title: Predicting the timeline to the final menstrual period: the study of women's health across the nation.
abstract: BACKGROUND
  Predicting the final menstrual period (FMP) would help women know when their menopause transition will be completed. Additionally, biological changes, such as accelerated bone loss, precede the FMP by at least 1 year.
  
  
  OBJECTIVE
  Our objective was to assess whether FSH, estradiol, or urinary N-telopeptide predict where an individual is on her timeline to FMP.
  
  
  METHODS
  The sample was 554 women from the Study of Women's Health Across the Nation. We modeled the probability of having crossed specified landmarks: 2 years before, 1 year before, and the FMP. We also modeled the probability of being in narrower intervals: 2 to1 year before FMP, 2 years before FMP and FMP, or 1 year before FMP and FMP. We determined the candidate markers that best predicted having crossed each landmark, with the optimum defined as the greatest area under the receiver-operator curve; created formulas for the probability of having crossed each landmark; and calculated sensitivity and specificity.
  
  
  RESULTS
  Final models included current estradiol and FSH (each as a fraction of 1 previous reference measure), age, menopause transition stage, race/ethnicity, and whether serum was collected during the early follicular phase. Areas under the receiver-operator curves of final models that predicted the probability of a woman having crossed 2 years before, 1 year before, and the FMP itself were 0.902, 0.926, and 0.945, respectively. If we classified women as having crossed the 2 years before the FMP landmark when predicted probability exceeded 0.3, sensitivity was 85% and specificity 77%.
  
  
  CONCLUSIONS
  This model could help patients and researchers estimate the time to FMP.
authors.affiliation: Division of Geriatrics, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095-1687, USA. ggreenda@mednet.ucla.edu
authors.forename: Gail A
authors.initials: GA
authors.lastname: Greendale
authors.forename: Shinya
authors.initials: S
authors.lastname: Ishii
authors.forename: Mei-Hua
authors.initials: MH
authors.lastname: Huang
authors.forename: Arun S
authors.initials: AS
authors.lastname: Karlamangla
mesh_terms: D000328:Adult; D000367:Age Factors; D015331:Cohort Studies; D005006:Ethnicity / Q000706:statistics & numerical data; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D008137:Longitudinal Studies; D008593:Menopause / Q000097:blood / Q000208:ethnology / Q000502:physiology*; D008597:Menstrual Cycle / Q000097:blood / Q000208:ethnology / Q000502:physiology*; D008598:Menstruation / Q000097:blood / Q000208:ethnology / Q000502:physiology; D008875:Middle Aged; D012680:Sensitivity and Specificity; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D016387:Women's Health / Q000706:statistics & numerical data