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.
Study Type
OBSERVATIONAL
Enrollment
70
Hospital Universitario de La Princesa
Madrid, Spain
RECRUITINGDevelopment 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.
Time frame: From the beginning of treatment, which is the initial visit, to 2 months of follow-up
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.
Time frame: From the beginning of treatment, which is the initial visit, to 2 months of follow-up
Individualized prediction algorithm
Implement an individualized prediction algorithm that allows real-time prediction of the symptomatic phase of the migraine attack.
Time frame: 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.
Time frame: From the beginning of treatment, which is the initial visit, to 2 months of follow-up
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