These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Predicting the timeline to the final menstrual period: the study of women's health across the nation.
    Author: Greendale GA, Ishii S, Huang MH, Karlamangla AS.
    Journal: J Clin Endocrinol Metab; 2013 Apr; 98(4):1483-91. PubMed ID: 23533245.
    Abstract:
    CONTEXT: 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%. CONCLUSION: This model could help patients and researchers estimate the time to FMP.
    [Abstract] [Full Text] [Related] [New Search]