Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea Severity in Children

Abstract

Purpose

The clinical presentation of Obstructive Sleep Apnea (OSA) in children is insidious and harmful. Early identification of children with OSA, particularly those at a higher risk for severe symptoms, is essential for making informed clinical decisions and improving long-term outcomes. Therefore, we developed and validated a risk prediction model for severity in Chinese children with OSA to effectively identify children with moderate-to-severe OSA in a clinical setting.

Patients and Methods

From June 2023 to September 2023, we retrospectively analyzed the medical records of 367 Children diagnosed with OSA through portable bedside polysomnography (PSG). Predictor variables were screened using the least absolute shrinkage and selection operator (LASSO) and logistic regression techniques to construct nomogram to predict the severity of OSA. Receiver operating characteristic curve (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.

Results

A total of 367 children with a median age of 84 months were included in this study. Neck circumference, ANB, gender, learning problem, and level of obstruction were identified as independent risk factors for moderate-severe OSA. The consistency indices of the nomogram in the training and validation cohorts were 0.841 and 0.75, respectively. The nomogram demonstrated a strong concordance between the predicted probabilities and the observed probabilities for children diagnosed with moderate-severe OSA. With threshold probabilities ranging from 0.1 to 1.0, the predictive model demonstrated strong predictive efficacy and yielded improved net benefit for clinical decision-making. ROC analysis was employed to classify the children into high and low-risk groups, utilizing the Optimal Cutoff value of 0.39.

Conclusion

A predictive model using LASSO regression was developed and validated for children with varying levels of OSA. This model identifies children at risk of developing OSA at an early stage.

Keywords:

• obstructive sleep apnea
• children
• cephalometric
• prediction nomogram
• risk prediction model

Abbreviations

OSA, Obstructive sleep apnea; CPM, Clinical predictive models; DCA, Decision Curve Analysis; ROC, Receiver operating characteristic curve; CIC, Clinical impact curve; AUC, Area Under Curve; LASSO, Least Absolute Selection and Shrinkage Operator; OMES, Protocol of orofacial myofunctional evaluation with scores; OSA-18, Obstructive sleep apnea 18 items survey scores; CSHQ, Children’s Sleep Habits Questionnaire; BMI, Body mass index; OMB, Obstructive mouth breathing; ANB, the angle between 3 point landmarks, A point, N and B point, determining the anteroposterior relation between maxilla and the mandible relative to the cranium; PM, Portable monitors.

Data Sharing Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Informed Consent

The studies involving human participants were reviewed and approved by the Ethics Committee of Children’s Hospital of Chongqing Medical University (No. 66, 2023). China Clinical Trial Registration Center registration number: ChiCTR2300068094. The study was conducted with the informed consent was obtained from all subjects and/or their legal guardian(s) before the investigation, and the study will not cause any harm to the child or his family. Personal information of the children will be kept strictly confidential unless required by law. All data from this study were published with the consent of the participants.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

There is no funding to report.