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一组八个关键问题有助于对常见前庭疾病进行分类——来自DizzyReg患者登记处的结果。

A Set of Eight Key Questions Helps to Classify Common Vestibular Disorders-Results From the DizzyReg Patient Registry.

作者信息

Strobl Ralf, Grözinger Michael, Zwergal Andreas, Huppert Doreen, Filippopulos Filipp, Grill Eva

机构信息

Institute for Medical Information Processing, Biometrics and Epidemiology, Ludwig-Maximilians-Universität München (LMU) Munich, Munich, Germany.

German Center for Vertigo and Balance Disorders, University Hospital Munich, Ludwig-Maximilians-Universität München (LMU) Munich, Munich, Germany.

出版信息

Front Neurol. 2021 Apr 29;12:670944. doi: 10.3389/fneur.2021.670944. eCollection 2021.

Abstract

Precise history taking is the key to develop a first assumption on the diagnosis of vestibular disorders. Particularly in the primary care setting, algorithms are needed, which are based on a small number of questions and variables only to guide appropriate diagnostic decisions. The aim of this study is to identify a set of such key variables that can be used for preliminary classification of the most common vestibular disorders. A four-step approach was implemented to achieve this aim: (1) we conducted an online expert survey to collect variables that are meaningful for medical history taking, (2) we used qualitative content analysis to structure these variables, (3) we identified matching variables of the patient registry of the German Center for Vertigo and Balance Disorders, and (4) we used classification trees to build a classification model based on these identified variables and to analyze if and how these variables contribute to the classification of common vestibular disorders. We included a total of 1,066 patients with seven common vestibular disorders (mean age of 51.1 years, SD = 15.3, 56% female). Functional dizziness was the most frequent diagnosis (32.5%), followed by vestibular migraine (20.2%) and Menière's disease (13.3%). Using classification trees, we identified eight key variables which can differentiate the seven vestibular disorders with an accuracy of almost 50%. The key questions comprised attack duration, rotational vertigo, hearing problems, turning in bed as a trigger, doing sport or heavy household chores as a trigger, age, having problems with walking in the dark, and vomiting. The presented algorithm showed a high-face validity and can be helpful for taking initial medical history in patients with vertigo and dizziness. Further research is required to evaluate if the identified algorithm can be applied in the primary care setting and to evaluate its external validity.

摘要

准确的病史采集是对前庭疾病诊断形成初步假设的关键。特别是在初级保健环境中,需要基于少量问题和变量的算法来指导适当的诊断决策。本研究的目的是确定一组可用于对最常见前庭疾病进行初步分类的关键变量。为实现这一目标,实施了四步方法:(1)我们进行了一项在线专家调查,以收集对病史采集有意义的变量;(2)我们使用定性内容分析来构建这些变量;(3)我们确定了德国眩晕与平衡障碍中心患者登记处的匹配变量;(4)我们使用分类树基于这些确定的变量构建分类模型,并分析这些变量是否以及如何有助于常见前庭疾病的分类。我们共纳入了1066例患有七种常见前庭疾病的患者(平均年龄51.1岁,标准差=15.3,56%为女性)。功能性头晕是最常见的诊断(32.5%),其次是前庭性偏头痛(20.2%)和梅尼埃病(13.3%)。使用分类树,我们确定了八个关键变量,这些变量可以以近50%的准确率区分七种前庭疾病。关键问题包括发作持续时间、旋转性眩晕

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d23/8116658/a85a13820ea3/fneur-12-670944-g0001.jpg

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