Assessment of anovulation in eumenorrheic women: Comparison of ovulation detection algorithms

Objective To compare previously used algorithms to identify anovulatory menstrual cycles in women self-reporting regular menses. Design Prospective cohort study. Setting Western New York. Patient(s) Two hundred fifty-nine healthy, regularly menstruating women followed for one (n = 9) or two (n = 250) menstrual cycles (2005-2007). Intervention(s) None. Main Outcome Measure(s) Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E₂, P, and LH concentrations. Result(s) Algorithms based on serum LH, E₂, and P levels detected a prevalence of anovulation across the study period of 5.5%-12.8% (concordant classification for 91.7%-97.4% of cycles). The prevalence of anovulatory cycles varied from 3.4% to 18.6% using algorithms based on urinary LH alone or with the primary E₂ metabolite, estrone-3-glucuronide, levels. Conclusion(s) The prevalence of anovulatory cycles among healthy women varied by algorithm. Mid-cycle LH surge urine-based algorithms used in over-the-counter fertility monitors tended to classify a higher proportion of anovulatory cycles compared with luteal-phase P serum-based algorithms. Our study demonstrates that algorithms based on the LH surge, or in conjunction with estrone-3-glucuronide, potentially estimate a higher percentage of anovulatory episodes. Addition of measurements of postovulatory serum P or urine pregnanediol may aid in detecting ovulation.