ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Self-Prediction of Migraine Days: Analysis of Cohort of Migraine Patients Using a Digital Platform

4 September 2018, 09:00 – 09:20

Abstract

Submitted by
Marina Vives-Mestres
Authors
Marina Vives-Mestres (Curelator Inc.), Kenneth J. Shulman (Curelator Inc.), Alec Mian (Curelator Inc.), Noah Rosen (Northwell Health)
Abstract
Background:
In this study, we examine the ability of 1,537 migraineurs to predict their own attacks 24hrs in advance. Prediction of migraine days might be expected to be difficult as there is significant confusion around what is a premonitory symptom and/or trigger factors with respect to cause and effect. Additionally, migraine premonitory symptoms and the potential trigger factors show significant inter-individual variation1. However, learning to accurately predict migraine attacks may aid in self-management of the condition, impact quality of life and allow optimal timing of medication dosing. Also understanding of the strategy of good predictors may lead to generalizable and useful information for other individuals.

Methods:
Individuals with migraine registered to use Curelator Headache® and then used the digital platform to enter on a daily basis lifestyle factors, possible headaches, and medications as well as migraine expectation for the next 24 hours. For each individual we are interested in the four variables: (1) number of correct migraine day predictions, (2) number of correct migraine-free day predictions, (3) number of wrong migraine day predictions and (4) number of wrong migraine-free day predictions. The four variables (2x2 contingency table) form a composition (living in a restricted space; the simplex) and a multiple regression on the log-ratio coordinates is fitted and adjusted by covariates.

Results:
Individuals who predicted better than random differed from those with random predictions in gender (having females the greater proportion in the non-random group), age, number of tracked days and migraine frequency. In all cases the average is higher in the non-random predictors group. Almost all individuals with non-random predictions have migraine expectation positively associated with migraine occurrence the next day. The retained log-ratio model includes the variables: total migraine days tracked with Curelator, migraine frequency, gender and account type. Good migraine day predictors have higher migraine frequency than good migraine-free predictors. Individuals tracking overall more migraines are worse predictors than those having tracked less migraines. Regularly menstruating females do more often use the high/moderate predictions than other females and finally, paid users wrongly predict migraine days more often than other users.

Conclusion:
Migraine frequency is the most relevant variable explaining migraine predictions, thus it is possible that the strategy of good predictors is simply an a priori knowledge of the probability of having a migraine based on their past experience. The second most relevant variable is the number of tracked migraine days possibly indicating that individuals having more trouble on managing their condition stay longer using Curelator. Finally up to 46 daily factors were included in the simplicial model barely improving it but indicating that individual predictions are not only based on what happens the day before the migraine, but also on a longer term relation between factors and migraine occurrence.

References:
1. Peris F et al. Towards improved migraine management: Determining potential trigger factors in individual patients. Cephalalgia. 2017; 37(5):452-463

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