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| Name | Class |
|---|---|
| Hospital Vall d'Hebron | OTHER |
| Hospital Donostia | OTHER |
| Hospital Universitario Marqués de Valdecilla | OTHER |
| Hospital Clínico Universitario Lozano Blesa |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Patients with episodic migraine | Patients with episodic migraine who present between 4 and 10 migraine attacks per month. These patients must have a diagnosis of migraine by a headache neurologist and according to the criteria proposed by ICHD-3. In addition, they must present at least 1 year of evolution of the migraine, as well as a normal neurological examination and have given their informed consent. | ||
| Control patients | People who have never had a headache episode with migraine characteristics. These may present, at most, one episode per month of headache with non-migraine characteristics in the last 3 months. In turn, they should not have a family history of migraine (1st and 2nd degree). |
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| Measure | Description | Time Frame |
|---|---|---|
| Development of a prediction strategy for the onset of migraine | Develop a prediction strategy for the onset of migraine attacks in patients with episodic migraine, based on hemodynamic and clinical variables of migraine attacks. | From the beginning of treatment, which is the initial visit, to 2 months of follow-up |
| Measure | Description | Time Frame |
|---|---|---|
| Posibility of predict the onset of pain in a migraine attack | 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. | From the beginning of treatment, which is the initial visit, to 2 months of follow-up |
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Inclusion Criteria:
Exclusion Criteria:
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Patients aged between 18 and 65 years with a diagnosis of episodic migraine who have between 4 and 14 days of headache per month. These patients will come from 7 Headache Units in Spain belonging to the following hospitals: • La Princesa University Hospital • Vall 'Hebron University Hospital • Marqués de Valdecilla University Hospital • Valladolid University Clinical Hospital • Donostia University Hospital • Lozano Blesa University Clinical Hospital • La Fe Polytechnic University Hospital
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Ana Beatriz Gago Veiga | Contact | +34 915202416 | anabeatriz.gago@salud.madrid.org | |
| Iris Fernández Lázaro | Contact | irisfdezlaz@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Ana Beatriz Gago Veiga | Fundación de Investigación Biomédica - Hospital Universitario de La Princesa | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hospital Universitario de La Princesa | Recruiting | Madrid | Madrid | 28006 | Spain |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 30310310 | Result | Gago-Veiga AB, Pagan J, Henares K, Heredia P, Gonzalez-Garcia N, De Orbe MI, Ayala JL, Sobrado M, Vivancos J. To what extent are patients with migraine able to predict attacks? J Pain Res. 2018 Sep 27;11:2083-2094. doi: 10.2147/JPR.S175602. eCollection 2018. | |
| 26134103 | Result | Pagan J, De Orbe MI, Gago A, Sobrado M, Risco-Martin JL, Mora JV, Moya JM, Ayala JL. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data. Sensors (Basel). 2015 Jun 30;15(7):15419-42. doi: 10.3390/s150715419. |
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Individual participant data (IPD) will not be shared with other researchers. This decision is based on data protection and confidentiality requirements in accordance with applicable European and national regulations (including GDPR). Given that this is a multicentre observational study conducted in routine clinical practice, access to IPD is restricted to the study investigators for the purposes of the predefined analyses.
Any potential secondary use of the dataset would only be considered in fully anonymized form and subject to prior ethical approval and compliance with applicable data protection legislation.
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| ID | Term |
|---|---|
| D008881 | Migraine Disorders |
| D020326 | Migraine without Aura |
| D020325 | Migraine with Aura |
| D006261 | Headache |
| ID | Term |
|---|---|
| D051270 | Headache Disorders, Primary |
| D020773 | Headache Disorders |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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| OTHER |
| Hospital Universitario La Fe | OTHER |
| Hospital Clínico Universitario de Valladolid | OTHER |
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| Individualized prediction algorithm |
Implement an individualized prediction algorithm that allows real-time prediction of the symptomatic phase of the migraine attack. |
| From the beginning of treatment, which is the initial visit, to 2 months of follow-up |
| Effectiveness of the prediction model | Measure the effectiveness of the prediction model both at the individual level and in a large group of patients. | From the beginning of treatment, which is the initial visit, to 2 months of follow-up |
| D009422 | Nervous System Diseases |
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |