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Erschienen in: Sleep and Breathing 2/2022

17.06.2021 | Epidemiology • Original Article

Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters

verfasst von: Guo-qiang Song, De-lu Wang, Hua-man Wu, Qiao-jun Wang, Fei Han, Guo-qiang Hu, Rui Chen

Erschienen in: Sleep and Breathing | Ausgabe 2/2022

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Abstract

Background and objective

The 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.

Methods

The 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).

Results

Based on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m2, with an area under the curve (AUC) of 0.7213 (95% confidence interval (CI), 0.6861–0.7566), a sensitivity of 57% and a specificity of 78%. Through forward conditional logistic regression analysis using a stepwise selection model developed from observed data, seven clinical variables were evaluated as independent predictors of OSA: age, BMI, sex, Epworth Sleepiness Scale score, witnessed apnoeas, dry mouth and arrhythmias. With this new model, the AUC was 0.7991 (95% CI, 0.7668–0.8314) for diagnosing OSA (sensitivity, 75%; specificity, 71%). The results were confirmed using the validation set. A nomogram for predicting OSA was generated based on this new model using statistical software.

Conclusions

BMI 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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Metadaten
Titel
Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters
verfasst von
Guo-qiang Song
De-lu Wang
Hua-man Wu
Qiao-jun Wang
Fei Han
Guo-qiang Hu
Rui Chen
Publikationsdatum
17.06.2021
Verlag
Springer International Publishing
Erschienen in
Sleep and Breathing / Ausgabe 2/2022
Print ISSN: 1520-9512
Elektronische ISSN: 1522-1709
DOI
https://doi.org/10.1007/s11325-021-02347-7

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