School of Health and Rehabilitation Sciences, University of Queensland, QLD, Australia.
School of Health Sciences, The University of Newcastle, NSW, Australia.
J Man Manip Ther. 2024 Apr;32(2):173-181. doi: 10.1080/10669817.2023.2250164. Epub 2023 Aug 31.
Cervical arterial dissection (CAD) is an important cause of stroke in young people which may be missed because early features may mimic migraine or a musculoskeletal presentation. The study aimed to develop a diagnostic support tool for early identification of CAD.
Retrospective observational study.
Tertiary hospital.
Radiologically confirmed CAD cases ( = 37), non-CAD stroke cases ( = 20), and healthy controls ( = 100).
The presence of CAD is confirmed with imaging. Predictive variables included risk factors and clinical characteristics of CAD. Variables with a p-value <0.2 included in a multivariable model. Predictive utility of the model is assessed by calculating area underthe ROC curve (AUC).
The model including four variables: age 40-55 years (vs < 40), trauma, recent onset headache, and > 2 neurological features, demonstrated excellent discrimination: AUC of 0.953 (95% CI: 0.916, 0.987). A predictive scoring system (total score/7) identified an optimal threshold of ≥ 3 points, with a sensitivity of 87% and specificity of 79%.
The study identified a diagnostic support tool with four variables to predict increased risk of CAD. Validation in a clinical sample is needed to confirm variables and refine descriptors to enable clinicians to efficiently apply the tool.Optimum cutoff scores of ≥ 3/7 points will help identify those in whom CAD should be considered and further investigation instigated. The potential impact of the tool is to improve early recognition of CAD in those with acute headache or neck pain, thereby facilitating more timely medical intervention, preventing inappropriate treatment, and improving patient outcomes.Wordcount: 3195.
颈内动脉夹层（CAD）是年轻人中风的一个重要原因，由于早期特征可能类似于偏头痛或肌肉骨骼表现，因此可能会被漏诊。本研究旨在开发一种诊断支持工具，以早期识别 CAD。
回顾性观察性研究。
三级医院。
经影像学证实的 CAD 病例（ = 37）、非 CAD 中风病例（ = 20）和健康对照者（ = 100）。
通过影像学确认 CAD 的存在。预测变量包括 CAD 的危险因素和临床特征。p 值<0.2 的变量纳入多变量模型。通过计算 ROC 曲线下面积（AUC）评估模型的预测效用。
包括年龄 40-55 岁（vs<40 岁）、创伤、近期发作的头痛和 > 2 种神经功能特征 4 个变量的模型具有出色的鉴别能力：AUC 为 0.953（95%CI：0.916，0.987）。一个预测评分系统（总分/7）确定了一个最佳阈值 ≥ 3 分，其敏感性为 87%，特异性为 79%。
本研究确定了一种具有四个变量的诊断支持工具，用于预测 CAD 的风险增加。需要在临床样本中进行验证，以确认变量并改进描述符，使临床医生能够有效地应用该工具。最佳截断分数 ≥ 3/7 分将有助于识别那些应考虑 CAD 的患者，并进一步进行调查。该工具的潜在影响是改善对急性头痛或颈部疼痛患者的 CAD 的早期识别，从而促进更及时的医疗干预，避免不适当的治疗，并改善患者的结局。词数：3195。
