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

Kristine E Lynch; Sunni L Mumford; Karen C Schliep; Brian W Whitcomb; Shvetha M Zarek; Anna Z Pollack; Elizabeth R Bertone-Johnson; Michelle Danaher; Jean Wactawski-Wende; Audrey J Gaskins; Enrique F Schisterman

doi:10.1016/j.fertnstert.2014.04.035

Reproductive Endocrinology Ovulation Physiology

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

Lynch KE, Mumford SL, Schliep KC, Whitcomb BW, Zarek SM, Pollack AZ, Bertone-Johnson ER, Danaher M, Wactawski-Wende J, Gaskins AJ, Schisterman EF

Published August 2014 Fertility and sterility, 102(2), 511-518.e2

DOI 10.1016/j.fertnstert.2014.04.035 PMID 24875398 PMC PMC4119548

RRM Academy Synopsis

Regular cycles without ovulation range from 3.4% to 18.6% by algorithm

Among 259 healthy women with regular periods, 3.4% to 18.6% of cycles were labeled as having no ovulation, depending on the hormone algorithm used. In this cohort, urine LH surge algorithms tended to label more cycles this way than blood progesterone algorithms. Women were followed for one or two cycles.

Key Findings

Six algorithms using blood tests labeled 5.5% to 12.8% of 509 cycles as anovulatory. Five algorithms using urine tests labeled 3.4% to 18.6% of 445 cycles as anovulatory.
The six blood-test algorithms gave the same classification for 91.7% to 97.4% of cycles, averaging 94.8%.
Urine LH surge algorithms averaged 13.2% anovulatory cycles. Luteal progesterone algorithms averaged 8.4%.
Twelve cycles (2.4%) were anovulatory by every blood-test algorithm. Four of those 12 were also anovulatory by at least one urine algorithm. No cycle was anovulatory by all five urine algorithms.
The five urine algorithms agreed on average for 80.1% of cycles (range 73.0% to 86.0%), with kappa values from −0.11 to 0.49.

Interpretation

This cohort study followed healthy women with regular cycles over time, comparing how hormone test algorithms label the same cycles as ovulating or not. Blood-test algorithms agreed with each other more often than urine tests did. Progesterone after ovulation stood out as a marker that may help confirm it. The authors call for testing these algorithms against ultrasound in future studies.

RRM Context

RRM care reads ovulation from several signals across the whole cycle. The authors suggest that progesterone measured after ovulation may help confirm it.

Our editorial summary of this paper, not the article's abstract.

Abstract

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.

Patients

Two hundred fifty-nine healthy, regularly menstruating women followed for one (n=9) or two (n=250) menstrual cycles (2005-2007).

Interventions

None.

Main Outcome Measures

Prevalence of sporadic anovulatory cycles identified using 11 previously defined algorithms that use E2, P, and LH concentrations.

Results

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.

Conclusions

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.

Topics

Reproductive Endocrinology › Ovulation Physiology › Anovulation · Menstrual Cycle › Cycle Biomarkers › Hormonal Markers · Diagnostics › Cycle Biomarkers › Ovulation Detection

Kristine E Lynch, Sunni L Mumford, Karen C Schliep, Brian W Whitcomb, Shvetha M Zarek, Anna Z Pollack, Elizabeth R Bertone-Johnson, Michelle Danaher, Jean Wactawski-Wende, Audrey J Gaskins, Enrique F Schisterman

K Lynch, S Mumford, K Schliep, B Whitcomb, S Zarek, A Pollack, Liz Bertone-Johnson, Beth Bertone-Johnson, Betsy Bertone-Johnson, E Bertone-Johnson, M Danaher, J Wactawski-Wende, A Gaskins, E Schisterman

PMID 24875398 24875398 DOI 10.1016/j.fertnstert.2014.04.035 10.1016/j.fertnstert.2014.04.035 Lynch et al. 2014, Lynch 2014

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

Regular cycles without ovulation range from 3.4% to 18.6% by algorithm

Key Findings

Interpretation

RRM Context

Abstract

Topics

By this author

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