Pediatric polysomnography-A review of indications, technical aspects, and interpretation

Polysomnography in hospitalized children: Characteristics and clinical practice at a single tertiary care center

BACKGROUND

Polysomnography (PSG) is the gold standard for the diagnosis of pediatric sleep-disordered breathing (SDB). However, the literature characterizing the indications for inpatient PSGs and the impact on clinical decision-making is limited.

OBJECTIVE

To determine the indications, results, and outcomes for children undergoing inpatient PSGs at our institution.

METHODS

We performed a retrospective review of children aged 0-18 years who underwent inpatient diagnostic PSGs between July 2018 and July 2021 at SickKids, Toronto, Canada. Baseline characteristics, indications, and management were reviewed and characterized by descriptive statistics.

RESULTS

Eighty-eight inpatient PSGs were performed in 75 children (male 62.7%). Median (interquartile range) age and body mass index z-score were 1.5 (0.2, 10.8) years and 0.27 (-1.58, 2.66), respectively. The most common indication for inpatient PSG was initiation and titration of ventilation (n = 34/75, 45.3%). Of the 75 children, 48 (64%) had multiple complex chronic conditions (CCCs). Sixty children (80%) underwent a baseline PSG for either the entire night or a portion of the night. Of these studies, 54 (90%) had clinically significant SDB of which isolated obstructive sleep apnea (OSA; 17/60, 28.3%) was the most common. The following management was undertaken for the 54 patients with SDB; respiratory technology (88.9%), surgical intervention (31.5%), positional therapy (1.9%), intranasal steroids (3.7%), and no further intervention (5.6%), respectively.

CONCLUSIONS

Our study highlights that inpatient PSG was an important diagnostic tool resulting in directed medical and surgical management. Future multicenter studies are needed to compare indications for inpatient PSGs across institutions to develop evidence-based clinical practice guidelines.

An automated heart rate-based algorithm for sleep stage classification: Validation using conventional polysomnography and an innovative wearable electrocardiogram device

Background

The rapid advancement in wearable solutions to monitor and score sleep staging has enabled monitoring outside of the conventional clinical settings. However, most of the devices and algorithms lack extensive and independent validation, a fundamental step to ensure robustness, stability, and replicability of the results beyond the training and testing phases. These systems are thought not to be feasible and reliable alternatives to the gold standard, polysomnography (PSG).

Materials and methods

This validation study highlights the accuracy and precision of the proposed heart rate (HR)-based deep-learning algorithm for sleep staging. The illustrated solution can perform classification at 2-levels (Wake; Sleep), 3-levels (Wake; NREM; REM) or 4- levels (Wake; Light; Deep; REM) in 30-s epochs. The algorithm was validated using an open-source dataset of PSG recordings (Physionet CinC dataset, n = 994 participants, 994 recordings) and a proprietary dataset of ECG recordings (Z3Pulse, n = 52 participants, 112 recordings) collected with a chest-worn, wireless sensor and simultaneous PSG collection using SOMNOtouch.

Results

We evaluated the performance of the models in both datasets in terms of Accuracy (A), Cohen's kappa (K), Sensitivity (SE), Specificity (SP), Positive Predictive Value (PPV), and Negative Predicted Value (NPV). In the CinC dataset, the highest value of accuracy was achieved by the 2-levels model (0.8797), while the 3-levels model obtained the best value of K (0.6025). The 4-levels model obtained the lowest SE (0.3812) and the highest SP (0.9744) for the classification of Deep sleep segments. AHI and biological sex did not affect scoring, while a significant decrease of performance by age was reported across the models. In the Z3Pulse dataset, the highest value of accuracy was achieved by the 2-levels model (0.8812), whereas the 3-levels model obtained the best value of K (0.611). For classification of the sleep states, the lowest SE (0.6163) and the highest SP (0.9606) were obtained for the classification of Deep sleep segment.

Conclusion

The results of the validation procedure demonstrated the feasibility of accurate HR-based sleep staging. The combination of the proposed sleep staging algorithm with an inexpensive HR device, provides a cost-effective and non-invasive solution deployable in the home environment and robust across age, sex, and AHI scores.

Pediatric pulmonary year in review 2021: Sleep medicine

Pediatric pulmonology publishes original research, review articles, and case reports on a wide variety of pediatric respiratory disorders. In this article, we summarized the past year's publications in sleep medicine and reviewed selected literature from other journals in this field. We focused on original research articles exploring aspects of sleep-disordered breathing in patients with underlying conditions such as cystic fibrosis, asthma, and sickle cell disease. We also explored sleep-disordered breathing risk factors, monitoring, diagnosis, and treatment; and included recent recommendations for drug-induced sleep endoscopy and ways to monitor and improve PAP adherence remotely.

[Obstructive sleep apnea syndrome in children]

Pediatric obstructive sleep apnea syndrome (OSAS) has a high prevalence in the general population. Risk factors are adenotonsillar hyperplasia, preterm birth, obesity, and craniofacial dysmorphia. A special feature of pediatric OSAS is that it can manifest in behavioral problems. These patients also have an increased risk of perioperative anesthesiologic complications. Diagnostic and therapeutic options should be defined individually using the "Snoring in childhood" algorithm of the German Sleep Research and Sleep Medicine Society (DGSM). Diagnosis based on polysomnography (PSG) is reserved for specialized pediatric sleep centers. The most common surgical treatment for pediatric OSAS is adenoidectomy with tonsillotomy. Positive airway pressure (PAP) therapy in children is only indicated in individual cases. Monitoring of treatment success is important after OSAS therapy.