Logistic regression analysis of echocardiographic parameters in asymptomatic mutation gene carriers of familial hypertrophic cardiomyopathy

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Logistic regression analysis of echocardiographic parameters in asymptomatic mutation gene carriers of familial hypertrophic cardiomyopathy

更新时间：2024-03-02

- Logistic regression analysis of echocardiographic parameters in asymptomatic mutation gene carriers of familial hypertrophic cardiomyopathy
- Journal of Clinical Ultrasound in Medicine Vol. 25, Issue 10, (2023)
- 作者机构：
  
  1. 宁夏医科大学总医院
  2. 宁夏医科大学临床医学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2023，
  
  Published Online：30 October 2023，
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Logistic regression analysis of echocardiographic parameters in asymptomatic mutation gene carriers of familial hypertrophic cardiomyopathy. [J]. Journal of Clinical Ultrasound in Medicine 25(10).(2023)
DOI：

Logistic regression analysis of echocardiographic parameters in asymptomatic mutation gene carriers of familial hypertrophic cardiomyopathy. [J]. Journal of Clinical Ultrasound in Medicine 25(10).(2023) DOI：

摘要

目的应用Logistic回归模型筛选超声心动图中早期识别FHCM无症状突变基因携带者（G+P-）的有效参数。方法纳入FHCM患者一级亲属56例，依据基因检测结果进行分组，其中病例组（G+P-） 22例，对照组（G-P-） 34例。应用Philips iE33超声诊断仪及TomTec脱机分析软件获取相关超声心动图参数。使用SPSS 22.0对数据进行分析，将超声心动图参数中有统计学意义的参数设为自变量进行二元Logistic回归分析，并建立回归方程。结果在众多超声心动图参数中，左室流出道速度时间积分（LVOT-VTI）与整体纵向应变（GLS）是识别G+P-与G-P-的独立危险因素。依此建立的回归方程为：Logistic (P)= 1.851+0.462X1+0.503X2（X1：LVOT-VTI；X2：GLS）。回归模型预测G+P-的ROC曲线下面积为0.906（95%CI:0.830-0.983），约登指数最大值为0.644，当截点值为Logistic (P)=0.247时，其预测灵敏度0.909，特异度0.735，准确度0.804。结论 Logistic回归分析在鉴别G+P-和G-P-中有良好的诊断效能，超声心动图参数LVOT-VTI与GLS可作为鉴别G+P-与G-P-的可靠指标，为G+P-者应用超声心动图精准预测提供了一种新的思路和方法

Abstract

Objective The Logistic regression model was used to screen the effective parameters for early identification of FHCM asymptomatic mutant gene carriers (G+P-) in echocardiography. Methods A total of 56 first-degree relatives of FHCM patients were enrolledand and grouped according to the results of genetic testing

including 22 cases of G+P- and 34 cases of G-P-. Philips iE33 ultrasonic diagnostic instrument and TomTec offline analysis software were used to obtain relevant echocardiographic parameters. SPSS 22.0 was used to analyze the data

and the parameters with statistical significance in echocardiography parameters were set as independent variables for binary logistic regression analysis

and regression equations were established. Results Among many echocardiographic parameters

LVOT-VTI and GLS were independent risk factors for identifying G+P- and G-P-. The regression equation thus established is: Logistic (P)= 1.851+0.462X1+0.503X2 (X1: LVOT-VTI; X2: GLS). The AUC value of the regression model to predicted G+P- was 0.906 (95%CI: 0.830-0.983)

and the maximum value of Youden index was 0.644. When the cut-off value was Logistic (P)=0.247

its predictive sensitivity was 0.909

the specificity was 0.735

and the accuracy was 0.804. Conclusion Logistic regression analysis has good diagnostic performance in differentiating G+P- and G-P-. Echocardiographic parameters LVOT-VTI and GLS can be used as reliable indicators for differentiating G+P- and G-P-

which providing a new idea and method for G+P- patients to accurately predict by echocardiography.

关键词

超声心动图三维斑点追踪技术肥厚型心肌病Logistic回归

Keywords

echocardiography3D speckle tracking imagingHypertrophic cardiomyopathyLogistic regression

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