Severe obstructive sleep apnea in children with syndromic craniosynostosis: analysis of pulse transit time.

STUDY OBJECTIVES: We examined the association between pulse transit time (PTT) and obstructive sleep apnea (OSA) in children with syndromic craniosynostosis (SCS), where OSA is a common problem and may cause cardiorespiratory disturbance. METHODS: Retrospective study of children (age <18 years) with SCS and moderate-to-severe OSA (i.e., obstructive apnea-hypopnea index [oAHI] ≥ 5), or no OSA (oAHI < 1) who underwent overnight polysomnography (PSG). Children without SCS and normal PSG were included as controls. Reference intervals (RIs) for PTT were computed by non-parametric bootstrap analysis. Based on RIs of controls, the sensitivity and specificity of PTT to detect OSA were determined. In a linear mixed-model the explanatory variables assessed were sex, age, sleep stage, and time after obstructive events. RESULTS: In all 68 included children (19 SCS with OSA, 30 SCS without OSA, 19 controls), obstructive events occurred throughout all sleep stages, most prominently during rapid eye movement sleep (REM) and non-REM sleep stages N1 and N2, with evident PTT changes. Greatest reductions were observed 4 - 8 s after an event (p < 0.05). In SCS with OSA, PTT RIs were lower during all sleep stages compared to SCS without OSA. The highest sensitivity was observed during N1 (55.5%), and the highest specificity during REM (76.5%). Lowest PTT values were identified during N1. CONCLUSIONS: Obstructive events occur throughout all sleep stages with transient reductions in PTT. However, PTT as a variable for OSA detection is limited by its sensitivity and specificity.

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children.

STUDY OBJECTIVES: Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification. METHODS: Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels. RESULTS: In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively. CONCLUSIONS: We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification. CITATION: van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med. 2024;20(3):389-397.

Comparing pulse rate measurement in newborns using conventional and dry-electrode ECG monitors.

AIM: Heart rate (HR) is the most important parameter to evaluate newborns' clinical condition and to guide intervention during resuscitation at birth. The present study aims to compare the accuracy of NeoBeat dry-electrode ECG for HR measurement with conventional ECG and pulse oximetry (PO). METHODS: Newborns with a gestational age ≥32 weeks and/or birth weight ≥1.5 kg were included when HR evaluation was needed. HR was simultaneously measured for 10 min with NeoBeat, PO and conventional ECG. RESULTS: A total of 18 infants were included (median (IQR) gestational age 39 (36-39) weeks and birth weight 3 150 (2 288-3 859) grams). Mean (SD) duration until NeoBeat obtained a reliable signal was 2.5 (9.0) s versus 58.5 (171.0) s for PO. Mean difference between NeoBeat and ECG was 1.74 bpm (LoA -4.987-8.459 and correlation coefficient 0.98). Paired HR measurements over 30-s intervals revealed no significant difference between NeoBeat and ECG. The positive predictive value of a detected HR <100 bpm by NeoBeat compared with ECG was 54.84%, negative predictive value 99.99%, sensitivity 94.44%, specificity 99.99% and accuracy 99.85%. CONCLUSIONS: HR measurement with NeoBeat dry-electrode ECG at birth is reliable and accurate.