Library Branch, US Army Institute of Surgical Research, Fort Sam Houston, Texas 78234-6315, USA.
Shock. 2009 Dec;32(6):565-71. doi: 10.1097/SHK.0b013e3181a993dc.
Heart rate complexity (HRC) is an emerging "new vital sign" for critically ill and injured patients. Traditionally, 800-beat data sets have been used to calculate HRC variables, thus limiting their practical use in an emergency. We sought to investigate whether data set reductions diminish the use of HRC to predict mortality in prehospital trauma patients. Ectopy-free, 800-beat sections of electrocardiogram (EKG) were collected from 31 prehospital trauma patients during their helicopter transport to a level 1 trauma center. Twenty patients survived (survivors) and 11 died (nonsurvivors) after admission. HRC was assessed via approximate entropy (ApEn), sample entropy (SampEn), and similarity of distributions (SOD). The amplitude of high-frequency oscillations was measured via the method of complex demodulation. This analysis was repeated in data sets of 800, 600, 400, 200, and 100 beats. For 800 beats, ApEn and SampEn were lower in nonsurvivors than in survivors, and SOD was higher. With data set reduction, ApEn in survivors and nonsurvivors gradually approached each other but remained different until the 200-beat dataset. Sample entropy did not change with data shortening and remained lower in nonsurvivors in all data sets. Similarity of distributions was nearly constant within groups for all data sets and discriminated survivors from nonsurvivors in 800- and 100-beat data sets. High-frequency amplitude distinguished survivors from nonsurvivors in 400-, 200-, and 100-beat data sets. Logistic regression was performed for the 800-, 200-, and 100-beat data sets, retaining SampEn as a predictor of mortality (area under the receiver-operating-characteristic curves, 0.821-0.895). HRC decreased in nonsurvivors versus survivors. This finding was confirmed for data sets as short as 100 beats by computationally different metrics. SampEn, SOD, and complex demodulation were relatively unaffected by data set reduction. These metrics may be useful for rapid identification of trauma patients with potentially lethal injuries using short EKG data sets.
心率复杂度 (HRC) 是一种新兴的“新生命体征”，适用于危重症和受伤患者。传统上，使用 800 个心跳数据点来计算 HRC 变量，这限制了它们在紧急情况下的实际应用。我们试图研究数据点减少是否会降低 HRC 在预测院前创伤患者死亡率方面的作用。从 31 名在直升机转运至 1 级创伤中心过程中的院前创伤患者的心电图 (EKG) 中采集无异位的 800 个心跳节段。入院后，20 名患者存活（存活者），11 名患者死亡（非存活者）。通过近似熵 (ApEn)、样本熵 (SampEn) 和分布相似性 (SOD) 评估 HRC。通过复解调法测量高频振荡的振幅。这项分析在 800、600、400、200 和 100 个心跳的数据集中重复进行。对于 800 个心跳，非存活者的 ApEn 和 SampEn 低于存活者，而 SOD 更高。随着数据点的减少，存活者和非存活者的 ApEn 逐渐接近，但在 200 个心跳数据集之前仍存在差异。缩短数据时，SampEn 没有变化，所有数据集的非存活者都较低。在所有数据集内，分布相似性在组内几乎保持不变，并且在 800 和 100 个心跳数据集中可以区分存活者和非存活者。高频振幅可以在 400、200 和 100 个心跳数据集中区分存活者和非存活者。对 800、200 和 100 个心跳数据集进行逻辑回归，保留 SampEn 作为死亡率的预测指标（接受者操作特征曲线下面积，0.821-0.895）。与存活者相比，非存活者的 HRC 降低。通过计算不同的指标，在数据点短至 100 个心跳时，证实了这一发现。SampEn、SOD 和复解调受数据集减少的影响相对较小。这些指标可能有助于使用短的 EKG 数据集快速识别可能致命损伤的创伤患者。
