Flynn Allyson C, Jelinek Herbert F, Smith Megan
School of Community Health, Charles Sturt University, Albury, Australia.
Aust J Rural Health. 2005 Apr;13(2):77-82. doi: 10.1111/j.1440-1854.2005.00658.x.
Cardiovascular complications are the main cause of death in people with diabetes. Early, asymptomatic changes are due to autonomic nervous system dysfunction, which if identified can lead to improved health. This study used detrended fluctuation analysis to identify changes in heart rate variability (HRV) associated with short-time electrocardiograph (ECG) recordings. The aim of the study was to determine whether heart rate variability analysis on short ECG recordings has the potential to be a useful adjunct to clinical practice.
Comparative design with three independent simple random samples.
University-based research project.
Forty-eight people with no diabetes or cardiovascular complications had a 20 min ECG recorded, which was subsequently analysed using mathematical procedures. All participants also had a lying-to-standing autonomic nervous system test. Data was analysed using a Student t-test.
Heart rate variability expressed as a numeric value (alpha(1)), is reduced in disease states. We found a significant difference in alpha(1)(P = 0.03) between the ECG recordings of the diabetes and control groups. In addition lower alpha(1)values were obtained from people identified with autonomic dysfunction within the diabetes group.
The importance of our findings is that abnormal HRV identifies people with cardiovascular disease, irrespective of diabetes status, that may have autonomic neuropathy. HRV analysis is easily implemented by primary health care providers and has the potential to lead to improved health care by reducing inequity in rural areas and specifically addressing cardiovascular complications associated with diabetes.
心血管并发症是糖尿病患者的主要死因。早期无症状变化是由自主神经系统功能障碍引起的，若能识别这些变化则可改善健康状况。本研究采用去趋势波动分析来识别与短程心电图（ECG）记录相关的心率变异性（HRV）变化。该研究的目的是确定短程ECG记录的心率变异性分析是否有可能成为临床实践的有用辅助手段。
具有三个独立简单随机样本的比较设计。
基于大学的研究项目。
48名无糖尿病或心血管并发症的人进行了20分钟的ECG记录，随后使用数学程序进行分析。所有参与者还进行了卧立位自主神经系统测试。数据采用学生t检验进行分析。
以数值表示的心率变异性（α(1)）在疾病状态下会降低。我们发现糖尿病组和对照组的ECG记录之间α(1)存在显著差异（P = 0.03）。此外，糖尿病组中被确定存在自主神经功能障碍的人获得的α(1)值较低。
我们研究结果的重要性在于，异常的HRV可识别出患有心血管疾病的人，无论其糖尿病状态如何，这些人可能患有自主神经病变。HRV分析易于由初级卫生保健提供者实施，并且有可能通过减少农村地区的不平等现象并特别解决与糖尿病相关的心血管并发症来改善医疗保健。
