The effect of low-dose ovarian stimulation with HMG plus progesterone on pregnancy outcome in women with history of recurrent pregnancy loss and secondary infertility: a retrospective cohort study.
Maria Elisabetta CocciaFrancesca RizzelloMauro CozzolinoValentina TurillazziTommaso CapezzuoliPublished in: Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology (2018)
We assessed the outcome of pregnancy in women with a history of recurrent pregnancy loss (RPL) following treatment with low-dose human menopausal gonadotropin (HMG)+progesterone or progesterone alone. This single-center retrospective cohort study included data from women diagnosed with RPL and treated between February 2005 and December 2012 with one cycle of HMG + progesterone or progesterone alone. Primary endpoint was the rate of ongoing pregnancies and losses by treatment, age (<38 vs. ≥38 years) and in the subgroup with unexplained RPL. Of 169 RPL patients, 35.5% (n = 60) received HMG + progesterone and 64.5% (n = 109) progesterone alone. Compared to progesterone alone, HMG + progesterone led to a lower, although not significant, frequency of losses (3.3% vs. 11.9%, p = .09) and a twofold higher rate of ongoing pregnancies (41.7% vs. 19.3%, p = .002). Similar results were obtained in the subgroup of patients with unexplained RPL (ongoing pregnancies: 48.1% upon HMG + progesterone vs. 21.3% upon progesterone, p = .03; losses: 0% vs. 8.5%, respectively, p = .29) and in those <38 years (ongoing pregnancies: 47.4% vs. 18.8%, respectively, p = .003; losses: 5.3% vs. 10.9% respectively, p = .47). These findings suggest that HMG in women with RPL may reduce the rate of miscarriages and increase that of live births regardless of RPL cause and in women aged <38 years.
Keyphrases
- pregnancy outcomes
- estrogen receptor
- preterm birth
- low dose
- gestational age
- polycystic ovary syndrome
- end stage renal disease
- randomized controlled trial
- type diabetes
- skeletal muscle
- machine learning
- insulin resistance
- metabolic syndrome
- deep learning
- prognostic factors
- electronic health record
- open label
- combination therapy
- phase iii
- double blind
- breast cancer risk