Helfenbein Eric D, Ackerman Michael J, Rautaharju Pentti M, Zhou Sophia H, Gregg Richard E, Lindauer James M, Miller David, Wang John J, Kresge Scott S, Babaeizadeh Saeed, Feild Dirk Q, Michaud Francis P
Advanced Algorithm Research Center, Philips Medical Systems, Milpitas, CA, USA.
J Electrocardiol. 2007 Nov-Dec;40(6 Suppl):S103-10. doi: 10.1016/j.jelectrocard.2007.06.019.
QT surveillance of neonatal patients, and especially premature infants, may be important because of the potential for concomitant exposure to QT-prolonging medications and because of the possibility that they may have hereditary QT prolongation (long-QT syndrome), which is implicated in the pathogenesis of approximately 10% of sudden infant death syndrome. In-hospital automated continuous QT interval monitoring for neonatal and pediatric patients may be beneficial but is difficult because of high heart rates; inverted, biphasic, or low-amplitude T waves; noisy signal; and a limited number of electrocardiogram (ECG) leads available. Based on our previous work on an automated adult QT interval monitoring algorithm, we further enhanced and expanded the algorithm for application in the neonatal and pediatric patient population. This article presents results from evaluation of the new algorithm in neonatal patients. Neonatal-monitoring ECGs (n = 66; admission age range, birth to 2 weeks) were collected from the neonatal intensive care unit in 2 major teaching hospitals in the United States. Each digital recording was at least 10 minutes in length with a sampling rate of 500 samples per second. Special handling of high heart rate was implemented, and threshold values were adjusted specifically for neonatal ECG. The ECGs studied were divided into a development/training ECG data set (TRN), with 24 recordings from hospital 1, and a testing data set (TST), with 42 recordings composed of cases from both hospital 1 (n = 16) and hospital 2 (n = 26). Each ECG recording was manually annotated for QT interval in a 15-second period by 2 cardiologists. Mean and standard deviation of the difference (algorithm minus cardiologist), regression slope, and correlation coefficient were used to describe algorithm accuracy. Considering the technical problems due to noisy recordings, a high fraction (approximately 80%) of the ECGs studied were measurable by the algorithm. Mean and standard deviation of the error were both low (TRN = -3 +/- 8 milliseconds; TST = 1 +/- 20 milliseconds); regression slope (TRN = 0.94; TST = 0.83) and correlation coefficients (TRN = 0.96; TST = 0.85) (P < .0001) were fairly high. Performance on the TST was similar to that on the TRN with the exception of 2 cases. These results confirm that automated continuous QT interval monitoring in the neonatal intensive care setting is feasible and accurate and may lead to earlier recognition of the "vulnerable" infant.
对新生儿患者，尤其是早产儿进行QT监测可能很重要，这是因为他们可能同时接触可延长QT间期的药物，还因为他们可能患有遗传性QT延长（长QT综合征），而这与约10%的婴儿猝死综合征的发病机制有关。对新生儿和儿科患者进行院内自动连续QT间期监测可能有益，但由于心率高、T波倒置、双相或低振幅、信号嘈杂以及可用的心电图（ECG）导联数量有限，实施起来很困难。基于我们之前在成人自动QT间期监测算法方面的工作，我们进一步改进并扩展了该算法，以应用于新生儿和儿科患者群体。本文介绍了对新生儿患者中新算法的评估结果。从美国两家主要教学医院的新生儿重症监护病房收集了新生儿监测心电图（n = 66；入院年龄范围为出生至2周）。每个数字记录长度至少为10分钟，采样率为每秒500个样本。对高心率进行了特殊处理，并针对新生儿心电图专门调整了阈值。所研究的心电图被分为一个开发/训练心电图数据集（TRN），包含医院1的24份记录，以及一个测试数据集（TST），包含42份记录，由医院1（n = 16）和医院2（n = 26）的病例组成。由2名心脏病专家对每份心电图记录在15秒时间段内的QT间期进行人工标注。使用差值（算法减去心脏病专家标注值）的均值和标准差、回归斜率以及相关系数来描述算法的准确性。考虑到记录嘈杂导致的技术问题，所研究的大部分心电图（约80%）算法均可测量。误差的均值和标准差都很低（TRN = -3±8毫秒；TST = 1±20毫秒）；回归斜率（TRN = 0.94；TST = 0.83）和相关系数（TRN = 0.96；TST = 0.85）（P <.0001）都相当高。除了2个病例外，TST上的表现与TRN上的表现相似。这些结果证实，在新生儿重症监护环境中进行自动连续QT间期监测是可行且准确的，可能会更早识别出“易损”婴儿。
