Background: Early prognostication of the outcome in pediatric cardiac arrest (CA) patients is crucial for clinical decision-making. Heart rate variability (HRV) has shown potential in predicting outcomes after CA in adult patients. This study investigates whether HRV can be used to predict survival outcomes after pediatric CA using machine learning techniques. Methods: This retrospective study included children with CA, who achieved return of spontaneous circulation (ROSC), and were admitted to the pediatric intensive care unit (PICU) of a tertiary hospital between 2012 and 2021. A 5-min electrocardiogram (ECG) segment acquired at 24 h after CA was used to calculate HRV parameters (time-, frequency-, and non-linear domains). These parameters were used to train a random forest model. The primary outcome was 12-month survival or death. Model performance was evaluated using receiver-operating characteristics (ROC) analysis and predictive values. Feature importance was assessed using Shapley values. Results: A total of 76 patients (male: 63.2%, median age: 2.5 [IQR: 0.4–8.0] years) were divided into survival (34) or death (42) groups based on 12-month outcomes. The machine learning model achieved an accuracy of 77.6% and a positive predictive value of 0.879 for mortality prediction. The most influential features for model predictions were the frequency-domain parameters total power and very-low frequency (VLF) power, with lower values associated with an increased probability of death. Conclusions: Analysis of HRV at 24 h after ROSC may serve as a strong predictor of 12-month survival after pediatric CA.

OBJECTIVES: To develop and validate a tool for standardised quality assessment of data-driven algorithms in healthcare, focusing on the underlying data pipeline. METHODS: Data Assessment Tool for Algorithm Critical Appraisal and Robust Evidence (DATA-CARE) was iteratively developed from the established Quality In Prognosis Studies framework, selected after reviewing 10 existing quality assessment tools for observational and artificial intelligence studies. DATA-CARE evaluates five quality domains of the data pipeline: study population, data, algorithm, outcome and report transparency. Each domain comprises three to five quality criteria. With a total score of 75 points, study quality is categorised as low (<45), moderate (45-59) or high (≥60). DATA-CARE was validated during a systematic review on data-driven algorithms using continuous physiological monitoring data within the paediatric intensive care unit. Two independent reviewers performed quality assessment using DATA-CARE of included studies. Tool validation was evaluated using inter-rater agreement and intraclass correlation coefficient (ICC). RESULTS: DATA-CARE demonstrated robust inter-rater agreement (93.5%) with ICC 0.98 (95% CI 0.96 to 0.99). Of 3858 screened studies, 31 were reviewed in the use case, describing diverse algorithms. Studies were predominantly low (32.3%) to moderate (41.9%) and sporadically (25.8%) high quality. DISCUSSION: Predominance of low-to-moderate quality studies reveals critical barriers to clinical implementation of data-driven algorithms, including low quality data capture and processing, lacking validation strategies and non-transparent reporting of findings. CONCLUSIONS: DATA-CARE allows standardised and reliable critical appraisal for a wide variety of algorithms, addressing current gaps in standardised and reproducible algorithm development.

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.