Dystonia Center and Movement Disorders Unit, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Functional Neurological Disorder Research Program, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Ann Clin Transl Neurol. 2021 Apr;8(4):732-748. doi: 10.1002/acn3.51307. Epub 2021 Mar 16.
Functional dystonia (FD) is a disabling and diagnostically challenging functional movement disorder (FMD). We sought to identify historical predictors of FD vs. other primary dystonias (ODs) and develop a practical prediction algorithm to guide neurologists.
1475 consecutive new patient medical records were reviewed at an adult/pediatric tertiary-referral dystonia clinic from 2005 to 2017. Ninety-nine met criteria for clinically established FD (85 adults and 14 pediatric), paired with 99 age/dystonia distribution-matched OD. Univariate and multivariate regression analyses were performed to identify predictors of FD and disability. We formed a prediction algorithm, assessed using the area under the receiver operating curve (AUC).
Multivariate logistic regression analysis investigating independent predictors of FD (P < 0.001) followed by development of a prediction algorithm showed that the most robust predictors included abrupt onset, spontaneous resolution/recurrence, pain, cognitive complaints, being on or pursuing disability, lifetime mood/anxiety disorder, comorbid functional somatic disorders, and having ≥3 medication allergies. The prediction algorithm had utility for both adult and pediatric FD, with excellent sensitivity/specificity (89%/92%) and an area under the curve (AUC) 0.95 (0.92-0.98). Greater disability (modified Rankin Scale) independently correlated with a number of functional examination features, unemployment/not attending school, number of medication allergies, and younger age of presentation. FD patients were high health-care utilizers and were more frequently prescribed opiates/opioids and benzodiazepines (P < 0.003).
This case-control study provides an algorithm to guide clinicians in gauging their index of suspicion for a FD, with diagnostic confirmation subsequently informed by neurological examination. While this algorithm requires prospective validation, health-care utilization data underscore the importance and need for more research in FD.
功能性运动障碍（FD）是一种致残且具有挑战性的功能性运动障碍（FMD）。我们试图确定 FD 与其他原发性运动障碍（OD）的历史预测因素，并开发一种实用的预测算法来指导神经科医生。
2005 年至 2017 年，在一家成人/儿科三级转诊治疗肌张力障碍的诊所对 1475 例连续新患者的病历进行了回顾。99 例符合临床确诊 FD 的标准（85 例成人和 14 例儿科），与 99 例年龄/肌张力障碍分布匹配的 OD 配对。进行单变量和多变量回归分析，以确定 FD 和残疾的预测因素。我们形成了一种预测算法，并用接收者操作特征曲线下的面积（AUC）进行评估。
多变量逻辑回归分析调查了 FD 的独立预测因素（P<0.001），随后开发了一种预测算法，结果表明最具预测性的因素包括突然发作、自发性缓解/复发、疼痛、认知障碍、正在服用或正在寻求残疾、终生心境/焦虑障碍、合并功能性躯体障碍以及有≥3 种药物过敏。该预测算法对成人和儿科 FD 均有效，具有良好的敏感性/特异性（89%/92%）和曲线下面积（AUC）为 0.95（0.92-0.98）。更大的残疾（改良 Rankin 量表）与多项功能检查特征、失业/不上学、药物过敏次数以及发病年龄较小独立相关。FD 患者是高医疗保健使用者，更常开阿片类药物/类阿片药物和苯二氮䓬类药物（P<0.003）。
这项病例对照研究提供了一种算法，可以帮助临床医生评估他们对 FD 的怀疑指数，随后通过神经检查来确认诊断。虽然该算法需要前瞻性验证，但医疗保健利用数据强调了在 FD 中进行更多研究的重要性和必要性。
