ArticleSleep & breathing = Schlaf & Atmung2022
Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters.
Article in Sleep & breathing = Schlaf & Atmung, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Development and Validation of Prediction Models for Severe Obstructive Sleep Apnea Based on Periodic Health Examinations.The clinical respiratory journal · 2026Article
- Optimizing Obstructive Sleep Apnea Risk Assessment in Hypertension: Development of a Predictive Nomogram in China.Nature and science of sleep · 2025Article
- Development and validation of a genomic nomogram based on a ceRNA network for comprehensive analysis of obstructive sleep apnea.Frontiers in genetics · 2023Article
- Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea.Frontiers in neuroscience · 2022Article
- Study on the correlation between the transverse dimension of maxilla and obstructive sleep apnea.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2021Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectiveThe diagnosis of obstructive sleep apnea (OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA.
methodsThe effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants).
resultsBased on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m
conclusionsBMI can be used as an indicator to screen for OSA in the community. We created an internally validated, highly distinguishable, visual and parsimonious prediction model comprising BMI and other parameters that can be used to identify patients with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.
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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.