Evidence map›Paper›PMID 30853041›Full record

ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2019

Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning.

Hisham Alshaer, Richard Hummel, Monique Mendelson, Travis Marshal, T Douglas Bradley

Registry-linked trialOpen access · bronzeAbstract read
In one paragraph

Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06594627 (Smart Pillows for Enhancing Sleep Quality_Pilot Study), which is not on this map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
3.3field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT06594627 narecruitingnot on this mapstarted 2024, after this paper: background citation

Smart Pillows for Enhancing Sleep Quality_Pilot Study

TypeinterventionalSponsorIndiana UniversityRan2024 to 2027Enrolled24ConditionsSnoring, Obstructive Sleep ApneaArmsSmart pillow
3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 55 citations in OpenAlex.

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  8. Patient centered outcomes of transoral robotic surgery for obstructive sleep apnea: a retrospective analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Hisham AlshaerSleep Research Laboratory and Home and Community Team, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Richard HummelSleep Research Laboratory, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Monique MendelsonSleep Research Laboratory, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
Travis MarshalSleep Research Laboratory, Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada.
T Douglas BradleySleep Research Laboratories of the Toronto Rehabilitation Institute and Toronto General Hospital, University Health Network, Toronto, Ontario, Canada.
University Health Network · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

STUDY

objectivesSnoring is perceived to be directly proportional to sleep apnea severity, especially obstructive sleep apnea (OSA), but this notion has not been thoroughly and objectively evaluated, despite its popularity in clinical practice. This might lead to overdiagnosis or underdiagnosis of OSA. The goal of this study is to examine this notion and objectively quantify the relationship between sleep apnea and snoring detected using advanced signal processing algorithms.

methodsWe studied adults referred for polysomnography, from which the apnea-hypopnea index (AHI) was derived. Breath sounds were recorded simultaneously, from which snoring was accurately quantified using acoustic analysis of breath sounds and machine-learning computer algorithms. The snore index (SI) was calculated as the number of snores per hour of sleep.

resultsIn 235 patients, the mean AHI was 20.2 ± 18.8 and mean SI was 320.2 ± 266.7 events/h. On the one hand, the overall correlation between SI and AHI was weak but significant (

conclusionsSnoring on its own is probably of limited usefulness in assessing sleep apnea presence and severity, because of its weak relationship with AHI. Thus, the complaint of snoring should be interpreted with caution to avoid unnecessary referrals for sleep apnea testing. Conversely, clinicians should be aware of the possibility of missing diagnosis of patients with sleep apnea who have minimal snoring.

Indexed as

Machine LearningAlgorithmsFemaleHumansMaleMiddle AgedPolysomnographySeverity of Illness IndexSleep Apnea, ObstructiveSleep Apnea SyndromesSnoringapnea-hypopnea indexfrequency of snoringmachine learningsleep apnea

Identifiers

PMID30853041
PMCPMC6411174
OpenAlexW2922189800

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.