Essential Tremor, Parkinson Disease, Tremor
Conditions
Keywords
Tremor, Essential Tremor, Parkinson Disease, Diagnosis, Differential, Machine Learning
Brief summary
In clinical practice, it is sometimes difficult to establish whether a patient's tremor is due to Parkinson's disease or essential tremor. The distinction is crucial as the health implications differ significantly between the two conditions. Therefore, the present study aims to develop a diagnostic method based on machine learning techniques to help differentiate whether a patient's tremor is due to one condition or the other. To achieve this, 110 patients with tremor, correctly diagnosed with either Parkinson's disease or essential tremor, will participate. They will undergo two diagnostic tests (tapping test and Archimedean spiral) to capture data that can be processed using machine learning techniques.
Interventions
DIAGNOSTIC_TESTTapping Test
For the administration of the tapping test, a computer application installed on a Tablet will be used. The test will be administered up to 3 times, with a 15-minute interval between each administration. Each tapping test trial will last 15 seconds. At the beginning of the session, the patient will perform 2 practice attempts.
DIAGNOSTIC_TESTArchimedes Spiral
For the administration of the Archimedean spiral, a computer application installed on a Tablet will be used. The Tablet screen will display a drawing of the spiral, serving as a reference for the participant. The test will be administered up to 3 times, with a 15-minute interval between each administration. At the beginning of the session, the patient will perform 2 practice attempts.
Sponsors
Consorci Sanitari de l'Alt Penedès i Garraf
Study design
Observational model
CASE_CROSSOVER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
45 Years to 79 Years
Healthy volunteers
No
Inclusion criteria
* Possibility to collaborate in the necessary evaluations. * Follow-up in the specialized consultation of Movement Disorders at the Neurology Service of Hospital Sant Camil-Consorci Sanitari Alt Penedes i Garraf. * Legal capacity to provide informed consent. * Signature of informed consent for study inclusion, either by the participant themselves or by their legal representative. * Participant with criteria from Group 1 or 2: Group 1: * Confirmed diagnosis of tremor due to Parkinson's disease, clinically established, based on the diagnostic criteria of the Movement Disorders Society, and additionally: * Tremor associated with bradykinesia of any duration. * Confirmatory clinical diagnosis of tremor due to Parkinson's disease (stages 1 to 2 of Hoehn and Yahr) by the neurologist responsible for the participant's follow-up. Group 2: * Confirmed diagnosis of essential tremor, based on the criteria of the Movement Disorders Society, and additionally: * Positional tremor plus kinetic and/or resting tremor with follow-up in outpatient neurology consultations for at least 3 years without a change in diagnosis. * Absence of bradykinesia.
Exclusion criteria
* Patients undergoing treatment with antipsychotics or antidepressants. * Patients with Parkinson's disease and dyskinesias. * Patients undergoing treatment with dopaminergic agonists or primidone. * Tremor of such severity that it prevents continuous tracing, at the investigator's discretion. * Cognitive or affective pathology that limits the ability to collaborate with the study procedures. * Participation in another clinical study involving an intervention, procedure, or visit frequency that is incompatible with the present study. * Participants diagnosed with any of the following conditions: Alcoholism of sufficient intensity to influence handwriting or cause neuropathy, at the investigator's discretion. Peripheral neuropathy of any cause. Dystonias. Previous stroke.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity | through study completion, an average of 1 year | Proportion of participants with confirmed Parkinson's disease for whom the machine learning-based diagnostic algorithm yields a 'positive' result. |
| Specificity | through study completion, an average of 1 year | Proportion of participants with confirmed essential tremor for whom the machine learning-based diagnostic algorithm yields a 'negative' result. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Reliability | through study completion, an average of 1 year | The reliability of the diagnostic algorithm will be evaluated based on the repeatability of the classification result (positive or negative) among the different tests conducted on the same patient |
Countries
Spain
Contacts
Primary ContactJosé Luis Camacho, MD
Backup ContactNoemí Casaponsa
Outcome results
None listed