Clinical Scorecard: Clinical Decision Support System for Stroke Risk Assessment in ED Patients with Dizziness

At a Glance

Key Highlights

• Stroke is frequently missed in ED patients presenting with dizziness, especially posterior circulation strokes.
• Existing diagnostic tools (HINTS, STANDING, TriAGe+) require specialized neurological expertise and have limitations in busy ED settings.
• A prediction model using structured and unstructured EMR data with NLP and ML can improve stroke risk assessment in dizzy patients.

Guideline-Based Recommendations

Diagnosis

• Consider stroke in patients presenting with acute dizziness, especially with risk factors such as advanced age, male gender, diabetes, atrial fibrillation, previous cerebrovascular disease, recurrent vertigo, and elevated blood pressure.
• Use clinical features combined with EMR data to improve stroke risk stratification.
• Recognize limitations of CT scans for posterior fossa ischemia and the resource constraints of MRI in emergency settings.

Management

• Develop and implement clinical decision support tools integrating EMR data and machine learning to assist ED physicians in stroke risk assessment.
• Avoid over-reliance on specialized neurological exams that may not be feasible in busy EDs with variable staff expertise.

Monitoring & Follow-up

• Monitor patients presenting with dizziness closely for stroke symptoms within seven days post-ED visit.
• Use inflammatory markers and vital signs as part of ongoing risk assessment.

Risks

• High risk of missed diagnosis of vestibular strokes leading to morbidity and mortality.
• False positives from neurological exams like HINTS may lead to unnecessary imaging.

Patient & Prescribing Data

ED patients aged ≥20 years presenting with acute dizziness without confirmed stroke at initial visit

Early identification of stroke risk using EMR-based prediction models may guide timely diagnostic imaging and interventions, potentially reducing missed strokes.

Clinical Best Practices

• Incorporate both structured (demographics, vital signs, lab results) and unstructured (physician notes) EMR data for comprehensive risk assessment.
• Apply natural language processing techniques to extract relevant clinical information from unstructured text.
• Use multivariate imputation methods to handle missing data and normalize variables before model development.
• Employ machine learning algorithms with cross-validation to identify predictive features and improve model accuracy.
• Recognize and address limitations of current clinical assessment tools in emergency settings.

References

• Acute dizziness in ED and stroke risk
• Limitations of CT and MRI in posterior circulation stroke
• HINTS exam and stroke diagnosis challenges
• Stroke predictors in dizziness patients
• Natural language processing in EMR for stroke prediction