Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants
Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants
复制标题
DOI：
10.1161/01.cir.93.12.2142
复制
发表时间：
1996-06-15
期刊：
影响因子：
37.8
通讯作者：
Cohen, RJ
中科院分区：
文献类型：
--
作者：
Bigger, JT;Steinman, RC;Cohen, RJ
本研究的目的是：(1)建立健康中年人rr区间波动对数（幂）对对数（频率）回归的正常值，(2)确定心肌梗死对对数（幂）对对数（频率）回归的影响，(3)确定心脏去神经支配对对数（幂）对对数（频率）回归的影响。(4)评价幂律回归参数预测心肌梗死后死亡的能力。方法与结果分为三组：(1)715例近期心肌梗死患者；(2)年龄和性别与梗死样本相匹配的健康人群274例；(3)心脏移植19例。使用快速傅里叶变换计算24小时rr间隔功率谱，并对对数（功率）在10（-4）和10(-2)Hz之间的对数（频率）进行回归。对数（幂）与对数（频率）之间呈幂律关系。也就是说，该函数描述了一条在健康受试者中斜率近似为1的下降直线。对于心肌梗死组，对数（功率）对对数（频率）的回归线向下平移，负斜率更陡（-1.15）。移植（去神经）组的回归线下移幅度更大，负斜率更陡（-2.08）。传统的功率谱带与斜率之间的相关性较弱，在10(-4)Hz的对数（功率）下，其相关性仅为中等。采用10(-4)Hz的斜率和对数（功率）预测死亡率，并与传统功率谱带的预测值进行比较。10(-4) Hz的斜率和对数（幂）是全因死亡率或心律失常死亡的良好预测指标。为了优化死亡预测，我们计算了与幂律回归线斜率不相关的对数（幂）截距。我们发现斜率和零相关对数（功率）的组合是一个出色的预测器，相对风险为bb10，并且比任何传统功率谱带的组合都要好。斜率和对数（幂）在10(-4)Hz的组合也是心肌梗死后死亡的一个很好的预测指标。结论心肌梗死或心脏失神经支配导致心率间隔波动对数（幂）与对数（频率）幂律回归关系斜率变陡，高度降低。单独地，特别是结合起来，幂律回归参数是任何原因死亡或心律失常死亡的极好预测因子，并且比传统的功率谱带更好地预测这些结果。
Background The purposes of the present study were (1) to establish normal values for the regression of log(power) on log(frequency) for RR-interval fluctuations in healthy middle-aged persons, (2) to determine the effects of myocardial infarction on the regression of log(power) on log(frequency), (3) to determine the effect of cardiac denervation on the regression of log(power) on log(frequency), and (4) to assess the ability of power law regression parameters to predict death after myocardial infarction.Methods and Results We studied three groups: (1) 715 patients with recent myocardial infarction; (2) 274 healthy persons age and sex matched to the infarct sample; and (3) 19 patients with heart transplants. Twenty-four-hour RR-interval power spectra were computed using fast Fourier transforms and log(power) was regressed on log(frequency) between 10(-4) and 10(-2) Hz. There was a power law relation between log(power) and log(frequency). That is, the function described a descending straight line that had a slope of approximate to-1 in healthy subjects. For the myocardial infarction group, the regression line for log(power) on log(frequency) was shifted downward and had a steeper negative slope (-1.15). The transplant (denervated) group showed a larger downward shift in the regression line and a much steeper negative slope (-2.08). The correlation between traditional power spectral bands and slope was weak, and that with log(power) at 10(-4) Hz was only moderate. Slope and log(power) at 10(-4) Hz were used to predict mortality and were compared with the predictive value of traditional power spectral bands. Slope and log(power) at 10(-4) Hz were excellent predictors of all-cause mortality or arrhythmic death. To optimize the prediction of death, we calculated a log(power) intercept that was uncorrelated with the slope of the power law regression line. We found that the combination of slope and zero-correlation log(power) was an outstanding predictor, with a relative risk of >10, and was better than any combination of the traditional power spectral bands. The combination of slope and log(power) at 10(-4) Hz also was an excellent predictor of death after myocardial infarction.Conclusions Myocardial infarction or denervation of the heart causes a steeper slope and decreased height of the power law regression relation between log(power) and log(frequency) of RR-interval fluctuations. Individually and, especially, combined, the power law regression parameters are excellent predictors of death of any cause or arrhythmic death and predict these outcomes better than the traditional power spectral bands.