To what extent are patients with migraine able to predict attacks?

• Main Authors: Gago-Veiga AB, Pagán J, Henares K, Heredia P, González-García N, De Orbe MI, Ayala JL, Sobrado M, Vivancos J
• Format: Article
• Language: English
• Published: Dove Medical Press 2018-09-01
• Series: Journal of Pain Research
• Subjects: Migraine; premonitory symptoms; prediction; real-time; electronic diary; machine learning
• Online Access: https://www.dovepress.com/to-what-extent-are-patients-with-migraine-able-to-predict-attacks-peer-reviewed-article-JPR

Ana B Gago-Veiga,1 Josué Pagán,2,3 Kevin Henares,2,4 Patricia Heredia,1 Nuria González-García,5 María-Irene De Orbe,4 Jose L Ayala,3,4 Mónica Sobrado,1 Jose Vivancos1 1Headache Unit, Department of Neurology, Instituto de Investigación Sanitaria Hospital, Universitario de la Princesa, Madrid, Spain; 2Department of Electronic Engineering, Universidad Politécnica de Madrid, Madrid, Spain; 3Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain; 4Department of Computer and Automation Architecture, Universidad Complutense de Madrid, Madrid, Spain; 5Headache Unit, Hospital Clínico Universitario San Carlos, Madrid, Spain Purpose: Premonitory symptoms (PSs) of migraine are those that precede pain in a migraine attack. Previous studies suggest that treatment during this phase may prevent the onset of pain; however, this approach requires that patients be able to recognize their PSs. Our objectives were to evaluate patients’ actual ability to predict migraine attacks based on their PSs and analyze whether good predictors meet any characteristic profile. Patients and methods: This prospective, observational study included patients with migraine with and without aura. Patients’ baseline characteristics were recorded. During a 2-month follow-up period, patients used a mobile application to record what they believed to be PSs and later to record the onset of pain, if this occurred. When a migraine attack ended, patients had to complete a form on the characteristics of the episode (including the presence of PSs not identified prior to the attack). Results: Fifty patients were initially selected. A final total of 34 patients were analyzed, recording 229 attacks. Of whom, 158 (69%) were accompanied by PSs and were recorded prior to the pain onset in 63 (27.5%) cases. A total of 67.6% of the patients were able to predict at least one attack, but only 35.3% were good predictors (>50% of attacks). There were only 11 cases in which a patient erroneously reported their PSs (positive predictive value: 85.1%). Good predictors were not differentiated by any specific clinical characteristic. However, a range of symptoms were particularly predictive; these included photophobia, drowsiness, yawning, increased thirst, and blurred vision. Conclusion: A large majority of patients with migraine experienced a PS and were able to predict at least one attack. Besides, only a small percentage of patients were considered as good predictors; however, they could not be characterized by any specific profile. Nonetheless, when patients with migraine believed that they were experiencing PSs, they were frequently correct. Keywords: migraine, premonitory symptoms, prediction, real-time, electronic diary, machine learning