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

作者全名："Liu, Yue; Xie, Shi Qi; Yang, Xia; Chen, Jing Lan; Zhou, Jian Rong"

作者地址："[Liu, Yue; Xie, Shi Qi; Yang, Xia; Chen, Jing Lan; Zhou, Jian Rong] Chongqing Med Univ, Sch Nursing, Chongqing, Peoples R China; [Xie, Shi Qi; Zhou, Jian Rong] Chongqing Med Univ, Sch Nursing, 1 Med Coll Rd, Chongqing 400016, Peoples R China"

通信作者："Xie, SQ; Zhou, JR (通讯作者)，Chongqing Med Univ, Sch Nursing, 1 Med Coll Rd, Chongqing 400016, Peoples R China."

来源：NATURE AND SCIENCE OF SLEEP

ESI学科分类：NEUROSCIENCE & BEHAVIOR

WOS号：WOS:001170768500001

JCR分区：Q2

影响因子：3.4

年份：2024

卷号：16

期号：　

开始页：193

结束页：206

文献类型：Article

关键词：obstructive sleep apnea; children; cephalometric; prediction nomogram; risk prediction model

摘要："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."

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