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Related Experiment Video

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3D-Neuronavigation In Vivo Through a Patient's Brain During a Spontaneous Migraine Headache
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Migraineurs were reliably identified using administrative data.

Carl van Walraven1, Ian Colman2

  • 1Department of Medicine/School of Epidemiology, Public Health, and Preventive Medicine, University of Ottawa, 451 Smyth Road, Ottawa ON K1N 6N5, Canada; Ottawa Hospital Research Institute; ICES uOttawa.

Journal of Clinical Epidemiology
|September 26, 2015
PubMed
Summary

This study improved a model for identifying migraine patients using health administrative data. Data-mining techniques enhanced accuracy for better health services research.

Keywords:
Administrative dataClaims signature modelData miningMigraineMultivariable logistic regressionValidation

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Area of Science:

  • Health Services Research
  • Data Mining
  • Epidemiology

Background:

  • Migraine is a prevalent condition causing significant pain and disability.
  • Accurate identification of migraineurs in health data is crucial for research.
  • Routinely collected health administrative data offers a valuable resource for this identification.

Purpose of the Study:

  • To externally validate a prior model for identifying migraineurs using health administrative data.
  • To develop an improved model using data-mining techniques for more accurate migraine cohort identification.
  • To assess the performance of the new model using discrimination and calibration metrics.

Main Methods:

  • A population-based, cross-sectional survey identified migraine status in Ontarians.
  • Participants' health administrative data (diagnoses, visits) were linked and analyzed.
  • A novel 'double threshold analysis' determined optimal probability cutoffs for the new model.

Main Results:

  • A new data-mining model demonstrated improved performance (c-statistic 0.724) compared to the previous one (c-statistic 0.707).
  • The enhanced model incorporated diagnostic codes, physician/emergency visits, hospitalizations, and nonlinear age terms.
  • A "double threshold analysis" achieved high specificity (99.96%) and positive predictive value (81.0%) for migraine identification.

Conclusions:

  • Data-mining techniques significantly improved a model for identifying migraineurs.
  • The enhanced model enables accurate migraine cohort identification from routinely collected health administrative data.
  • This facilitates more robust health services research on migraine.