Awake Multimodal Phenotyping for Prediction of Oral Appliance Treatment Outcome.
Awake Multimodal Phenotyping for Prediction of Oral Appliance Treatment Outcome.
复制标题
用于预测口腔矫治器治疗结果的清醒多模式表型分析。
DOI：
10.5664/jcsm.7484
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
P. Cistulli
中科院分区：
文献类型：
--
作者：
K. Sutherland;Andrew S. L. Chan;J. Ngiam;O. Dalci;M. Darendeliler;P. Cistulli
研究目的
口腔矫治器（OA）是一种有效的治疗阻塞性睡眠呼吸暂停（OSA）的方法。然而，治疗反应在任何个体中都是不确定的，并且是实施这种形式的治疗的临床障碍。因此，需要准确和临床适用的预测方法。本研究的目的是根据多个清醒评估获取下颌前移时咽部反应的不同方面来推导预测模型。我们假设多模态模型将提供稳健的预测。
方法
OSA患者（呼吸暂停低通气指数[AHI] > 10次/h）被招募接受定制OA治疗（n = 142，59%为男性）。参与者接受了面部摄影（颅面结构）、肺功能测定（50%肺活量时的吸气中期流量[MIF 50]和50%肺活量时的呼气中期流量[MEF 50]以及MEF 50/MIF 50的比值）和鼻咽镜检查（Mueller手法和下颌前移导致的鼻咽塌陷）。治疗反应由3个标准定义：（1）AHI < 5起事件/h+减少≥ 50%，（2）AHI < 10起事件/h+减少≥ 50%，（3）AHI减少≥ 50%。多变量回归模型用于评估表型评估与单独的临床特征（年龄、性别、肥胖、基线AHI）相比的预测效用。
结果
颅面结构和流量-容积循环可预测治疗反应。每个标准的预测模型的准确度（受试者工作特征曲线下面积）分别为0.90（标准1）、0.79（标准2）和0.78（标准3）。然而，这些预测模型，包括表型评估没有提供一个统计学上的显着改善临床预测。
结论
多模式清醒表型不能增强OA治疗结果预测。这些基于办公室的清醒评估对于稳健的临床预测模型的实用性有限。未来的工作应侧重于睡眠相关的评估。
评注
关于这篇文章的评论出现在本期的第1837页。
临床试验注册
登记处：澳大利亚和新西兰临床试验注册中心，标题：阻塞性睡眠呼吸暂停患者口腔矫治器治疗结局的多模式表型分析，标识符：ACTRN 12611000409976，URL：https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx? id=336663。
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.
COMMENTARY
A commentary on this article appears in this issue on page 1837.
CLINICAL TRIAL REGISTRATION
Registry: Australian New Zealand Clinical Trials Registry, Title: Multimodal phenotyping for the prediction of oral appliance treatment outcome in obstructive sleep apnoea, Identifier: ACTRN12611000409976, URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336663.
影响因子：
5
作者：
Peppard, Paul E.;Young, Terry;Hla, Khin Mae
通讯作者：
Hla, Khin Mae
DOI：
10.1164/rccm.201601-0099oc
发表时间：
2016-12-01
影响因子：
24.7
作者：
Edwards, Bradley A.;Andara, Christopher;Wellman, Andrew
通讯作者：
Wellman, Andrew