Orofacial myofunctional therapy (OMT) is one of the therapeutic methods for neuromuscular re-education and has been considered as one of the auxiliary methods for obstructive sleep apnea hypopnea syndrome (OSAHS) and orthodontic treatment. There is a dearth of comprehensive analysis of OMT’s effects on muscle morphology and function. This systematic review examines the literature on the craniomaxillofacial effects of OMT in children with OSAHS. This systematic analysis was carried out using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standards, and the research was scanned using PICO principles. A total of 1776 articles were retrieved within a limited time, with 146 papers accepted for full-text perusing following preliminary inspection and 9 of those ultimately included in the qualitative analysis. Three studies were rated as having a severe bias risk, and five studies were rated as having a moderate bias risk. Improvement in craniofacial function or morphology was observed in most of the 693 children. OMT can improve the function or morphology of the craniofacial surface of children with OSAHS, and its effect becomes more significant as the duration of the intervention increases and compliance improves. In the majority of the 693 infants, improvements in craniofacial function or morphology were seen. The function or morphology of a kid’s craniofacial surface can be improved with OMT, and as the duration of the intervention lengthens and compliance rises, the impact becomes more pronounced.

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.