Migraine, Migraine Disorders, Migraine Headache, Migraine With Aura, Migraine Without Aura
Conditions
Keywords
Migraine, Headache, Predict, Prediction, headache prediction, predict headache, migraine prediction, predict migraine, migraine attacks, migraine attack, headache attack, migraine attack prediction, predict migraine attack, prediction algorithm, algorithm
Brief summary
The study of the ability to predict pain in a migraine attack, through premonitory symptoms and through an ambulatory monitoring device through real-time recording of hemodynamic variables, is one of the strategic lines of research of the unit. of Headaches at the Hospital de La Princesa since 2013 together with the Complutense and Polytechnic University of Madrid. Their results have been reflected in various publications (Pagán J, et al. Sensors 2015; Gago-Veiga AB, et al. J Pain Res 2018) and have promoted the creation of several invention patents.
Detailed description
Patients with episodic migraine will be recruited from the monographic headache clinics of the 7 centers participating in the study. These patients, for a maximum period of 2 months, must monitor their hemodynamic variables with a wearable device and record all the clinical characteristics of their migraine attacks. Subsequently, with these records, an individualized algorithm will be created for each patient that aims to predict the onset of the migraine attack.
Interventions
None listed
Sponsors
Fundación de Investigación Biomédica - Hospital Universitario de La Princesa
Hospital Vall d'Hebron
Hospital Donostia
Hospital Universitario Marqués de Valdecilla
Hospital Clínico Universitario Lozano Blesa
Hospital Universitario La Fe
Hospital Clínico Universitario de Valladolid
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
Inclusion criteria
* Age between 15 years and 69 years. * Diagnosed with migraine by a headache neurologist and according to the criteria proposed by ICHD-3. * History of migraine of at least 1 year of evolution. * Normal neurological examination. * Have given your informed consent. * Be able to describe your clinical situation and the characteristics of your headache. * Have an average of 10 to 14 migraine days per month in the three months prior to inclusion (high-frequency episodic migraine). * User-level management capacity of "smartphone" type electronic devices. * Be able to complete two months of study follow-up.
Exclusion criteria
* Presence of another type of headache, with the exception of headache due to excessive use of analgesic medication. * Cognitive deficiency or any other pathology that may prevent or make it difficult for the patient to perform the study correctly. * Neurological focus in the examination. * Pregnancy or breastfeeding period. * Patients with known heart disease or bronchopathy, Sjögren\'s syndrome, diabetes mellitus, or hypo/hyperthyroidism. * Carriers of pacemakers, neurostimulators or any other electronic device that is considered to make the interpretation of biometric records difficult. * Anatomical problem that makes the use of the device impossible. * Patients in whom it is expected that a change in migraine preventive treatment or other usual treatment will be possible during the study period.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Development of a prediction strategy for the onset of migraine | From the beginning of treatment, which is the initial visit, to 2 months of follow-up | Develop a prediction strategy for the onset of migraine attacks in patients with episodic migraine, based on hemodynamic and clinical variables of migraine attacks. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Posibility of predict the onset of pain in a migraine attack | From the beginning of treatment, which is the initial visit, to 2 months of follow-up | To analyze whether it is possible to predict the onset of pain in a migraine attack, through ambulatory and non-invasive monitoring of physiological variables. |
| Individualized prediction algorithm | From the beginning of treatment, which is the initial visit, to 2 months of follow-up | Implement an individualized prediction algorithm that allows real-time prediction of the symptomatic phase of the migraine attack. |
| Effectiveness of the prediction model | From the beginning of treatment, which is the initial visit, to 2 months of follow-up | Measure the effectiveness of the prediction model both at the individual level and in a large group of patients. |
Countries
Spain
Contacts
CONTACTAna Beatriz Gago Veiga
CONTACTIris Fernández Lázaro
PRINCIPAL_INVESTIGATORAna Beatriz Gago Veiga
Fundación de Investigación Biomédica - Hospital Universitario de La Princesa
Outcome results
None listed