hs-cTnI检测在预测NSTEMI方面与贝克曼检测表现相当。此外，纳入入院后1-2小时的hs-cTnI测量值可显著提高模型的有效性。
