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Comparison of SWAN and WISE menopausal status classification algorithms.

Janet M Johnston1, Alicia Colvin, B Delia Johnson

  • 1Department of Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. johnston@edc.pitt.edu

Journal of Women'S Health (2002)
|January 4, 2007
PubMed
Summary

Classifying menopausal status using the Women's Ischemia Syndrome Evaluation (WISE) algorithm showed good agreement with the Study of Women's Health Across the Nation (SWAN) method. Discordant classifications revealed key perimenopausal changes.

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Area of Science:

  • Reproductive endocrinology and women's health research.

Background:

  • Accurate classification of menopausal status is crucial for epidemiological and clinical studies concerning midlife women.
  • Current epidemiological studies, like the Study of Women's Health Across the Nation (SWAN), primarily rely on self-reported menstrual bleeding history for classification.
  • This reliance on bleeding history can be a limitation in precisely defining menopausal stages.

Purpose of the Study:

  • To evaluate the applicability of the Women's Ischemia Syndrome Evaluation (WISE) algorithm, which incorporates hormone levels, for classifying menopausal status in the SWAN cohort.
  • To compare the menopausal status classifications derived from the WISE algorithm with those obtained through the traditional SWAN method (bleeding history).
  • To investigate the characteristics of women whose menopausal status classifications differed between the SWAN and WISE methods.

Main Methods:

  • The WISE algorithm, initially developed using menstrual, reproductive history, and serum hormone levels, was applied to SWAN participants.
  • Menopausal status classifications from the WISE algorithm were compared against the existing SWAN classifications.
  • Agreement between the two methods was assessed using kappa statistics, and characteristics of women with discordant classifications were analyzed.

Main Results:

  • A substantial proportion of women received concordant menopausal status classifications between the SWAN and WISE methods at baseline (76.7%) and at the fifth annual follow-up (72.7%).
  • Kappa values indicated moderate agreement (0.52 at baseline, 0.57 at follow-up) between the two classification systems.
  • Subgroups of women with discordant classifications exhibited significant trends with increasing age: rising menopausal symptoms, elevated follicle-stimulating hormone (FSH), and decreased estrogen levels (p < 0.001).

Conclusions:

  • The WISE algorithm offers a valuable alternative for menopausal status classification in studies possessing hormone data, particularly when resources for adjudication are limited.
  • The algorithm is suitable for studies with or without an intact uterus and does not require data specific to the menstrual cycle phase.
  • Future research could enhance perimenopausal assessment by integrating hormonal measures with bleeding pattern data, potentially combining elements of both SWAN and WISE approaches.