The commentary, published in October 2026, draws attention to research examining geographic and racial and ethnic patterns of polysomnography use among Medicaid-enrolled children between 2017 and 2019, a body of work led by investigators including C. Christine McLaughlin, Jamie L. Hawke, Emily F. Boss, and their colleagues. Medicaid covers roughly forty percent of American children, making it the single largest payer of pediatric care in the country, and an ideal lens through which to observe how diagnostic practice varies when financial barriers at the point of care are nominally removed. What those data reveal is not a smooth national distribution of sleep testing but stark regional and demographic gradients that persist even among children who share the same insurance coverage.

The clinical stakes are considerable. Obstructive sleep apnea in children affects an estimated one to five percent of the pediatric population, and its consequences extend far beyond disrupted nights. Chronic intermittent hypoxia and fragmented sleep during critical developmental windows are associated with behavioral problems, impaired attention and learning, cardiovascular strain, and metabolic dysfunction. The standard treatment for most children is adenotonsillectomy, the surgical removal of the adenoids and tonsils, which resolves airway obstruction in the majority of cases. Clinical practice guidelines from the American Academy of Pediatrics and the American Academy of Otolaryngology recommend polysomnography before surgery in most circumstances, particularly for children under three, those with obesity, craniofacial anomalies, neuromuscular disease, or other risk factors for perioperative respiratory complications.

The rationale for preoperative testing is grounded in physiology and surgical safety. The apnea-hypopnea index measured during a polysomnogram quantifies how many times per hour a child’s airflow collapses or diminishes, and children with severe obstruction face elevated risks of postoperative respiratory events, including oxygen desaturation, airway obstruction during anesthesia recovery, and the need for unplanned intensive care admission. Studies of children undergoing adenotonsillectomy have repeatedly identified preoperative polysomnographic severity as one of the strongest predictors of these complications, alongside young age, obesity, and cardiac findings. Knowing a child’s baseline severity allows anesthesiologists and surgeons to plan appropriate monitoring, choose the right postoperative setting, and anticipate trouble before it happens in the recovery room.

Yet the commentary makes clear that adherence to this testing paradigm is anything but uniform. Children enrolled in Medicaid, and children from Black, Hispanic, and other minoritized racial and ethnic groups, are consistently less likely to undergo polysomnography before adenotonsillectomy than privately insured white children, and regional variation compounds these disparities. In some parts of the country, preoperative sleep testing is routine; in others, it is the exception. The result is a system in which two children with identical symptoms and identical surgical indications may receive fundamentally different workups depending on their zip code and their family’s background.

The drivers of this variation are multifactorial, and Grigg-Damberger’s commentary situates the new utilization data within a decade of accumulated evidence. Access to pediatric sleep laboratories is geographically concentrated: accredited facilities cluster around academic medical centers and large metropolitan hospitals, leaving families in rural areas and smaller communities facing long travel distances, waiting lists that stretch for months, and the logistical burden of overnight stays far from home. For a parent working an hourly job without paid leave, an overnight sleep study two hundred miles away is not a neutral inconvenience; it can be an insurmountable barrier. Where sleep laboratories are scarce, clinicians may reasonably default to operating on clinical judgment alone, a practice the guidelines permit in uncomplicated cases but which can miss severe disease in children who look deceptively healthy by day.

Racial and ethnic disparities add a second, more troubling layer. Prior studies of tonsillectomy utilization among Medicaid-insured children have documented that Black children are less likely to receive the surgery at all despite comparable or greater disease burden, and surveys of families suggest that caregiver willingness to consider surgery varies with trust in the medical system, the quality of counseling received, and prior experiences of care. Children from minoritized groups also carry a higher baseline prevalence of severe sleep-disordered breathing, driven in part by higher rates of obesity and asthma, which makes under-testing particularly consequential: the children at greatest perioperative risk are precisely those least likely to have that risk quantified before they reach the operating room.

The commentary also engages with a genuine clinical debate: whether routine polysomnography before adenotonsillectomy is always necessary, or whether selective testing is a defensible way to conserve scarce laboratory capacity. Some investigators have argued that for the typical healthy child with clear-cut tonsillar hypertrophy and classic symptoms, surgery without a sleep study is reasonable and that mandatory testing introduces delay and cost without changing outcomes. Others counter that clinical signs correlate poorly with objective severity, that snoring frequency in young children predicts cognitive and behavioral problems better than the apnea-hypopnea index itself, and that skipping the test systematically disadvantages children whose symptoms are dismissed or whose families lack the advocacy needed to push for referral. The disagreement is not merely academic; it determines how scarce testing resources should be allocated and whether the current pattern of underuse in vulnerable populations should be read as clinical pragmatism or as inequity.

What makes the new utilization data so striking is that they emerge from a population in which insurance itself should not be the limiting factor. Medicaid removes the out-of-pocket cost barrier that private deductibles can create, yet disparities persist, which points to structural and systemic explanations: the uneven distribution of pediatric sleep medicine infrastructure, referral patterns that differ by practice setting, differences in how symptoms are perceived and communicated across cultural and linguistic lines, and the cumulative effect of fragmented care for children who move between providers. Grigg-Damberger’s framing, borrowed from the real estate mantra, is deliberate: location, location, location. A child’s address predicts their access to diagnosis in ways that clinical guidelines have not yet overcome.

The path forward, as the commentary implies, requires both measurement and infrastructure. Telemedicine-based sleep assessment, home sleep apnea testing adapted for children, and expanded training for pediatric sleep technologists could extend diagnostic capacity beyond academic centers. Quality measures that track preoperative testing rates by region and by demographic group would make the disparities visible to payers and health systems in a way that individual clinicians cannot easily see. And the underlying research, published in the Journal of Clinical Sleep Medicine, provides the kind of population-level evidence that policy interventions demand. Until then, the overnight sleep study that should precede most pediatric tonsil surgeries will remain, for too many American children, a test determined not by the severity of their disease but by the accident of where they were born.

Subject of Research: Geographic and racial/ethnic disparities in pediatric polysomnography use among US Medicaid-insured children

Article Title: Location, location, location: wide geographic and racial/ethnic variations in pediatric polysomnography in US Medicaid children

Article References: Grigg-Damberger, M. (2026). Location, location, location: wide geographic and racial/ethnic variations in pediatric polysomnography in US Medicaid children. Journal of Clinical Sleep Medicine, 22(1), Article 184. https://doi.org/10.1007/s44470-026-00170-z

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00170-z

Keywords: polysomnography, pediatric sleep apnea, Medicaid, adenotonsillectomy, health disparities, sleep-disordered breathing, tonsillectomy, geographic variation, Journal of Clinical Sleep Medicine, obstructive sleep apnea, pediatric otolaryngology, health equity

The underlying research drew on the control arm of PATS, a multicenter randomized controlled trial conducted from 2016 to 2021 at seven tertiary referral centers across the United States. The trial enrolled children aged 3.0 to 12.9 years who snored at least three nights per week and had an obstructive apnea-hypopnea index, or oAHI, below 3 events per hour, along with enlarged tonsils of Brodsky grade II or greater. Children were randomly assigned to early adenotonsillectomy or to watchful waiting with supportive care. In this secondary analysis, the researchers focused on the 234 children who did not undergo surgery, a group with a mean age of 6.2 years that included 111 girls, 65 children of Black or African American race, and 37 children of Hispanic ethnicity. The analysis was exploratory rather than prespecified, and the investigators followed observational study reporting guidelines while leveraging the rigor of the original trial’s standardized protocols.

The technical core of the study rested on two distinct definitions of progression. The first was polysomnographic: a child was considered to have progressed if the oAHI on a repeat overnight sleep study reached 3 or more events per hour at 12 months. All sleep studies were scored centrally by certified technologists blinded to each child’s clinical presentation, according to American Academy of Sleep Medicine pediatric rules. The second definition was symptomatic: a caregiver-completed Pediatric Sleep Questionnaire-Sleep-Related Breathing Disorder scale score of 0.33 or higher on the 0-to-1 range, a validated threshold indicating clinically significant symptom impact. The researchers also collected a battery of baseline measures, including the modified Epworth Sleepiness Scale for children, the OSA-18 quality-of-life instrument, behavioral inventories such as the Behavior Rating Inventory of Executive Function and the Child Behavior Checklist, physical examination findings including tonsil grade and palate position, and histories of asthma, allergies, ADHD, and environmental tobacco smoke exposure confirmed where possible by urinary cotinine assays.

The headline result on the objective side was reassuring. Of the 150 children who completed 12-month polysomnography, only 20, or 13 percent, progressed to an oAHI of 3 or greater. The mean baseline oAHI in the cohort was just 0.8 events per hour, and most children’s airway physiology remained stable or improved over the observation year. This finding aligns with earlier evidence that roughly 40 percent of children with mild-to-moderate obstructive sleep apnea show spontaneous improvement within a year without treatment, and that most children with primary snoring never develop frank apnea. For families worried that a year of observation might allow a silent slide into severe disease, the data suggest that such deterioration is the exception rather than the rule, at least over a 12-month horizon in this prepubertal, predominantly non-obese sample.

The symptomatic picture was far less comforting. Among the 192 children with follow-up questionnaire data, 110, or 57 percent, had a high symptom burden at 12 months, and the overwhelming majority of those cases, 94 percent, represented persistence of symptoms that were already elevated at baseline rather than new onset. Nearly three quarters of children whose baseline questionnaire score crossed the clinical threshold still scored above it a year later. Even more telling, among the children whose symptoms persisted or worsened and who also completed repeat sleep studies, only 18 percent had progressed objectively to an oAHI of 3 or greater. In other words, four out of five children whose breathing events remained technically mild still felt the consequences of disrupted sleep, a mismatch that echoes prior work showing that parent-reported symptom scales often mediate the behavioral benefits of adenotonsillectomy better than polysomnographic indices do.

Which children were most likely to get worse? On the objective measure, Black race more than doubled the odds of polysomnographic progression, with an unadjusted odds ratio of 2.71, and a higher baseline symptom score raised the odds by 74 percent per standard deviation. The racial disparity is consistent with findings from the earlier Childhood Adenotonsillectomy Trial, or CHAT, and with a broader literature showing that Black children carry higher rates of sleep apnea, greater severity, and more persistent disease after surgery. The authors point to multifactorial explanations, including chronic exposure to respiratory irritants and allergens common in disadvantaged neighborhoods; notably, Black children in PATS were more likely to live in such neighborhoods. On the symptomatic side, asthma nearly tripled the odds of persistence or progression, ADHD more than tripled them, and tobacco smoke exposure raised them by nearly two and a half times, while worse sleepiness, poorer disease-specific quality of life, and greater behavioral impairment at baseline all pointed in the same direction.

One result defied expectations. Children with larger tonsils, grade III or IV on the Brodsky scale, had lower odds of symptomatic persistence or progression, with an odds ratio of 0.42. Among children with big tonsils, the proportion of caregivers reporting a high symptom inventory dropped from 69 percent at baseline to 49 percent at follow-up, while among children with smaller grade II tonsils the figure barely moved, from 75 percent to 70 percent. The authors propose several mechanisms: a possible ceiling effect in which children with large tonsils are already at maximal obstruction and have room only to improve as tonsils shrink or airways grow, versus children with smaller tonsils whose obstruction may arise from adenoid hypertrophy, diffuse airway inflammation, low pharyngeal muscle tone, or other non-tonsillar sites that a simple oral examination cannot capture. PATS did not assess adenoid size, and the finding is consistent with a long-standing literature showing that tonsil size correlates poorly with objective apnea severity.

The asthma connection deserves particular attention. Asthma and sleep-disordered breathing are known to reinforce each other bidirectionally: children with asthma are roughly twice as likely to have sleep-disordered breathing, asthma severity tracks with sleep apnea severity, and disrupted sleep worsens asthma control. Within PATS, both study arms received referral for asthma management as part of supportive care, yet at follow-up only 39 percent of children with asthma in the surgery group had high symptom scores compared with 74 percent in the observation group. Although a separate analysis of surgery’s effect on asthma symptoms did not reach statistical significance, the pattern suggests that optimizing asthma treatment alone did little to relieve sleep-related symptoms over a year of watchful waiting, lending support to earlier surgery for children whose mild snoring coexists with asthma.

How useful are these variables for prediction in practice? The honest answer is: modestly. The best multivariable model for polysomnographic progression, combining Black race, REM-sleep apnea index, and the baseline symptom score, achieved an area under the curve of only 0.70, barely better than the 0.62 achieved with clinical variables alone. For symptomatic persistence, the model pairing the baseline questionnaire score with tonsil grade performed better, reaching an AUC of 0.85, but the authors caution that the exploratory analysis was not powered to build a definitive risk calculator, and confidence intervals around several predictors were wide. The study also excluded children with severe obesity, recurrent tonsillitis, and major chronic illness, so the findings apply most directly to the prepubertal, otherwise healthy snorers who dominate typical clinic populations.

The clinical message that emerges is subtle but actionable. A single repeat sleep study a year later captures only the small minority of children whose breathing physiology objectively worsens, and it may miss the larger group whose symptoms, behavior, and quality of life remain impaired even as their apnea index stays low. For the symptomatic snoring child, the authors suggest, repeated symptom assessment and attention to behavioral and quality-of-life impacts may guide decisions better than polysomnography alone. Children who are Black, or who have asthma, ADHD, tobacco smoke exposure, or a heavy baseline symptom burden, may be stronger candidates for earlier adenotonsillectomy than their peers. As the largest controlled dataset yet assembled on this question, the analysis reframes watchful waiting not as a passive default but as a monitored strategy with identifiable risk profiles, and it sets the stage for longer, larger studies to determine which children truly benefit from waiting and which would be better served by operating sooner.

Subject of Research: Progression and symptom persistence of mild sleep-disordered breathing in children managed with watchful waiting instead of adenotonsillectomy

Article Title: Progression of mild sleep-disordered breathing in children managed with watchful waiting

Article References: Kirkham, E. M., Ishman, S., Garetz, S., Baldassari, C. M., Mitchell, R. B., Liu, C., Tapia, I. E., Elden, L. M., Hassan, F., Ibrahim, S., Ross, K., Cen, M., Wang, R., Redline, S., & Chervin, R. D. (2026). Progression of mild sleep-disordered breathing in children managed with watchful waiting. Journal of Clinical Sleep Medicine, 22(1), Article 106. https://doi.org/10.1007/s44470-026-00082-y

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00082-y

Keywords: pediatric sleep apnea, sleep-disordered breathing, adenotonsillectomy, watchful waiting, polysomnography, snoring, PATS trial, asthma, ADHD, health disparities, tonsil size, Journal of Clinical Sleep Medicine

The research team, led by Colleen C. McLaughlin of the Center for Child Health Equity and Outcomes Research at Nationwide Children’s Hospital in Columbus, Ohio, drew on the Transformed Medicaid Statistical Information System, the federal data repository that captures claims from Medicaid and the Children’s Health Insurance Program, known as CHIP. Together these programs insure roughly half of all American children, making them the only data source capable of painting a truly national picture of pediatric healthcare utilization. The investigators analyzed claims from 2017 through 2019, deliberately stopping before the COVID-19 pandemic distorted enrollment figures and care patterns, and identified overnight, attended polysomnography using the standard billing codes for the procedure.

The scale of the analysis was extraordinary. Among more than 51.7 million children enrolled with unrestricted benefits at some point during the three-year window, the researchers identified 482,688 polysomnograms. After careful exclusions for incomplete records and data quality problems, including the removal of Rhode Island and Vermont, 478,568 tests remained in the state-level analysis, representing 99.1 percent of all identified studies. The denominator of 95.5 million person-years of enrollment yielded a national utilization rate of 50.1 sleep studies per 10,000 Medicaid-enrolled children per year. That headline number, however, conceals a startling degree of local variation.

At the state level, age-adjusted rates ranged from a low of 23 polysomnograms per 10,000 person-years in Kansas to a high of 93 per 10,000 in Michigan, a fourfold difference between neighboring Midwestern states. Mapping the data at an even finer resolution, using Census Bureau Public Use Microdata Areas that each contain at least 100,000 residents, revealed that the East North Central Census Division, anchored by Michigan and the Great Lakes industrial states, had the highest mean local rate at 64.9 per 10,000. Sixteen of the 25 microdata areas in the top 1 percent of utilization, defined as rates above 140 per 10,000, sat within that single division, and 15 of those were in Michigan alone. By contrast, the Pacific and West South Central divisions, encompassing states such as California, Oklahoma, and Texas, recorded the lowest rates, at 39.6 and 43.7 per 10,000 respectively. Fourteen of the 25 lowest-rate microdata areas were in Texas.

The statistical machinery behind these findings was rigorous. The team standardized rates to the age distribution of the enrolled population, applied the Jenks natural breaks optimization method to group mapped values, and used the Games–Howell test, which tolerates unequal variances and adjusts for multiple comparisons, to confirm that the differences between Census divisions were statistically significant. Multivariable Poisson regression with clustered error estimators then linked utilization to neighborhood characteristics at the ZIP code level, adjusting for beneficiary age. Because Medicaid data quality varies by state, the researchers also ran a sensitivity analysis excluding the 14 states flagged by the federal Medicaid Data Quality Atlas as having unusable enrollment or claims data for at least one study year, along with six states reporting high rates of missing or invalid procedure codes. The core conclusions held firm, with mean rates for every ZIP code grouping shifting by less than 10 percent.

Two demographic patterns emerged with particular clarity. First, urbanicity mattered: metropolitan ZIP codes, which accounted for 84.1 percent of all the sleep studies in the dataset, had the highest adjusted mean rate at 41.3 per 10,000, and rates declined steadily as communities became more rural, with isolated rural towns showing roughly 6 to 9.7 fewer studies per 10,000 than metropolitan areas. Second, and more troubling, the racial and ethnic composition of a neighborhood was strongly associated with testing rates. ZIP codes where at least 90 percent of residents were White non-Hispanic had the highest rate, 50.8 per 10,000, while ZIP codes where a majority of residents belonged to other racial and ethnic groups, including Asian, Native Hawaiian and Pacific Islander, and American Indian or Alaskan Native populations, had the lowest, at 27.6 per 10,000. Neighborhoods with larger populations of color received between 4.2 and 23.2 fewer sleep studies per 10,000 than the predominantly White areas.

The authors were careful to note that some of these differences, while statistically significant, were modest in absolute terms, and that the analysis relied on ZIP code level demographics rather than individual self-reported race and ethnicity, which most states record too poorly to use. Still, the direction of the disparity is consistent with a growing body of evidence. Prior studies have suggested that polysomnography may itself contribute to racial and ethnic disparities in tonsillectomy rates among Medicaid-insured children, because requiring the test before surgery adds cost, delays treatment, and increases the risk that families lose contact with the care system altogether. Research in Ontario, Canada, has shown that shorter travel times to sleep clinics increase the likelihood that children receive the test, and longer travel distances to pediatric sleep laboratories, which are scarcer than adult facilities, are a plausible explanation for the rural deficit observed in the American data.

The study also situates the geographic patterns within a genuine clinical controversy. Professional societies disagree about when a child with habitual snoring needs a sleep study before tonsillectomy. The American Academy of Sleep Medicine and the American Academy of Pediatrics have recommended polysomnography for all children with suspected obstructive sleep apnea, while the American Academy of Otolaryngology–Head and Neck Surgery takes a narrower position in its 2019 guideline, recommending the test mainly for children with elevated anesthesia or surgical risks, such as obesity or Down syndrome, or when the need for surgery is unclear. Because pediatric polysomnography requires specialized equipment and measurement algorithms distinct from adult studies, and because home sleep tests are not approved for children, capacity is limited, and clinicians who know their local labs have long waitlists may simply skip the test, a workaround the pediatrics guideline explicitly permits when access is a barrier.

What makes the new findings so consequential is that they provide the empirical baseline that has been missing from that debate. Without knowing where testing happens and for whom, professional groups and policymakers could not judge whether guideline recommendations were feasible or equitable across the country. The authors argue that their data can now inform efforts to reduce variation in the evaluation and treatment of children with sleep-disordered breathing, expand access to polysomnography, and refine referral guidelines to reflect real-world availability. They also caution that Medicaid coverage itself varies by state eligibility policy and by the geography of poverty, so the estimates cannot be used to plan care for individual patients or to reconfigure services in any single locality.

For the millions of American families navigating a child’s sleep problems on public insurance, the study is a stark reminder that a diagnosis considered routine in Michigan may be far harder to obtain in Kansas, California, or Texas, and that the odds of getting tested are shaped by the demographics of the ZIP code a child calls home. The researchers call for further investigation into the availability of pediatric sleep laboratories and the structural barriers, from transportation to provider location data gaps, that keep children from the tests that could change their trajectories. Untangling those barriers, they conclude, is essential if the promise of definitive sleep diagnosis is to reach every child who needs it, regardless of geography or background.

Subject of Research: Geographic and racial/ethnic disparities in pediatric polysomnography utilization among US children enrolled in Medicaid

Article Title: Geographic and racial/ethnic patterns of polysomnography use among children enrolled in Medicaid, 2017–2019

Article References: McLaughlin, C. C., Hawke, J. L., Boss, E. F., Brock, G. M., Lind, M. M., Padamsee, T. J., Pattisapu, P., Chisolm, D. J., & Chavez, L. J. (2026). Geographic and racial/ethnic patterns of polysomnography use among children enrolled in Medicaid, 2017–2019. Journal of Clinical Sleep Medicine, 22(1), Article 128. https://doi.org/10.1007/s44470-026-00149-w

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00149-w

Keywords: polysomnography, pediatric sleep apnea, Medicaid, health disparities, geographic variation, obstructive sleep-disordered breathing, rural health, healthcare access, sleep medicine, CHIP, tonsillectomy, health equity

The letter responds directly to a recent study by Kirkham and colleagues, published in the same journal, that followed children with mild sleep-disordered breathing who were managed with watchful waiting rather than immediate surgery. That study identified prognostic factors associated with progression of the condition, and the Turkish authors contend that such findings are at risk of being misinterpreted. If a child carries a feature that predicts worsening over time, the intuitive leap is that removing the obstructing tonsillar and adenoid tissue early would prevent that worsening, and that high-risk children therefore stand to gain the most from adenotonsillectomy. Yılmaz and his colleagues argue that this leap is logically unsound, and they ground their objection in a well-established framework from clinical epidemiology: the distinction between prognostic and predictive markers.

The distinction was crystallized in a widely cited 2015 paper by Kristel Ballman in the Journal of Clinical Oncology, which the letter authors invoke. A prognostic marker tells you something about the likely course of a disease or condition regardless of which treatment the patient receives. A predictive marker, by contrast, tells you something about the differential effect of a specific treatment, identifying patients in whom the therapy works better or worse than it does in others. A tumor biomarker may signal aggressive disease in every patient who carries it, yet only randomized evidence can show whether that same biomarker identifies patients who respond to a particular drug. The two concepts answer different questions, and conflating them can lead to treatment decisions that are not supported by evidence.

Applied to pediatric sleep medicine, the logic becomes clear. If a child with mild sleep-disordered breathing has characteristics associated with a higher likelihood of progression, those characteristics are prognostic. They suggest the child’s symptoms may worsen whether or not surgery is performed. They do not demonstrate that surgery will produce a larger improvement in that child than in a low-risk child. Only a predictive analysis, ideally within a randomized trial that formally tests for an interaction between the risk factor and treatment assignment, can establish differential treatment benefit. Without such an interaction analysis, claiming that high-risk children derive greater benefit from adenotonsillectomy extrapolates beyond what the data can support.

This is not merely a statistical quibble, because the strongest available evidence on the question comes from a landmark randomized clinical trial. In 2023, Redline and colleagues published in JAMA the results of a randomized trial of adenotonsillectomy for snoring and mild sleep apnea in children, a study that grew out of the Pediatric Adenotonsillectomy Trial for Snoring, or PATS, protocol described by Wang and colleagues in BMJ Open in 2020. That trial randomized children with mild sleep-disordered breathing to surgery or to watchful waiting and evaluated outcomes including behavioral measures and symptom resolution. The letter authors point to this randomized evidence as the appropriate standard against which claims of differential benefit must be tested, rather than observational progression data from cohorts of children managed without surgery.

The stakes of the distinction are considerable. Adenotonsillectomy, while generally safe, is a real surgical intervention carried out under general anesthesia, with associated risks of bleeding, infection, pain and the considerable burden that surgery imposes on children and families. If clinicians begin triaging children toward surgery on the basis of prognostic risk alone, families may be counseled toward an operation on the mistaken understanding that their child is precisely the kind of patient who benefits most. Conversely, if genuine predictive markers of surgical benefit do exist, identifying them properly could spare low-benefit children an unnecessary operation while directing those who would truly gain toward timely treatment. Either way, the answer determines how thousands of pediatric consultations unfold each year.

The watchful waiting study that prompted the letter adds an important empirical dimension to the debate. By demonstrating that many children with mild sleep-disordered breathing who are managed without immediate surgery do not progress, and by characterizing which children are more likely to worsen, it provides clinicians with genuinely useful information about the natural history of the condition. Natural history data of this kind are exactly what prognostic research is designed to deliver. The letter authors do not dispute the value of that data; what they dispute is the inferential step of converting prognosis into treatment guidance. Knowing that a subset of children tends to worsen over time does not reveal whether early surgery would have altered that trajectory, because the children in the watchful waiting cohort were, by design, not operated on.

There is also a broader lesson here for how medical evidence travels. Findings from observational cohorts are frequently repurposed, in review articles, guidelines and clinical conversations, as justification for interventions they were never designed to evaluate. The prognostic-predictive framework offers a simple safeguard: before using a risk marker to justify treatment, ask whether the marker was shown to modify the effect of that treatment in a randomized comparison. In oncology, where the framework was formalized, this discipline has reshaped how biomarkers are validated and how targeted therapies are prescribed. Sleep medicine, the letter suggests, should adopt the same rigor, particularly for a condition as common and as variably managed as mild pediatric sleep-disordered breathing.

For now, the practical message for clinicians and families is one of calibrated caution. Mild sleep-disordered breathing in children sits on a spectrum that ranges from benign primary snoring to frank obstructive sleep apnea, and the decision to operate should rest on the best randomized evidence available, on the severity and impact of symptoms, and on shared decision making that weighs the known benefits and burdens of surgery. A child’s risk of progression is relevant information, but it is information about the disease, not about the treatment. As Yılmaz, Özer and Topal emphasize, until studies demonstrate that specific risk factors predict differential benefit from adenotonsillectomy, prognostic risk alone cannot establish that high-risk children have more to gain from the operation. The distinction may sound like semantics, but in the clinic it is the difference between evidence-based surgery and surgery based on an inferential shortcut.

Subject of Research: The distinction between prognostic and predictive risk markers in deciding adenotonsillectomy benefit for children with mild sleep-disordered breathing

Article Title: Prognostic risk does not establish greater adenotonsillectomy benefit in children with mild sleep-disordered breathing

Article References: Yılmaz, E., Özer, N. K., & Topal, E. (2026). Prognostic risk does not establish greater adenotonsillectomy benefit in children with mild sleep-disordered breathing. Journal of Clinical Sleep Medicine, 22(1), Article 176. https://doi.org/10.1007/s44470-026-00196-3

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00196-3

Keywords: adenotonsillectomy, pediatric sleep apnea, sleep-disordered breathing, prognostic markers, predictive markers, watchful waiting, randomized controlled trial, clinical decision making, snoring, biomarkers, evidence-based medicine, Journal of Clinical Sleep Medicine

The study, led by Dr. Ann M. Murray and Dr. Julie M. Baughn along with colleagues at the Mayo Clinic Center for Sleep Medicine, set out to answer a deceptively simple question. Could a photoplethysmography-based home sleep apnea test, worn as a ring, reliably identify obstructive sleep apnea in young children? Photoplethysmography, or PPG, is an old technology finding new life in consumer wearables. It works by shining light into the skin, usually at the fingertip, and measuring how much light is reflected back. Because blood absorbs light differently depending on how much oxygen it carries and how much of it is pulsing through the vessels, a PPG sensor can track pulse rate, blood oxygen saturation, and subtle changes in the pulse waveform. Software algorithms can then infer breathing disturbances from these signals, since each apnea or hypopnea event leaves a characteristic fingerprint in the pulse and oxygen data.

In adults, this approach has proven accurate enough that home testing is now standard practice for many patients with suspected obstructive sleep apnea. Young children, however, are a different physiological and behavioral challenge. Their breathing patterns are faster, their apnea events are often shorter and more subtle, and their small fingers and restless sleep make sensor placement difficult. The American Academy of Sleep Medicine has issued a position paper emphasizing that home sleep apnea tests in children require rigorous validation before widespread adoption, and previous studies have mostly focused on adolescents rather than preschoolers, the age group in which obstructive sleep apnea peaks due to enlarged tonsils and adenoids.

To test the device under the most favorable possible conditions, the researchers enrolled fifty children aged two to six who had already been referred for polysomnography because of suspected obstructive sleep apnea. Rather than sending the device home, the team asked each child to wear the ring-shaped SleepImage PPG sensor concurrently during their standard in-laboratory sleep study. This design is important: it means the new technology was evaluated side by side with the full polysomnographic apparatus, attended by trained technologists, with no risk of the setup errors or lost nights that plague true home testing. If the device struggled even in this ideal scenario, its performance in an unattended home environment would likely be no better.

The results revealed problems at two distinct levels: feasibility and accuracy. Of the fifty children enrolled, thirty-nine completed testing, and even among these the device did not always deliver usable data. Device-related issues such as sensor detachment, intolerance by the child, and user setup errors precluded successful data collection in eleven percent of studies. An additional twenty-five percent of the recordings captured less than two hours of total sleep time as measured by the PPG device, a duration generally considered too short to characterize a child’s sleep-disordered breathing reliably. In other words, in more than a third of cases the device either failed outright or produced too little data to be clinically meaningful, and this occurred under supervised laboratory conditions rather than in a child’s bedroom.

The technical shortfall showed up starkly in the comparison of sleep duration measurements. Using the automated SleepImage scoring software, the mean difference in total sleep time between the PPG device and full polysomnography was minus 220 minutes, a gap of nearly four hours, with a 95 percent confidence interval of minus 278 to minus 62 minutes and a p value below 0.001. This enormous discrepancy reflects the fact that the PPG-based algorithm could not distinguish sleep from wakefulness in young children the way electroencephalography-based polysomnography can. When the researchers created a limited polysomnography dataset, trimming the laboratory recording to include only the periods when PPG data were available, the mean difference in sleep time shrank to just minus 11 minutes, which was not statistically significant. That comparison confirmed the device was capturing data during the same windows of time; it simply could not tell how much of that time the child was actually asleep.

Diagnostic accuracy proved equally troubling. Using automated scoring, the device showed a sensitivity of 94.1 percent for diagnosing obstructive sleep apnea, meaning it correctly flagged most children who truly had the condition. But the specificity was only 22.7 percent, indicating the device frequently produced positive results in children who did not have obstructive sleep apnea on polysomnography. The positive predictive value was 0.48, meaning that fewer than half of the children flagged by the device actually had the diagnosis confirmed by the gold standard, while the negative predictive value of 0.83 was somewhat stronger. The obstructive apnea-hypopnea index, the key severity metric counting breathing pauses and reductions per hour of sleep, was significantly higher when measured by the PPG device than by either full or limited polysomnography, with p values below 0.001. The device also detected no central apnea events at all, a notable omission because central sleep apnea, in which the brain temporarily stops sending breathing signals, is clinically relevant in young children and cannot be assessed by pulse-based inference alone.

There was one genuine bright spot in the findings. Caregivers reported good ease of use and tolerance of the ring-shaped device, suggesting that the fundamental form factor is acceptable even to toddlers and preschoolers who might resist more cumbersome equipment. This matters because the barriers to pediatric sleep diagnosis are not purely technical; they are also human. Polysomnography requires an overnight stay in a sleep laboratory with dozens of electrodes attached to the scalp, chest, abdomen, and face, an experience that can be distressing for young children and demanding for families. Previous work by some of the same Mayo Clinic investigators has focused on improving the emotional experience of pediatric sleep studies, including the use of certified child life specialists to ease setup. A comfortable, minimally invasive alternative remains an attractive goal, which is precisely why rigorous validation studies like this one are needed before such devices are trusted.

The stakes of getting pediatric sleep apnea diagnosis right are considerable. Obstructive sleep apnea affects a meaningful share of preschool-aged children, with systematic reviews suggesting a notable prevalence in the general population, and it has been linked to behavioral problems, impaired learning, cardiovascular strain, and metabolic consequences. The standard treatment, adenotonsillectomy, has been shown in randomized trials to improve sleep and related outcomes even in children with mild disease. Yet the decision to proceed with surgery in a young child is not one to make on the basis of an inaccurate test. A device that overcalls the diagnosis could send children to unnecessary surgery, while one that misses severity could delay needed treatment. The authors conclude that this particular PPG-based home sleep apnea test was technically inadequate and diagnostically inaccurate in children two to six years of age, and they emphasize that future research must determine the best technology and the appropriate ages of use before any home sleep apnea test can be recommended for widespread use in children. For now, the message for parents and clinicians is clear: when it comes to diagnosing sleep apnea in preschoolers, the overnight laboratory study remains the standard that home gadgets have yet to meet.

Subject of Research: Validation of a photoplethysmography-based home sleep apnea test against polysomnography for diagnosing obstructive sleep apnea in preschool children

Article Title: A photoplethysmography-based home sleep apnea test compared with polysomnography in children 2 to 6 years of age

Article References: Murray, A. M., Matarese, C. A., Lloyd, R. M., Sorensen, C., LeMahieu, A., Reckward, D., Tullis, R., Bellinger, M., Brown, V., Herold, D. L., Morgenthaler, T. I., & Baughn, J. M. (2026). A photoplethysmography-based home sleep apnea test compared with polysomnography in children 2 to 6 years of age. Journal of Clinical Sleep Medicine, 22(1), Article 140. https://doi.org/10.1007/s44470-026-00156-x

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00156-x

Keywords: pediatric sleep apnea, home sleep apnea test, photoplethysmography, polysomnography, preschool children, obstructive sleep apnea, SleepImage, apnea-hypopnea index, sleep medicine, diagnostic accuracy, Mayo Clinic, wearable sensors

The letter, written by Anmol Devi of Ghulam Muhammad Mahar Medical College in Sukkur, Pakistan, examines a recent study by Murray and colleagues that evaluated a photoplethysmography-based home sleep apnea test, known as PPG-HSAT, against polysomnography in children aged two to six years. Polysomnography, or PSG, remains the gold standard for diagnosing obstructive sleep apnea in children, but it is expensive, resource-intensive, and often uncomfortable for young patients. A reliable home alternative would be transformative, allowing clinicians to screen and diagnose far more children in their own sleep environments. The appeal is obvious, which is precisely why Devi argues that the methodological details behind accuracy claims deserve close scrutiny.

The first concern centers on what happens when the home test simply fails to produce usable data. In the original study, 43 children attempted concurrent testing with both the home device and laboratory polysomnography. Four of them, roughly nine percent, had no interpretable PPG-HSAT data at all. Among the 39 children with some captured data, a further 11, or 28 percent, had less than two hours of recorded sleep. Despite these substantial losses, the study calculated sensitivity, specificity, positive predictive value, and negative predictive value only among the children whose home recordings were successfully captured. The headline figures were striking: a sensitivity of 94.1 percent, meaning the device caught nearly all true cases, but a specificity of just 22.7 percent, meaning it flagged many children as positive who did not have the condition by the reference standard.

Devi’s argument is that these numbers may not mean what they appear to mean. In diagnostic accuracy research, excluding missing or inconclusive index-test results is a well-recognized source of bias. When only technically successful recordings are analyzed, the study population becomes a selected subset, potentially healthier in terms of device tolerance or simply easier to monitor, and therefore less representative of the intended clinical population. A device that fails in a quarter of attempted recordings is not the same device, in clinical terms, as one evaluated only where it worked. The reported sensitivity and specificity may reflect performance among technically successful recordings rather than the overall diagnostic effectiveness of the test as it would be deployed in practice.

The solution Devi proposes draws on established methodological literature. An intention-to-diagnose analysis, analogous in spirit to intention-to-treat analysis in therapeutic trials, would classify technically unsuccessful studies as indeterminate or failed tests rather than discarding them. This approach, supported by a scoping review of methods for handling missing values and inconclusive results in diagnostic studies published in Statistical Methods in Medical Research, yields estimates that better reflect real-world performance. It answers the question clinicians actually face: if I prescribe this home test for a toddler suspected of having sleep apnea, how likely am I to get an accurate answer, or any answer at all? A test with excellent accuracy conditional on success but a high failure rate may be far less useful than its published numbers suggest.

The second methodological concern involves the reference standard itself. Polysomnography is only as unbiased as the people interpreting it, and Devi notes that in the original study, PSG technologists and physicians were not blinded to study participation. Moreover, the PSG interpretation incorporated audiovisual findings, including snoring, mouth breathing, and airway-protective maneuvers. These observations are clinically valuable, but they also carry subjective judgment, and their integration into the diagnostic classification opens the door to what the literature calls diagnostic review bias, which arises when interpreters of the reference standard are aware of the index-test result or the investigational context.

The letter acknowledges that the original authors reported the SleepImage data, the commercial PPG-based system under evaluation, were reviewed independently at a later time. That is an important safeguard for the index test. What remains unclear, Devi writes, is whether the PSG interpretation was fully independent of the investigational testing context. If the sleep technologists scoring the reference standard knew that a child was part of a study evaluating a home apnea test, even subtle expectations could color their reading of borderline respiratory events. This may be particularly consequential in pediatric obstructive sleep apnea, where classification often hinges on borderline cases and where clinical and audiovisual findings, such as a parent’s report of snoring or visible mouth breathing, can influence whether a child crosses the diagnostic threshold.

Diagnostic review bias is not a hypothetical worry. Reviews of diagnostic accuracy methodology, including work published in Radiology Research and Practice on recognizing and addressing sources of bias, have repeatedly shown that unblinded reference-standard interpretation can inflate apparent agreement between tests and distort estimates of sensitivity and specificity. In fields like radiology, where imaging studies are routinely compared against clinical reference standards, blinding procedures are now considered a core quality marker of accuracy research. Sleep medicine, Devi implies, should hold itself to the same standard, especially as commercial home-testing platforms seek regulatory approval and clinical adoption on the basis of validation studies.

Importantly, the letter does not claim that the original study’s findings are invalid. Devi explicitly states that these issues do not invalidate the study, but that they may affect the reported estimates of diagnostic performance. The distinction matters. The Murray study addresses an important clinical question, and the underlying data remain valuable. The concern is about interpretation: how far the reported sensitivity of 94.1 percent and specificity of 22.7 percent can be generalized to the population of children who would actually be offered the test, including those whose recordings fail or fall short of adequate sleep duration.

The broader stakes extend well beyond a single study. Pediatric obstructive sleep apnea affects a meaningful share of preschool children and is associated with behavioral problems, poor growth, cardiovascular strain, and impaired quality of life. Untreated, it can shape a child’s development during critical years. Yet access to polysomnography is limited in many regions, and waiting lists for pediatric sleep laboratories can stretch for months. If home sleep apnea tests can be validated rigorously for young children, the public health payoff could be enormous: earlier diagnosis, earlier treatment with adenotonsillectomy or other interventions, and reduced burden on families. That promise is exactly why methodological rigor in validation studies is not an academic nicety but a prerequisite for safe clinical adoption.

Devi’s recommendations are concrete. Future evaluations of pediatric home sleep apnea tests should account for technically unsuccessful recordings, reporting them transparently and incorporating them into intention-to-diagnose analyses, and should ensure independent, ideally blinded, interpretation of the reference standard. Such measures would provide more robust and clinically meaningful estimates of accuracy, allowing clinicians and regulators to judge these devices on the terms that matter: how they perform in the full population of children who would use them, not just the subset in whom they happen to work. As home testing moves from novelty toward standard of care, the letter serves as a timely reminder that in diagnostic medicine, the fine print of methodology often determines whether a promising technology earns its place at the bedside, or in this case, the child’s own bedroom.

Subject of Research: Methodological biases in validating home sleep apnea tests against polysomnography in preschool children

Article Title: Methodological considerations in assessing diagnostic accuracy of home sleep apnea tests in children

Article References: Devi, A. (2026). Methodological considerations in assessing diagnostic accuracy of home sleep apnea tests in children. Journal of Clinical Sleep Medicine, 22(1), Article 159. https://doi.org/10.1007/s44470-026-00185-6

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00185-6

Keywords: home sleep apnea test, pediatric sleep apnea, polysomnography, photoplethysmography, diagnostic accuracy, intention-to-diagnose analysis, diagnostic review bias, PPG-HSAT, children aged 2 to 6, sleep medicine, reference standard blinding, Journal of Clinical Sleep Medicine