Identifying menstrual migraine- improving the diagnostic criteria using a statistical method.

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ID: 39882
2019
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Abstract
To develop a robust statistical tool for the diagnosis of menstrually related migraine.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 [Formula: see text] menstrual cycles ([Formula: see text]-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.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 [Formula: see text]-criterion and the sMM.sMM was superior to the [Formula: see text]-criterion for varying numbers of menstrual cycles and increased in accuracy with more cycle data. In contrast, the [Formula: see text]-criterion showed maximum sensitivity only for three cycles, although specificity increased with more cycle data.While the ICHD [Formula: see text]-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.
Reference Key
barra2019identifyingthe Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Barra, Mathias;Dahl, Fredrik A;MacGregor, E Anne;Vetvik, Kjersti Grøtta;
Journal The journal of headache and pain
Year 2019
DOI
10.1186/s10194-019-1035-7
URL
Keywords Keywords not found

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