医学
GDF15型
临床终点
心脏病学
内科学
QT间期
冠状动脉疾病
心肌梗塞
人口
心力衰竭
生物标志物
生物化学
环境卫生
化学
临床试验
作者
Kuan‐Hung Yeh,Yao‐Ting Chang,Jyh‐Ming Jimmy Juang,Fu‐Tien Chiang,Ming‐Sheng Teng,Semon Wu,Jeng-Feng Lin,Yu‐Lin Ko
摘要
The corrected QT interval (QTc) predicts prognosis for the general population and patients with coronary artery disease (CAD). Growth differentiation factor-15 (GDF-15) is a biomarker of myocardial fibrosis and left ventricular (LV) remodelling. The interaction between these two parameters is unknown.This study included 487 patients with angiographically confirmed CAD. QTc was calculated using the Bazett formula. Multiple biochemistries and GDF-15 levels were measured. The primary endpoint was total mortality, and the secondary endpoints comprised the combination of total mortality, myocardial infarction and hospitalisation for heart failure and stroke.The mean follow-up period was 1029 ± 343 days (5-1692 days), during which 21 patients died and 47 had secondary endpoints. ROC curve analysis for the optimal cut-off value of primary endpoint is 1.12 ng/mL for GDF-15 (AUC = 0.787, P = 9.0 × 10-6 ) and 438.5 msec for QTc (AUC = 0.698, P = .002). Utilising linear regression, QTc has a positive correlation with Log-GDF-15 (r = .216, P = 1.0 × 10-6 ). Utilising Kaplan-Meier analysis, both QTc interval and GDF-15 level are significant predictors for primary end point (P = .000194, P = 2.0 × 10-6 , respectively) and secondary endpoint (P = .00028, P = 6.15 × 10-8 , respectively). When combined these two parameters together, a significant synergistic predictive power was noted for primary and secondary endpoint (P = 2.31 × 10-7 , P = 1.26 × 10-8 , respectively). This combined strategy also showed significant correlation with the severity of CAD (P < .001).In Chinese patient with angiographically confirmed CAD, a combined strategy utilising an ECG parameter (QTc) and a circulating biomarker (GDF-15) has good correlation with the severity of CAD, and improves the predictive power for total mortality.
科研通智能强力驱动
Strongly Powered by AbleSci AI