Bigger J T, Fleiss J L, Rolnitzky L M, Steinman R C
Department of Medicine, School of Public Health, Columbia University, New York, NY 10032.
Circulation. 1993 Sep;88(3):927-34. doi: 10.1161/01.cir.88.3.927.
We studied 715 patients 2 weeks after myocardial infarction to test the hypothesis that short-term power spectral measures of RR variability (calculated from 2 to 15 minutes of normal RR interval data) will predict all-cause mortality or arrhythmic death.
We performed power spectral analyses on the entire 24-hour RR interval time series. To compare with the 24-hour analyses, we selected short segments of ECG recordings from two time periods for analysis: 8 AM to 4 PM and midnight to 5 AM. The former corresponds to the time interval during which short-term measures of RR variability would most likely be obtained. The latter, during sleep, represent a period of increased vagal tone, which may simulate the conditions that exist when patients have a signal-averaged ECG recorded, ie, lying quietly in the laboratory. Four frequency domain measures were calculated from spectral analysis of heart period data over a 24-hour interval. We computed the 24-hour power spectral density and calculated the power within three frequency bands: (1) 0.0033 to < 0.04 Hz, very low frequency power, (2) 0.04 to < 0.15 Hz, low frequency power, and (3) 0.15 to 0.40 Hz, high frequency power. In addition, we calculated the ratio of low to high frequency power. These measures were calculated for 15-, 10-, 5-, and 2-minute segments during the day and at night. Mean power spectral values from short periods during the day and night were similar to 24-hour values, and the correlations between short segment values and 24-hour values were strong (many correlations were > or = 0.75). Using the optimal cutpoints determined previously for the 24-hour power spectral values, we compared the survival experience of patients with low values for RR variability in short segments of ECG recordings to those with high values. We found that power spectral measures of RR variability were excellent predictors of all-cause, cardiac, and arrhythmic mortality and sudden death. Patients with low values were 2 to 4 times as likely to die over an average follow-up of 31 months as were patients with high values. The power spectral measures of RR variability did not predict arrhythmic or sudden deaths substantially better than all-cause mortality.
Power spectral measures of RR variability calculated from short (2 to 15 minutes) ECG recordings are remarkably similar to those calculated over 24 hours. The power spectral measures of RR variability are excellent predictors of all-cause mortality and sudden cardiac death.
我们对715例心肌梗死后2周的患者进行了研究，以检验以下假设：RR间期变异性的短期功率谱测量值（根据2至15分钟正常RR间期数据计算得出）将预测全因死亡率或心律失常性死亡。
我们对整个24小时RR间期时间序列进行了功率谱分析。为了与24小时分析结果进行比较，我们从两个时间段选择了短片段心电图记录进行分析：上午8点至下午4点以及午夜至凌晨5点。前者对应于最有可能获得RR间期变异性短期测量值的时间间隔。后者处于睡眠期间，代表迷走神经张力增加的时期，这可能模拟患者进行信号平均心电图记录时存在的条件，即安静地躺在实验室中。通过对24小时间隔内心脏周期数据的频谱分析计算出四个频域测量值。我们计算了24小时功率谱密度，并计算了三个频带内的功率：（1）0.0033至<0.04Hz，极低频功率；（2）0.04至<0.15Hz,低频功率；（3）0.15至0.40Hz，高频功率。此外，我们还计算了低频与高频功率之比。这些测量值在白天和晚上的15分钟、10分钟`、5分钟和2分钟片段中进行计算。白天和晚上短时间段的平均功率谱值与24小时值相似，短片段值与24小时值之间的相关性很强（许多相关性>或=0.75）。使用先前为24小时功率谱值确定的最佳切点，我们比较了心电图记录短片段中RR间期变异性低值患者与高值患者的生存经验。我们发现RR间期变异性的功率谱测量值是全因、心脏和心律失常性死亡率以及猝死的优秀预测指标。低值患者在平均31个月的随访期内死亡的可能性是高值患者的2至4倍。RR间期变异性的功率谱测量值在预测心律失常或猝死方面并不比全因死亡率显著更好。
从短（2至15分钟）心电图记录计算得出的RR间期变异性功率谱测量值与24小时计算得出的测量值非常相似。RR间期变异性的功率谱测量值是全因死亡率和心源性猝死的优秀预测指标。
