The underlying evidence comes from the Medical Expenditure Panel Survey, administered by the US Agency for Healthcare Research and Quality, covering the years 2017 through 2022. This survey is unusual in that it captures data from households themselves as well as from their medical providers, including clinics, hospitals, home health agencies, and pharmacies, along with a sample of insurance plans offered by employers. Crucially, the analysis adjusted for covariates and comorbidities that either facilitate or impede access to health care, or that influence the perceived need for care. That methodological rigor matters, because the question of whether sleep disorders drive excess spending, or whether sicker children simply use more services of every kind, is exactly the kind of confounding that national datasets can help untangle when handled carefully.

The new US figures echo what researchers in Israel documented more than two decades ago. Studies published in the early 2000s showed that children with obstructive sleep apnea syndrome used health care services at markedly higher rates than their peers, and, strikingly, that health care utilization and expenditures fell after the children were treated, often with adenotonsillectomy. Davidson Ward suggests this pattern may hold true across the full spectrum of pediatric sleep disorders: diagnose and treat the sleep problem, and downstream medical costs decline. If that generalizes, the economic case for early identification becomes compelling, because the alternative is paying indefinitely for the complications of an untreated, and frequently reversible, condition.

Yet the 2.88 billion dollar figure almost certainly understates the true cost. The commentary points out that the study did not capture indirect costs such as missed school time, impaired quality of life for the child, and lost wages when parents stay home to care for a chronically sleepless child. Spending on over-the-counter sleep aids and related medications was also excluded. In addition, the survey window included the early years of the COVID-19 pandemic, when families may have avoided seeking care altogether, potentially suppressing recorded expenditures. Even with these limitations, the study’s central message stands: identifying and managing sleep disorders in children is one of those rare interventions that promises both fiscal savings and broad gains in well-being.

So what should be done? The commentary lays out a multi-pronged agenda, beginning with research. Davidson Ward calls for exploring the causes and management of pediatric sleep disorders through both new and existing data, and notes hopefully that the HHS Make America Healthy Again report on children’s well-being mentions sleep health several times, mainly in the context of screen time competing with sleep time. A particular gap involves pediatric insomnia, especially in children with special needs such as autism, where even evidence-based behavioral interventions can fall short. Researchers are encouraged to follow the activities of the National Center on Sleep Disorders Research, which maintains a listserv announcing funding opportunities in the field.

Access to care is the second pillar, and it starts with education. An international survey cited in the commentary found that sleep education in pediatric residency programs is minimal, and that this gap leaves pediatricians reporting unease with treating sleep disorders in children. The proposed fixes are structural: the American Board of Pediatrics and the Accreditation Council for Graduate Medical Education are partnering to shorten pediatric fellowships for trainees aiming to become clinician educators, which could enlarge the applicant pool and make the extra year of sleep medicine fellowship more attractive. Meanwhile, the Advancing Innovation in Residency Education initiative allows pulmonary fellows to train in sleep medicine concurrently with their primary fellowship, and permits board certified or board eligible physicians in relevant specialties to train part-time as fellows. The goal is to expand a pediatric sleep medicine workforce that currently numbers only around 270 specialists nationwide, and the commentary suggests the model could extend to neurology, psychiatry, and otolaryngology.

Technology offers a third route to wider access. Telehealth, the commentary notes, removes the burden of travel for families and allows clinical services to expand without the overhead of brick-and-mortar clinics; the American Academy of Sleep Medicine describes telehealth as having immediate potential to dramatically increase sleep medicine accessibility and clinical efficacy. Diagnostics are another bottleneck. In-laboratory polysomnography remains the gold standard for diagnosing sleep-disordered breathing, but wait times are long. Evidence that home sleep apnea testing is applicable to children and adolescents is accumulating, and forthcoming clinical practice guidelines from the AASM could support insurance coverage for the approach, freeing pediatric sleep laboratories to concentrate on the most complicated cases. The commentary also advocates for wearable devices designed and tested specifically for children, with FDA approval and insurance coverage from the outset, rather than laborious post-market comparisons against adult-oriented gold standards.

Efficiency gains could come from artificial intelligence as well. AI tools that assist in the analysis of polysomnographic data, the commentary argues, should free up clinician time for patient care, easing a workflow in which manual sleep study scoring is notoriously labor intensive. On the therapy side, the commentary urges expansion of proven treatments: hypoglossal nerve stimulation is FDA approved for adolescents with Down syndrome, and Davidson Ward asks why it should not be available for other children who cannot tolerate mask-based positive pressure therapy. Palatal expansion shows promise for relieving obstructive sleep apnea and, she suggests, might be a covered benefit for children on public insurance. She also calls for children to be included in clinical trials of pharmacologic therapies, noting that off-label treatment creates insurance barriers and breeds understandable skepticism among parents, patients, and providers, and recommends that sleep medications be incorporated into the Pediatric Trials Network.

Finally, the commentary makes an explicit appeal for advocacy, aimed at clinicians and the public alike. It points readers to the National Sleep Foundation’s Best Slept Sleep for a Stronger Nation campaign, the AASM’s Sleep for a Stronger Nation initiative and its Community Sleep Health and Public Awareness Grant, and the annual Health Datapalooza sponsored by AcademyHealth, which convenes leaders from government, industry, academia, and the patient community to explore emerging data applications. Clinicians are encouraged to give Grand Rounds, serve as resources for local school boards and child advocacy groups, and support organizations that lobby for sleep health. The broader vision is societal: age-appropriate school start times, help for families managing the conflict between screen time and bedtime, and reduced risk of drowsy driving among teenagers, all of which would create an environment that supports sleep rather than undermining it.

The stakes extend well beyond the health care ledger. As the commentary emphasizes, healthy sleep benefits every domain of a child’s development, from learning and mood to long-term cardiometabolic health, and untreated sleep disorders raise the risk of comorbidities that compound costs over a lifetime. The convergence of new national expenditure data, federal open data initiatives under the OPEN Government Data Act, workforce reforms in training, and maturing home diagnostic technologies gives the field an unusual window of opportunity. The closing sentiment of the commentary is simple and, for a field that has long operated in the shadow of adult sleep medicine, quietly defiant: those who practice pediatric sleep medicine should be proud of their role, because they help children sleep, and in doing so they help families, schools, and the health system as a whole.

Subject of Research: Health care costs and treatment access for pediatric sleep disorders in the United States

Article Title: Pediatric sleep disorders are common and costly: how can we help?

Article References: Ward, S. L. D. (2026). Pediatric sleep disorders are common and costly: how can we help?. Journal of Clinical Sleep Medicine, 22(1), Article 127. https://doi.org/10.1007/s44470-026-00152-1

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00152-1

Keywords: pediatric sleep disorders, obstructive sleep apnea, health care expenditures, polysomnography, home sleep apnea testing, telehealth, sleep medicine workforce, hypoglossal nerve stimulation, pediatric insomnia, Medical Expenditure Panel Survey, sleep health advocacy, artificial intelligence

The study, led by Susana Sousa of CUF Tejo Hospital in Lisbon together with collaborators at Nox Medical in Iceland and the University of Porto, set out to test a cloud-based software called DeepRESP in a population where conventional home testing struggles most. The system had already earned FDA 510(k) clearance after validation on nearly 3,500 routine clinical recordings from sleep clinics across the United States, demonstrating non-inferiority or superiority to established predicate devices. But the atrial fibrillation population poses a unique challenge. Irregular heart rhythms and rate-controlling medications such as beta-blockers scramble the cardiac signals that many simplified sleep-staging algorithms depend on, potentially undermining their accuracy precisely in the patients who need reliable testing the most.

The researchers enrolled 88 consecutive patients with atrial fibrillation referred for sleep assessment at a cardiology outpatient unit, excluding only those with prior sleep apnea diagnoses or recent acute cardiac events. Two-thirds of the participants were men, and the women were significantly older than the men, averaging nearly 67 years compared with about 61. The cohort carried a heavy burden of comorbidity: half were obese, nearly half had hypertension, and roughly one in five had diabetes. Most telling of all, every single participant met criteria for sleep apnea, with an apnea-hypopnea index of at least five events per hour, and 80 percent had moderate to severe disease. Yet only about a quarter reported clinically significant daytime sleepiness, underscoring how easily the condition hides in this cardiac population.

Each patient underwent ambulatory level II polysomnography with a full complement of physiological channels, including six-lead electroencephalography, electrooculography, submental electromyography, nasal pressure airflow, thoracic and abdominal respiratory inductance plethysmography, oximetry, and body position sensing. Certified sleep physicians and technicians manually scored these recordings according to American Academy of Sleep Medicine criteria, establishing the gold-standard reference. DeepRESP then processed the same recordings, but crucially it was allowed to see only the subset of signals available in a home sleep apnea test: oximetry, nasal pressure airflow, and the respiratory inductance plethysmography belts. No electroencephalography, no eye movement channels, no muscle electrodes, and no human editing of the automated output.

The technical heart of the system is the Nox BodySleep 2.0 algorithm, which infers sleep states and arousals exclusively from the thoracic and abdominal breathing belts sampled at 25 hertz. The underlying physiology is elegant. Breathing is irregular and behaviorally influenced during wakefulness, becomes remarkably stable and metabolically regulated in non-REM sleep, and turns erratic again in REM sleep, when skeletal muscle atonia reshapes thoracoabdominal mechanics. These state-dependent signatures are imprinted on the plethysmography signals, and the neural network learns to decode them. Arousals add a further fingerprint: each one triggers a transient ventilatory response, a gasp-like perturbation in the breathing pattern that the algorithm can detect and quantify, enabling it to score hypopneas that end in arousal even when oxygen saturation barely dips.

The results were remarkable for a system flying blind without brain waves. At the epoch level, overall agreement with manual scoring reached 0.91 for wake, 0.95 for REM sleep, and 0.87 for non-REM sleep. Arousal detection achieved an overall percent agreement of 0.80, with a positive percent agreement of 0.72 and a negative percent agreement of 0.84. Respiratory event detection was even stronger, with overall agreement of 0.94 for apneas, 0.78 for hypopneas, and 0.81 for respiratory events combined. Bootstrapping with 10,000 iterations generated confidence intervals for every metric, and the estimates held tightly across resamples.

At the level of the clinical indices that actually drive treatment decisions, the concordance was equally persuasive. The apnea-hypopnea index, the single most important number in sleep medicine, showed an intraclass correlation coefficient of 0.92 against manual polysomnography, with Bland-Altman analysis revealing a mean bias of 6.36 events per hour and limits of agreement stretching from minus 6.83 to 19.56. That bias was driven almost entirely by the hypopnea index, which contributed a bias of 5.45 events per hour, while the apnea index remained highly stable at just 0.91 events per hour. Total sleep time correlated at 0.82 with a mean bias of about 18.5 minutes, and the arousal index reached an intraclass correlation of 0.83 with a low mean bias of 1.44, although individual variance widened at higher arousal frequencies.

Why does this matter beyond the sleep laboratory? Untreated sleep apnea is a recognized saboteur of atrial fibrillation management. It increases recurrence after cardioversion and catheter ablation, blunts the efficacy of antiarrhythmic drugs, and elevates cardiovascular mortality through intermittent hypoxemia, sympathetic activation, and structural cardiac remodeling. Yet conventional home sleep apnea tests, lacking electroencephalography, systematically underestimate disease in patients whose hypopneas terminate in arousals rather than significant desaturation, and in those whose recorded sleep duration is short. A recent evaluation of peripheral arterial tonometry-based home testing in atrial fibrillation patients found only slight to fair agreement with polysomnography for severity classification, with a tendency to overestimate disease. By contrast, the breathing-based approach sidesteps the confounding effects of arrhythmia and cardiac medication entirely, because respiratory effort signals are robust to the electrical chaos of a fibrillating heart.

The authors are candid about the caveats. The cohort was modest in size and drawn from a single tertiary cardiology center, raising the possibility of referral bias. Although recordings were ambulatory, they were acquired as level II polysomnography under technologist supervision, so respiratory belt quality may have exceeded what unattended home testing typically delivers, where sensor displacement and suboptimal positioning are common. The arousal index showed wider limits of agreement than the other parameters, suggesting that breathing-derived arousal estimates, while clinically informative, may not fully replace electroencephalography for fine-grained characterization of sleep fragmentation. Comorbidities such as heart failure, Cheyne-Stokes respiration, and hypoventilation syndromes can produce breathing patterns that mimic obstructive events, and the system has not been assessed in patients already using respiratory support. Nor does DeepRESP score respiratory effort-related arousals.

Even with these limitations, the study marks a genuine inflection point for precision medicine in sleep diagnostics. It challenges the long-standing assumption that neurophysiological channels are indispensable for reliable sleep architecture assessment, at least in the populations where home testing is needed most. A scalable, electroencephalography-independent pathway that reproduces gold-standard indices for apnea severity, total sleep time, and arousal burden could transform screening for the vast, underdiagnosed population of atrial fibrillation patients, many of whom are minimally symptomatic and would otherwise never reach a sleep laboratory. The authors call for validation in fully unattended home recordings and for studies of long-term impact on clinical management and cardiovascular outcomes. If those efforts succeed, the humble breathing belt, read by a neural network, may become the stethoscope of twenty-first-century sleep medicine.

Subject of Research: Validation of a deep learning system for EEG-independent sleep staging, arousal detection, and respiratory event scoring in atrial fibrillation patients

Article Title: Validation of a deep learning-based system for sleep staging, arousal detection, and respiratory event scoring in atrial fibrillation patients

Article References: Sousa, S., Teixeira, C., Sigmarsdóttir, T., Finnsson, E., Ágústsson, J., Sigþórsson, S., Bjarkason, S., Drummond, M., & Bugalho, A. (2026). Validation of a deep learning-based system for sleep staging, arousal detection, and respiratory event scoring in atrial fibrillation patients. Journal of Clinical Sleep Medicine, 22(1), Article 130. https://doi.org/10.1007/s44470-026-00145-0

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00145-0

Keywords: deep learning, atrial fibrillation, obstructive sleep apnea, home sleep apnea testing, polysomnography, respiratory inductance plethysmography, sleep staging, arousal detection, apnea-hypopnea index, machine learning, sleep-disordered breathing, clinical validation