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. 2019 Jan 18;19(1):18.
doi: 10.1186/s12890-019-0782-1.

Development and validation of a simple-to-use clinical nomogram for predicting obstructive sleep apnea

Huajun Xu  1   2   3 , Xiaolong Zhao  1   2 , Yue Shi  4 , Xinyi Li  1   2   3 , Yingjun Qian  1   2   3 , Jianyin Zou  1   2   3 , Hongliang Yi  5   6   7 , Hengye Huang  8 , Jian Guan  9   10   11 , Shankai Yin  1   2   3
Affiliations

Development and validation of a simple-to-use clinical nomogram for predicting obstructive sleep apnea

Huajun Xu et al. BMC Pulm Med. .

Abstract

Background: The high cost and low availability of polysomnography (PSG) limits the timely diagnosis of OSA. Herein, we developed and validated a simple-to-use nomogram for predicting OSA.

Methods: We collected and analyzed the cross-sectional data of 4162 participants with suspected OSA, seen at our sleep center between 2007 and 2016. Demographic, biochemical and anthropometric data, as well as sleep parameters were obtained. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality, select factors, and construct the nomogram. The performance of the nomogram was assessed using calibration and discrimination. Internal validation was also performed.

Results: The LASSO regression analysis identified age, sex, body mass index, neck circumference, waist circumference, glucose, insulin, and apolipoprotein B as significant predictive factors of OSA. Our nomogram model showed good discrimination and calibration in terms of predicting OSA, and had a C-index value of 0.839 according to the internal validation. Discrimination and calibration in the validation group was also good (C-index = 0.820). The nomogram identified individuals at risk for OSA with an area under the curve (AUC) of 0.84 [95% confidence interval (CI), 0.83-0.86].

Conclusions: Our simple-to-use nomogram is not intended to replace standard PSG, but will help physicians better make decisions on PSG arrangement for the patients referred to sleep center.

Keywords: Nomogram; Obstructive sleep apnea; Risk factor.

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Conflict of interest statement

Ethics approval and consent to participate

The study was conducted according to the World Medical Association Declaration of Helsinki in 1975, as revised in 1983, and was approved by the Ethic Committee of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital. All subjects provided their informed written consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Factor selection using the LASSO logistic regression model. a LASSO coefficients of 18 candidate variables. b Identification of the optimal penalization coefficient (λ) in the LASSO model was achieved by 10-fold cross-validation and the minimum criterion. The left vertical line represents the minimum error, and the right vertical line represents the cross-validated error within 1 standard error of the minimum. LASSO, least absolute shrinkage and selection operator
Fig. 2
Fig. 2
Our simple-to-use nomogram established for predicting OSA. The nomogram was developed in the training group by incorporating the following eight parameters: age (years), sex (0 = male; 1 = female), glucose (mmol/L), ApoB (g/L), insulin (μU/mL), BMI (kg/m2), NC (cm), and WC (cm). OSA, obstructive sleep apnea; ApoB, apolipoprotein B; NC, neck circumference; WC, waist circumference; BMI, body mass index
Fig. 3
Fig. 3
Calibration curves of the nomogram. a Calibration curve for the nomogram in the training group. b Calibration curve for the nomogram in the validation group
Fig. 4
Fig. 4
The LASSO feature regression. Standardized total score for each participant in the training group. Green bars represent scores for subjects without OSA, and red bars represent scores for those with OSA
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