4.4 Article

Left Ventricular Torsional Mechanics in Uncomplicated Pregnancy

期刊

CLINICAL CARDIOLOGY
卷 34, 期 9, 页码 543-548

出版社

WILEY-BLACKWELL
DOI: 10.1002/clc.20942

关键词

-

向作者/读者索取更多资源

Background: Alterations in left ventricular (LV) twist (torsion) and untwist have been described for a variety of physiologic and pathologic conditions. Little information is available regarding changes in these parameters during normal pregnancy. Hypothesis: Pregnancy is associated with significant changes in LV torsional mechanics. Methods: Left ventricular twist and untwist was measured in 32 pregnant females (mean gestation 199 +/- 48 d) and 23 nonpregnant controls using speckle-tracking echocardiography. Results: Left ventricular ejection fraction (68 +/- 5% vs 66 +/- 5%) was similar between the groups (P not significant). There was a significant increase in peak LV twist from nonpregnant controls (9.4 +/- 3.7 degrees) to second-trimester (12.0 +/- 4.2 degrees) and third-trimester subjects (12.6 +/- 5.9 degrees, all P < 0.05). Peak LV twist velocity was also increased in second- and third-trimester groups compared with controls (94 +/- 24 degrees/sec and 93 +/- 30 vs 64 +/- 21 degrees/sec, respectively, both P < 0.05). Both peak untwist velocity and time to peak untwist velocity were not significantly different between groups (P not significant). Multiple regression ar alysis indicate that only systolic blood pressure (r = 0.394, P = 0.005) was an independent predictor for increased LV torsion. Conclusions: There are significant changes in LV torsional indices during the course of pregnancy, whereas untwist parameters remain unchanged. Blood pressure is independently associated with increased torsion during pregnancy.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.4
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
Article Cardiac & Cardiovascular Systems

Comparison of machine-learning models for the prediction of 1-year adverse outcomes of patients undergoing primary percutaneous coronary intervention for acute ST-elevation myocardial infarction

Saeed Tofighi, Hamidreza Poorhosseini, Yaser Jenab, Mohammad Alidoosti, Mohammad Sadeghian, Mehdi Mehrani, Zhale Tabrizi, Parisa Hashemi

Summary: This study aimed to predict adverse clinical outcomes in STEMI patients treated with primary PCI using machine learning models. The results showed that the DRF and GBM models had the best performance in predicting major adverse cardiovascular events.

CLINICAL CARDIOLOGY (2024)