Letzkus Lisa, Picavia Robin, Lyons Genevieve, Brandberg Jackson, Qiu Jiaxing, Kausch Sherry, Lake Doug, Fairchild Karen
Department of Pediatrics, Neurodevelopmental and Behavioral Pediatrics, UVA Children's Hospital, University of Virginia School of Medicine, Charlottesville, VA, USA.
University of Virginia School of Medicine, Charlottesville, VA, USA.
Pediatr Res. 2025 Feb;97(3):1040-1046. doi: 10.1038/s41390-023-02853-2. Epub 2023 Oct 27.
Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.
We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP). We performed HCTSA of >2000 HR metrics and identified 24 metrics analyzed on HR data from two 7-day periods: week 1 and 37 weeks' postmenstrual age (week 1, week 37). Multivariate modeling was used to optimize a parsimonious prediction model.
Week 1 HR metrics with maximum AUC for CP prediction reflected low variability, including "RobustSD" (AUC 0.826; 0.772-0.870). At week 37, high values of a novel HR metric, "LongSD3," the cubed value of the difference in HR values 100 s apart, were added to week 1 HR metrics for CP prediction. A combined birthweight + early and late HR model had AUC 0.853 (0.805-0.892).
Using HCTSA, we discovered novel HR metrics and created a parsimonious model for CP prediction in preterm NICU patients.
We discovered new heart rate characteristics predicting CP in preterm infants. Using every-2-s HR from two 7-day periods and highly comparative time series analysis, we found a measure of low variability HR week 1 after birth and a pattern of recurrent acceleration in HR at term corrected age that predicted CP. Combined clinical and early and late HR features had AUC 0.853 for CP prediction.
心率（HR）模式可反映中枢神经系统功能障碍。我们之前使用高度对比时间序列分析（HCTSA）来识别预测新生儿重症监护病房（NICU）患者死亡率的HR模式，现在使用该方法来发现预测早产儿脑瘫（CP）的模式。
我们研究了孕周<37周的NICU患者，这些患者在整个NICU住院期间每2秒记录一次HR数据，且有或无CP的后期诊断（n = 57例CP和1119例无CP）。我们对2000多个HR指标进行了HCTSA，并确定了24个指标，这些指标是根据出生后第1周和孕龄37周（第1周、第37周）这两个7天时间段的HR数据进行分析的。使用多变量建模来优化一个简洁的预测模型。
用于CP预测的第1周HR指标中，具有最大曲线下面积（AUC）的指标反映了低变异性，包括“稳健标准差”（AUC 0.826；0.77²-0.870）。在第37周时，一个新的HR指标“长标准差³”（相隔100秒的HR值之差的立方值）的高值被添加到第1周的HR指标中用于CP预测。一个结合出生体重+早期和晚期HR的模型的AUC为0.853（0.805-0.892）。
使用HCTSA，我们发现了新的HR指标，并为NICU早产儿的CP预测创建了一个简洁的模型。
我们发现了预测早产儿CP的新心率特征。通过使用出生后两个7天时间段的每2秒HR数据以及高度对比时间序列分析，我们发现出生后第1周的低变异性HR指标以及足月矫正年龄时HR反复加速的模式可预测CP。临床特征与早期和晚期HR特征相结合对CP预测的AUC为0.853。
