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Journal Article Multicenter Study Research Support, N.I.H., Extramural Ppublish 7 citations

PubMed: 35125179 DOI: 10.1016/j.fertnstert.2021.10.024

A personalized medicine approach to ovulation induction/ovarian stimulation: development of a predictive model and online calculator from level-I evidence.

Fertil Steril 117(2):408-418

Authors: Irene Souter, Fangbai Sun, Heping Zhang, Michael P Diamond, Richard S Legro, Robert A Wild, Karl R Hansen, Nanette Santoro

Affiliations

  • Department of Obstetrics/Gynecology and Reproductive Biology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
  • Collaborative Center for Statistics, Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut.
  • Department of Obstetrics and Gynecology, Augusta University, Augusta, Georgia.
  • Department of Obstetrics and Gynecology, Pennsylvania State University, Hershey, Pennsylvania.
  • Department of Obstetrics and Gynecology, University of Oklahoma Health Schiences Center, Oklahoma City, Oklahoma.
  • Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado.

Abstract

OBJECTIVE

To estimate the probability of clinical or multiple pregnancy during ovulation induction (OI)/ovarian stimulation (OS).

DESIGN

Secondary analysis of two multicenter randomized clinical trials (combined).

SETTING

Multicenter.

PATIENTS

A total of 750 women with polycystic ovary syndrome and 900 women with unexplained infertility.

INTERVENTIONS

Ovulation induction/OS with either timed intercourse (polycystic ovary syndrome) or intrauterine insemination.

MAIN OUTCOME MEASURES

Clinical and multiple pregnancy rates/cycle, cumulative pregnancy rates. Age, body mass index, parity, diagnosis, medication, markers of ovarian reserve, and ovarian response were considered in multivariable regression models for clinical, multiple, and cumulative pregnancy rates. Receiver operating characteristic curves were created for clinical and multiple pregnancy rates.

RESULTS

Younger patient and partner age, treatment type, lower body mass index, and medication dose were all associated with clinical pregnancy. Variables associated with multiple pregnancy included the abovementioned variables (except age), in addition to diagnosis, parity, higher antral follicle count, antimüllerian hormone levels, and ovarian response. Gonadotropin use was associated with multiple pregnancy, with progressively increasing odds ratios (cycles 1-4). Receiver operating characteristic curves indicated the model's predictive power to be fair for clinical pregnancy (areas under the curve [95% confidence interval {CI}]: 0.78 [0.75-0.81] for cycle 1 and 0.70 [0.64-0.75] for cycle 4) and good-to-excellent for multiple pregnancy (areas under the curve [95% CI]: 0.78 [0.72-0.84] for cycle 1 and 0.86 [0.78-0.93] for cycle 4). Partner age, lower medication dose, parity, antimüllerian hormone levels, and diagnosis were associated with cumulative pregnancy rates.

CONCLUSIONS

Using the majority of the factors known to predict the outcome of OI/OS cycles, we constructed an easy-to-use formula that may predict individualized chances of clinical and multiple pregnancy for commonly used fertility treatments (https://pregnancyprediction.medicine.yale.edu/CalDirect.html).

CLINICAL TRIAL REGISTRATION NUMBERS

Assessing Multiple Intrauterine Gestations after Ovulation Stimulation NCT01044862; PPCOSII NCT00719186.

MeSH

Pregnancy, Fertility, Body Mass Index, Ovary, Infertility, Hormones, Precision Medicine, Diagnosis, Polycystic Ovary Syndrome, Therapeutics, Parity, Pregnancy Rate, Ovulation, Ovulation Induction, Pregnancy, Multiple

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