Assessing Accuracy of Clinical Diagnosis and Lesion Location in Acute Neurological Deficits - How Good Are Neurologists?

Stroke, Acute Clinical Trial

— HOGAN  

Official title:

Assessing Accuracy of Clinical Diagnosis and Lesion Location in Acute Neurological Deficits - How Good Are Neurologists?

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03009656
• Other study ID #: 2016-01819
• Status: Recruiting
• Start date: January 10, 2017
• Est. completion date: December 31, 2020

Study information

• Verified date: July 2019
• Source: University Hospital Inselspital, Berne
• Contact: Christoph Schankin, PD Dr. med.
• Email: christoph.schankin[@]insel.ch
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The emergency setting for acute neurological conditions, such as stroke, is peculiar due to time pressure and limited resources for further diagnostics. Clinical skills are essential for swift and accurate bedside diagnosis and thus are the basis for early and correct treatment. This is especially evident in the context of computed tomography being the standard neuroimaging method world-wide with its limitations for detecting smaller infarcts, strokes in the posterior fossa and reduced sensitivity for stroke mimics, such as epileptic seizures or migraine aura. To date, the accuracy of clinical bedside diagnosis of stroke by neurologists verified by magnetic resonance imaging (MRI) in the emergency setting has not been studied in detail. In order to improve clinical diagnosing and future treatment it is essential to quantify the accuracy of clinical diagnosis of stroke in the emergency setting ("how good are neurologists?") and to assesses whether there are any differences between experienced staff neurologists and junior physicians.

Description:

Background:

The emergency setting for acute neurological conditions, such as stroke, is peculiar due to time pressure and limited resources for further diagnostics. Clinical skills are essential for swift and accurate bedside diagnosis and thus are the basis for early and correct treatment. This is especially evident in the context of computed tomography being the standard neuroimaging method world-wide with its limitations for detecting smaller infarcts, strokes in the posterior fossa and reduced sensitivity for stroke mimics, such as epileptic seizures or migraine aura. To date, the accuracy of clinical bedside diagnosis of stroke by neurologists verified by magnetic resonance imaging (MRI) in the emergency setting has not been studied in detail. Management of acute stroke patients is a main interest of the neurovascular research group at Inselspital Bern. For example, the investigators analysed the prediction of large vessel occlusion in acute stroke patients by clinical examination and found a significant association of stroke severity measured with the NIHSS score and location of vessel occlusion. Analysis of outcome in stroke patients with mild and rapidly improving symptoms demonstrated that three of four of these patients had a favourable outcome, but those with a central vessel occlusion were likely to deteriorate with poor outcome. These studies showed that there is a correlation of clinical symptoms with the mechanism of stroke, which is important for the outcome after treatment. Importantly, however, the quality of clinical assessment itself is likely highly variable, for example depending on the experience of the treating physician. Factors influencing this clinical assessment, which needs to be done under high temporal and emotional pressure in the emergency setting have not been investigated so far but might be crucial for rapid and successful treatment ("time is brain").In order to improve clinical diagnosing and future treatment it is essential to quantify the accuracy of clinical diagnosis of stroke in the emergency setting ("how good are neurologists?") and to assesses whether there are any differences between experienced staff neurologists and junior physicians.

Rationale:

By assessing whether prediction of aetiology of acute neurological deficits is experience-based the investigators aim to understand what symptoms/signs impede the in-experienced from swiftly making the correct diagnosis in the emergency setting. This should help to improve resident training and with this treatment of patients with acute neurological deficits.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 800
• Est. completion date: December 31, 2020
• Est. primary completion date: December 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Age = 18 years.

• Non-refusal of "general consent"

• Patients with focal clinical neurological deficits with symptom onset of < 6 hours or wake-up strokes.

Exclusion Criteria:

• Interval from symptom onset to clinical examination of > 6 hours.

• Patients who do not have focal clinical neurological deficit at examination will not be included in the study.

Related Conditions & MeSH terms

• Diagnostic Self Evaluation
• Emergencies
• Stroke
• Stroke Hemorrhagic
• Stroke Syndrome
• Stroke, Acute
• Strokes Thrombotic

Intervention

Other:
• No study specific interventions

Locations

Country	Name	City	State
Switzerland	Bern University Hospital - Inselspital	Bern	BE

Sponsors (1)

Lead Sponsor	Collaborator
University Hospital Inselspital, Berne

Country where clinical trial is conducted

Switzerland, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Proportion of correct initial diagnoses by emergency physicians in patients with focal clinical neurological deficits	Primary endpoint is the proportion of correct initial diagnoses by emergency physicians in patients with focal clinical neurological deficits, calculated by comparing the initial assessment with the final diagnosis at discharge. If the initial assessment was correct, the diagnosis of the emergency physician will be rated as correct (correct answer = Ac), if it was incorrect, it will be rated as incorrect (incorrect answer = Ai).; The proportion of accurate initial diagnoses will be calculated as: Ac / (Ac + Ai)	7 days +/- 7 days