Identifying menstrual migraine– improving the diagnostic criteria using a statistical method

Abstract

Abstract Objective To develop a robust statistical tool for the diagnosis of menstrually related migraine. Background The International Classification of Headache Disorders (ICHD) has diagnostic criteria for menstrual migraine within the appendix. These include the requirement for menstrual attacks to occur within a 5-day window in at least 23 $\frac {2}{3}$ menstrual cycles ( 23 $\frac {2}{3}$-criterion). While this criterion has been shown to be sensitive, it is not specific. Yet in some circumstances, for example to establish the underlying pathophysiology of menstrual attacks, specificity is also important, to ensure that only women in whom the relationship between migraine and menstruation is more than a chance occurrence are recruited. Methods Using a simple mathematical model, a Markov chain, to model migraine attacks we developed a statistical criterion to diagnose menstrual migraine (sMM). We then analysed a data set of migraine diaries using both the 23 $\frac {2}{3}$-criterion and the sMM. Results sMM was superior to the 23 $\frac {2}{3}$-criterion for varying numbers of menstrual cycles and increased in accuracy with more cycle data. In contrast, the 23 $\frac {2}{3}$-criterion showed maximum sensitivity only for three cycles, although specificity increased with more cycle data. Conclusions While the ICHD 23 $\frac {2}{3}$-criterion is a simple screening tool for menstrual migraine, the sMM provides a more specific diagnosis and can be applied irrespective of the number of menstrual cycles recorded. It is particularly useful for clinical trials of menstrual migraine where a chance association between migraine and menstruation must be excluded.

Keywords

• Menstrually related migraine
• Diagnostic criteria
• Statistical criteria
• Markov chain model
• Operations research