Evidence map›Paper›PMID 41665698›Full record

ArticleSleep & breathing = Schlaf & Atmung2026

Development and validation of a diagnostic model for obstructive sleep apnea.

Fang Wu, Shuai Yu, Peng-Jiao Xu, Xiao-Yang Zhang, Rong-Jie He, Ya-Nan Guo, Ling-Zhao Yan, Chao Wang, Ya-Hui Xu

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Fang Wu *Department of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Shuai Yu *Department of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Peng-Jiao XuDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Xiao-Yang ZhangDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Rong-Jie HeDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Ya-Nan GuoDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Ling-Zhao YanDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China.
Chao WangDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China. wangchao_03@126.com.
Ya-Hui XuDepartment of Sleep Medicine, Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, Brain Institute, The Second Affiliated Hospital of Xinxiang Medical University, Henan Academy of Innovations in Medical Science, Xinxiang, Henan, 453002, China. 18737354427@163.com.

Funding

Henan Provincial Science and Technology Research Project 235101610004Open project of Henan Collaborative Innovation Center for Prevention and Treatment of Mental Disorders, the Second Affiliated Hospital of Xinxiang Medical University XTkf06Open Project of Mental and Psychological Disease Clinical Medical Research Center of Henan Province 2020-zxkfkt-007
6 · The paper itself

Abstract

STUDY

objectivesObstructive sleep apnea (OSA) is a prevalent, underdiagnosed disorder linked to serious health risks. This study developed a diagnostic model for OSA.

methodsClinical data were analyzed from 3,038 adults who underwent polysomnography at the Second Affiliated Hospital of Xinxiang Medical University between 2015 and 2024. Sixteen candidate predictors were initially considered and a 70:30 split was used to divide participants into training and validation groups. A multivariate logistic regression model was developed to predict OSA risk. Model performance was assessed using the area under the receiver operating characteristic curve, calibration plots, Brier score, and decision curve analysis.

resultsThe model included four predictors of OSA (age, longest apnea time, lowest oxygen saturation, and oxygen desaturation index). The model demonstrated high accuracy with the area under the receiver operating characteristic curve values of 0.957 (95% CI: 0.945–0.969) in the training group and 0.945 (95% CI: 0.923–0.967) in the validation group. Brier scores were 0.071 and 0.081, with calibration plots showing excellent agreement between predicted and observed probabilities. Decision curve analysis confirmed clinical usefulness.

conclusionsThis study developed a highly accurate OSA diagnostic model to support clinical decision-making following polysomnography. Further validation in diverse populations is warranted to optimize its broad applicability.

Indexed as

Sleep Apnea, ObstructiveAdultFemaleHumansMaleMiddle AgedPolysomnographyDiagnostic modelNomogramObstructive sleep apneaPredictors

Identifiers

PMID41665698

What Socratic holds

Textmetadata
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Registered trials

None linked

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 Socratic graph.