Long-term prognostic value of major and minor ECG abnormalities in latent Keshan disease with suspect chronic Keshan disease

潜在克山病伴疑似慢性克山病患者中主要和次要心电图异常的长期预后价值

阅读:1

Abstract

OBJECTIVE: This study aims to determine whether baseline electrocardiography (ECG) abnormalities, the appearance of new ECG abnormalities, or other clinical characteristics are associated with increased rates of progression to chronic Keshan disease (KD) among patients with latent KD. METHODS: Four hundred and fourteen new latent KD patients from a monitored population in China were diagnosed and then followed for 10 years. Baseline and 10-year ECG abnormalities were classified according to the Minnesota Code as major and minor. Using Cox proportional hazards regression models, the addition of ECG abnormalities to traditional risk factors were examined to predict chronic KD events. RESULTS: In 414 latent KD patients with ECG abnormalities, 220 (53.1%) had minor and 194 (46.9%) had major ECG abnormalities. During the follow-up, 92 (22.2%) patients experienced chronic KD events; 32 (14.5%) and 60 (30.9%) of these chronic KD events occurred in the minor and major ECG abnormalities groups, respectively. After adjustment for baseline potential confounders, the hazard ratios and 95% confidence intervals (CIs) for progression to chronic KD in latent KD patients with major ECG abnormalities versus those with minor ECG abnormalities was 2.43 (95% CI 1.58-3.93). CONCLUSIONS: Major ECG abnormalities and new ventricular premature complex abnormalities that occurred during the follow-up were both associated with an increased risk of progression to chronic KD. Atrial fibrillation and right bundle branch block with left anterior hemiblock are the most strongly predictive components of major ECG abnormalities. Depending on the model, adding ECG abnormalities to traditional risk factors was associated with improved risk prediction in latent KD.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。