Consensus-Based Attributes for Identifying Patients With Spasmodic Dysphonia and Other Voice Disorders
Consensus-Based Attributes for Identifying Patients With Spasmodic Dysphonia and Other Voice Disorders
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DOI：
10.1001/jamaoto.2018.0644
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发表时间：
2018-08-01
影响因子：
7.8
通讯作者：
Stebbins, Glenn
中科院分区：
文献类型：
--
作者：
Ludlow, Christy L.;Domangue, Rickie;Stebbins, Glenn
重要性内收肌痉挛发音障碍(ADSD)、外展肌痉挛障碍(ABSD)、语音震颤(VT)和肌肉紧张性发音障碍(MID)研究的障碍是缺乏选择这些疾病患者的标准。目的确定专家之间的一致性，而不是使用标准指南来对ABSD、ADSD、VT和MTD患者进行分类，并制定专家共识属性来对患者进行分类以进行研究。设计、背景和参与者从2011年到2016年，一项多中心观察性研究考察了盲目专家对ADSD患者进行分类时的一致性。ABSD、VT或MTD(第一项研究)。随后，一项由专家小组和46名社区专家进行的重复阶段的德尔菲法研究，就用于对患有4种疾病的患者进行分类的属性达成共识(第二项研究)。这项研究使用了178名临床诊断为ADSD、ABSD、VT MTD、声带麻痹/瘫痪、心理性嗓音障碍或继发于帕金森病的低音患者的便利样本。参与者年龄在18岁或以上，没有喉部结构疾病或ADSD手术，按照标准方案进行了语音和鼻咽镜视频记录。EXPOSURES语音和鼻咽镜视频记录遵循标准协议。MAIN结果和测量4个站点的专家将178名患者分为11类。四位国际专家在观看了演讲和鼻咽镜视频记录后，在没有指导的情况下，使用相同的类别独立地对75名患者进行了分类。来自4个站点的每个成员在观看了语音/喉咙任务的视频剪辑后，还对来自其他站点的50名患者进行了分类。中间评分者K<040表示评分者对之间和招聘网站之间的分类一致性较差。因此，一个由13名专家组成的德尔福小组对ADSD、ABSD、VT和MTD的语音和喉部运动属性进行了识别和排序，并由46名社区专家进行了审查。结果在没有指南的情况下对患者进行分类时，评分者的分类分布不同(似然比，chi(2)=107.66)，评价者之间的一致性较差，与站点类别的一致性也较差。对于11个类别，最高符合率为34%，K值均未超过0.26。在外部评分者对中，最高K值为0.23，最高一致性为38.5%。使用6个类别，最高符合率为73.3%，最高K值为0.40。Delphi方法得到了18个用于分类语音和鼻咽镜检查疾病的属性。结论在对患者进行分类研究时，没有指南的相关专家的一致性很差，导致基于Delphi的痉挛性发音障碍属性库的开发，用于对ADSD、ABSD、VT和MTD患者进行分类研究。
IMPORTANCE A roadblock for research on adductor spasmodic dysphonia (ADSD), abductor SD (ABSD), voice tremor (VT), and muscular tension dysphonia (MID) is the lack of criteria for selecting patients with these disorders.OBJECTIVE To determine the agreement among experts not using standard guidelines to classify patients with ABSD, ADSD, VT, and MTD, and develop expert consensus attributes for classifying patients for research.DESIGN, SETTING AND PARTICIPANTS From 2011 to 2016, a multicenter observational study examined agreement among blinded experts when classifying patients with ADSD. ABSD, VT or MTD (first study). Subsequently, a 4-stage Delphi method study used reiterative stages of review by an expert panel and 46 community experts to develop consensus on attributes to be used for classifying patients with the 4 disorders (second study). The study used a convenience sample of 178 patients clinically diagnosed with ADSD, ABSD, VT MTD, vocal fold paresis/paralysis, psychogenic voice disorders, or hypophonia secondary to Parkinson disease. Participants were aged 18 years or older, without laryngeal structural disease or surgery for ADSD and underwent speech and nasolaryngoscopy video recordings following a standard protocol.EXPOSURES Speech and nasolaryngoscopy video recordings following a standard protocol.MAIN OUTCOMES AND MEASURES Specialists at 4 sites classified 178 patients into 11 categories. Four international experts independently classified 75 patients using the same categories without guidelines after viewing speech and nasolaryngoscopy video recordings. Each member from the 4 sites also classified 50 patients from other sites after viewing video clips of voice/laryngeal tasks. Interrater K less than 040 indicated poor classification agreement among rater pairs and across recruiting sites. Consequently, a Delphi panel of 13 experts identified and ranked speech and laryngeal movement attributes for classifying ADSD, ABSD, VT, and MTD, which were reviewed by 46 community specialists. Based on the median attribute rankings, a final attribute list was created for each disorder.RESULTS When classifying patients without guidelines, raters differed in their classification distributions (likelihood ratio, chi(2) = 107.66), had poor interrater agreement, and poor agreement with site categories. For 11 categories, the highest agreement was 34%, with no K values greater than 0.26. In external rater pairs, the highest K was 0.23 and the highest agreement was 38.5%. Using 6 categories, the highest percent agreement was 73.3% and the highest K was 0.40. The Delphi method yielded 18 attributes for classifying disorders from speech and nasolaryngoscopic examinations.CONCLUSIONS AND RELEVANCE Specialists without guidelines had poor agreement when classifying patients for research, leading to a Delphi-based development of the Spasmodic Dysphonia Attributes Inventory for classifying patients with ADSD, ABSD, VT, and MTD for research.