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Development of a diagnostic support tool for predicting cervical arterial dissection in primary care
Lucy Caroline Thomas1,2, Elizabeth Holliday3, John R Attia3
1School of Health and Rehabilitation Sciences, University of Queensland, QLD, Australia.
Insights
A new diagnostic tool aids in early cervical arterial dissection (CAD) detection, a stroke cause in young adults. The tool uses four key variables to identify patients needing further investigation, improving timely intervention.
Area of Science:
- Neurology
- Vascular Medicine
- Diagnostic Imaging
Background:
- Cervical arterial dissection (CAD) is a significant cause of stroke, particularly in younger populations.
- Early symptoms of CAD can be non-specific, mimicking conditions like migraine or musculoskeletal issues, leading to delayed diagnosis.
- Accurate and timely identification of CAD is crucial for effective treatment and improved patient outcomes.
Purpose of the Study:
- To develop and validate a diagnostic support tool for the early identification of cervical arterial dissection (CAD).
- To identify key clinical variables that predict the risk of CAD.
- To create a scoring system to assist clinicians in recognizing potential CAD cases.
Main Methods:
- A retrospective observational study was conducted in a tertiary hospital setting.
- Data from radiologically confirmed CAD cases (n=37), non-CAD stroke cases (n=20), and healthy controls (n=100) were analyzed.
- A multivariable model was developed using predictive variables with p-values <0.2, and its predictive utility was assessed using the area under the ROC curve (AUC).
Main Results:
- A diagnostic model incorporating age (40-55 years), trauma, recent onset headache, and >2 neurological features demonstrated high predictive accuracy (AUC = 0.953).
- A scoring system (total score/7) with an optimal threshold of ≥3 points achieved 87% sensitivity and 79% specificity.
- The identified variables effectively discriminate between CAD and non-CAD cases.
Conclusions:
- A four-variable diagnostic support tool was developed to predict an increased risk of cervical arterial dissection (CAD).
- The tool shows excellent discriminatory power and can be used with a scoring system to identify patients requiring further investigation.
- Further clinical validation is necessary to refine the tool for efficient application by clinicians, aiming to improve early CAD recognition and patient outcomes.
Objectives:
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.
Design:
Retrospective observational study.
Setting:
Tertiary hospital.
Participants:
Radiologically confirmed CAD cases (n = 37), non-CAD stroke cases (n = 20), and healthy controls (n = 100).
Main Outcome Measures:
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).
Results:
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%.
Conclusions:
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.
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