1.230032 安徽 合肥,安徽医科大学(龙女,罗春苗)
2.230011 安徽 合肥,合肥市第二人民医院,心血管内科(龙女,罗春苗)
龙女,主要从事心血管内科疾病研究工作。
罗春苗,E-mail: 15956072732@163.com
收稿:2026-03-03,
修回:2026-03-17,
录用:2026-03-25,
纸质出版:2026-04-28
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龙女, 罗春苗. QTcBaz联合NPAR及hs-CRP对急性心肌梗死患者PCI术后发生MACEs的预测价值[J]. 临床心电学杂志, 2026, 35(2): 121-128.
LONG Nv,LUO Chunmiao..Predictive Value of QTcBaz Combined with NPAR and hs-CRP for Post-PCI MACEs in Patients with Acute Myocardial Infarction[J].J Clin Electrocardiol,2026,35(02):121-128.
目的
2
通过对比不同心率校正公式对AMI患者PCI术后预后的评估影响,筛选最优电生理指标,进而探讨QTcBaz联合中性粒细胞百分比与白蛋白比值(NPAR)及超敏C反应蛋白(hs-CRP)对MACEs的预测价值。
方法
2
回顾性分析288例行PCI治疗的AMI患者资料。根据术后是否发生MACEs分为事件组(
n
=72)与无事件组(
n
=216)。通过对比Bazett、Fridericia、Framingham及Hodges公式生成的QTc间期筛选最优电生理指标,采用多因素Logistic回归筛选独立危险因素并构建递进式预测模型。
结果
2
事件组多种QTc校正指标QTcBaz、QTcFri、QTcFra、QTcHo均显著高于无事件组(
P
<
0.05),ROC曲线显示QTcBaz的预测效能最优(AUC=0.734),显著优于其他校正公式;多因素回归显示,在校正年龄、心功能及冠脉病变程度后,QTcBaz[
OR
=1.024 95%
CI
(1.013~1.035)]、NPAR[
OR
=3.518 95%
CI
(1.205~10.271)]及ln(hs-CRP)[
OR
=1.955 95%
CI
(1.397~2.736)]是MACEs的独立危险因素(均
P
<
0.05);模型效能:基础临床模型(模型A)的AUC为0.831;整合了电学脆弱性与炎症负荷维度的最终联合模型(模型D)效能最优,AUC达0.882,灵敏度86.5%,特异度80.4%,其预测价值显著优于传统临床模型(∆AUC=0.051,
Z
=3.894,
P
<
0.001)。
结论
2
QTcBaz、NPAR及hs-CRP均是AMI患者术后MACEs的独立预后因素;在多种心率校正公式中,QTcBaz具有更佳的风险识别效能;在传统临床指标基础上联合心电学与炎症指标,可显著提升风险预测效能。
Objective To investigate the predictive value of QTcBaz combined with neutrophil percentage/ albumin ratio
and high-sensitivity C-reactive protein for major adverse cardiovascular and cerebrovascular events (MACEs) following percutaneous coronary intervention (PCI) in patients with acute myocardial infarction
and to compare the impact of different heart rate correction formulas on prognostic assessment. Methods A retrospective analysis of 288 patients with acute myocardial infarction (AMI) undergoing percutaneous coronary intervention (PCI). Patients were categorized into an event group (
n
=72) and a non-event group (
n
=216) based on the occurrence of MACEs p
ostoperatively. The optimal electrophysiological indicator was identified by comparing QTc intervals derived from the Bazett
Fridericia
Framingham
and Hodges formulas. Independent risk factors were selected using multivariate logistic regression
and a stepwise predictive model was constructed.
Results
2
①Multiple QTc correction indices in the event group (QTcBaz
QTcFri
QTcFra
and QTcHo) were significantly higher than those in the non-event group (
P
<
0.05). ROC curve analysis demonstrated that QTcBaz exhibited the highest predictive performance (AUC=0.734)
significantly outperforming other correction formulas; ②Multivariate regression analysis revealed that after adjusting for age
cardiac function
and coronary artery disease severity
QTcBaz[
OR
=1.024 95%
CI
(1.013~1.035)
]
、NPAR[
OR
=3.518 95%
CI
(1.205~10.271)
]
and (hs-CRP)[
OR
=1.955 95%
CI
(1.397~2.736)
]
were independent risk factors for MACEs (all
P
<
0.05); ③Model performance: The AUC of the baseline clinical model (Model A) was 0.831; The final combined model (Model D)
integrating electrical vulnerability and inflammatory burden dimensions
demonstrated optimal performance with an AUC of 0.882
sensitivity of 86.5%
and specificity of 80.4%. Its predictive value significantly surpassed that of traditional clinical models (∆AUC=0.051
Z
=3.894
P
<
0.001).
Conclusions
2
QTcBaz
NPAR
and hs-CRP are all independent prognostic factors for postoperative MACEs in AMI patients. Among various heart rate correction formulas
QTcBaz demonstrates superior risk identification performance.Combining these with traditional clinical indicators
along with electrocardiographic and inflammatory markers
significantly enhances risk prediction performance.
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