title: Determination of menopausal status in women: the NHLBI-sponsored Women's Ischemia Syndrome Evaluation (WISE) Study.
abstract: OBJECTIVE
  Accurate classification of menopausal status is important to epidemiological research evaluating the role of reproductive hormones in disease processes. Algorithms relying on repeat hormone assays are unfeasible in large epidemiological studies. This paper summarizes the development of the Women's Ischemia Syndrome Evaluation (WISE) Hormonal menopausal status algorithm for determining premenopausal, perimenopausal, and postmenopausal status using menstrual and reproductive history and reproductive hormone levels obtained at a single clinic visit.
  
  
  METHODS
  The authors compared the accuracy of this algorithm with two currently used self-report algorithms: Menstrual, based only on months since last menstrual period, and Historical, which adds age and surgical history.
  
  
  RESULTS
  The study population consisted of 515 women (329 clearly postmenopausal) enrolled in the WISE study who were undergoing coronary angiography for suspected ischemia. A subgroup of 186, not clearly postmenopausal, was classified by these three algorithms. Results were evaluated against individualized expert consensus classification. The Menstrual and Historical classifications differed significantly (p < 0.0001) from expert consensus, with 32%-26% discordant classifications, respectively. For the WISE Hormonal classification, discordance was 4%.
  
  
  CONCLUSIONS
  The authors conclude that inaccurate classifications of menopausal status occur frequently in self-report algorithms. Use of the relatively simple WISE algorithm can improve the accuracy of menopausal status classification for epidemiological research.
authors.affiliation: Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA. djohnson@edc.pitt.edu
authors.forename: B Delia
authors.initials: BD
authors.lastname: Johnson
authors.forename: C Noel Bairey
authors.initials: CN
authors.lastname: Merz
authors.forename: Glenn D
authors.initials: GD
authors.lastname: Braunstein
authors.forename: Sarah L
authors.initials: SL
authors.lastname: Berga
authors.forename: Vera
authors.initials: V
authors.lastname: Bittner
authors.forename: T Keta
authors.initials: TK
authors.lastname: Hodgson
authors.forename: Gretchen L
authors.initials: GL
authors.lastname: Gierach
authors.forename: Steven E
authors.initials: SE
authors.lastname: Reis
authors.forename: Diane A
authors.initials: DA
authors.lastname: Vido
authors.forename: Barry L
authors.initials: BL
authors.lastname: Sharaf
authors.forename: Karen M
authors.initials: KM
authors.lastname: Smith
authors.forename: George
authors.initials: G
authors.lastname: Sopko
authors.forename: Sheryl F
authors.initials: SF
authors.lastname: Kelsey
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000175:diagnosis; D017023:Coronary Angiography; D003324:Coronary Artery Disease / Q000175:diagnosis; D005260:Female; D006304:Health Status; D006801:Humans; D008593:Menopause; D008875:Middle Aged; D017202:Myocardial Ischemia / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012649:Self Concept; D011795:Surveys and Questionnaires; D013997:Time Factors; D014481:United States; D016387:Women's Health