Letter to the editor regarding “To what extent are patients with migraine able to predict attacks?” - Fang X (Journal of Pain Research, 2018)
Xiang Fang,1,* Weili Kong,2,* Zeping Yu,1 Jianqing Qiu,3 Hong Duan11Department of Orthopedics, West China School of Medicine/West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 2Department of Otolaryngology, West China Hospital, Sichuan University, Chengdu, Sichuan, People’s Republic of China; 3Department of Epidemiology and Health Statistics, West China School of Public Health, Sichuan University, Chengdu, Sichuan, People’s Republic of China*These authors contributed equally to this workWith interest, we read the article by Gago-Veiga et al published in Journal of Pain Research in October 2018.1 Migraine attacks, encompassing a wide range of symptoms, greatly undermine the quality of life for patients. Premonitory symptoms usually precede and alert the patients of the attack. The objectives of this prospective study1 were to illuminate if any good predictor or specific combination of premonitory symptoms exists for prediction of migraine attacks. A total of 34 patients recording 229 attacks were analyzed: 67.6% were able to predict at least one attack, while only 35.3% were able to predict >50% of attacks.The positive predictive value was 85.1%. The authors concluded some specific symptoms were predictive, even though only a few were good predictors (predicting >50% of attacks).Authors’ replyAna B Gago-Veiga, Josué Pagán, Kevin Henares, Patricia Heredia, Nuria González-García, María- Irene De Orbe, Jose L Ayala, Mónica Sobrado, Jose VivancosHeadache Unit, Department of Neurology, Instituto de Investigación Sanitaria Hospital Universitario de la Princesa, Madrid 28006, SpainWe value Fang et al’s comments, and thank them for the insights provided on our recent work. We understand some of the comments, but we would like to let you know why we decided to present the results in the most suitable way from the perspective of the analysis performed using machine-learning techniques.View the original paper by Gago-Veiga and colleagues.