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Awake Multimodal Phenotyping for Prediction of Oral Appliance Treatment Outcome

机译:唤醒多模态表型预测口腔矫治器治疗的结果

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Study Objectives:An oral appliance (OA) is a validated treatment for obstructive sleep apnea (OSA). However, therapeutic response is not certain in any individual and is a clinical barrier to implementing this form of therapy. Therefore, accurate and clinically applicable prediction methods are needed. The goal of this study was to derive prediction models based on multiple awake assessments capturing different aspects of the pharyngeal response to mandibular advancement. We hypothesized that a multimodal model would provide robust prediction.Methods:Patients with OSA (apnea-hypopnea index [AHI] 10 events/h) were recruited for treatment with a customized OA (n = 142, 59% male). Participants underwent facial photography (craniofacial structure), spirometry (mid-inspiratory flow at 50% vital capacity [MIF50] and mid-expiratory flow at 50% vital capacity [MEF50] and the ratio MEF50/MIF50) and nasopharyngoscopy (velopharyngeal collapse with Mueller maneuver and mandibular advancement). Treatment response was defined by 3 criteria: (1) AHI 5 events/h plus 50% reduction, (2) AHI 10 events/h plus 50% reduction, (3) 50% AHI reduction. Multivariable regression models were used to assess predictive utility of phenotypic assessments compared to clinical characteristics alone (age, sex, obesity, baseline AHI).Results:Craniofacial structure and flow-volume loops predicted treatment response. Accuracy of the prediction models (area under the receiver operating characteristic curve) for each criterion were 0.90 (criterion 1), 0.79 (criterion 2), and 0.78 (criterion 3). However, these prediction models including phenotypic assessments did not provide a statistically significant improvement over clinical predictors only.Conclusions:Multimodal awake phenotyping does not enhance OA treatment outcome prediction. These office-based, awake assessments have limited utility for robust clinical prediction models. Future work should focus on sleep-related assessments.
机译:研究目标:口腔矫治器(OA)是一种有效的阻塞性睡眠呼吸暂停(OSA)治疗方法。然而,治疗反应在任何个体中都是不确定的,并且是实施这种治疗形式的临床障碍。因此,需要准确且临床上可应用的预测方法。这项研究的目的是基于多个清醒评估得出预测模型,该评估模型捕获咽部对下颌前移反应的不同方面。我们假设多模式模型将提供可靠的预测。方法:招募OSA(呼吸暂停-呼吸不足指数[AHI]> 10事件/ h)的患者,以定制的OA治疗(n = 142,男性占59%)。参与者进行了面部摄影(颅面结构),肺活量测定(50%肺活量[MIF50]时吸气中流量和50%肺活量[MEF50]和MEF50 / MIF50的比值)和鼻咽镜检查(Mueller咽喉塌陷)动作和下颌前移)。治疗反应由3个标准定义:(1)AHI 5事件/小时加50%降低;(2)AHI 10事件/小时加50%降低;(3)AHI降低50%。与单独的临床特征(年龄,性别,肥胖,基线AHI)相比,使用多变量回归模型评估表型评估的预测效用。结果:颅面结构和流量环可预测治疗反应。每个标准的预测模型(接收器工作特性曲线下的区域)的准确性分别为0.90(标准1),0.79(标准2)和0.78(标准3)。然而,包括表型评估在内的这些预测模型仅在临床预测指标上并未提供统计学上的显着改善。结论:清醒多模式表型不能增强OA治疗结果的预测。这些基于办公室的清醒评估对强大的临床预测模型的实用性有限。未来的工作应集中在与睡眠有关的评估上。

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