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Prediction of Risk Factors for First Trimester Pregnancy Loss in Frozen-thawed Good-quality Embryo Transfer Cycles Using Machine Learning Algorithms

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Publisher Springer
Date 2022 Nov 18
PMID 36399255
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Abstract

Purpose: Can the risk factors that cause first trimester pregnancy loss in good-quality frozen-thawed embryo transfer (FET) cycles be predicted using machine learning algorithms?

Methods: This is a retrospective cohort study conducted at Sisli Memorial Hospital, ART and Reproductive Genetics Center, between January 2011 and May 2021. A total of 3805 good-quality FET cycles were included in the study. First trimester pregnancy loss rates were evaluated according to female age, paternal age, body mass index (BMI), diagnosis of infertility, endometrial preparation protocols (natural/artificial), embryo quality (top/good), presence of polycystic ovarian syndrome (PCOS), history of recurrent pregnancy loss (RPL), recurrent implantation failure (RIF), severe male infertility, adenomyosis and endometriosis.

Results: The first trimester pregnancy loss rate was 18.2% (693/ 3805). The presence of RPL increased first trimester pregnancy loss (OR = 7.729, 95%CI = 5.908-10.142, P = 0.000). BMI, which is > 30, increased first trimester pregnancy loss compared to < 25 (OR = 1.418, 95%CI = 1.025-1.950, P = 0.033). Endometrial preparation with artificial cycle increased first trimester pregnancy loss compared to natural cycle (OR = 2.101, 95%CI = 1.630-2.723, P = 0.000). Female age, which is 35-37, increased first trimester pregnancy loss compared to < 30 (OR = 1.617, 95%CI = 1.120-2.316, P = 0.018), and female age, which is > 37, increased first trimester pregnancy loss compared to < 30 (OR = 2.286, 95%CI = 1.146-4,38, P = 0.016). The presence of PCOS increased first trimester pregnancy loss (OR = 1.693, 95%CI = 1.198-2.390, P = 0.002). The number of previous IVF cycles, which is > 3, increased first trimester pregnancy loss compared to < 3 (OR = 2.182, 95%CI = 1.708-2.790, P = 0.000).

Conclusions: History of RPL, RIF, advanced female age, presence of PCOS, and high BMI (> 30 kg/m) were the factors that increased first trimester pregnancy loss.

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References
1.
Patel S, Kilburn B, Imudia A, Armant D, Skafar D . Estradiol Elicits Proapoptotic and Antiproliferative Effects in Human Trophoblast Cells. Biol Reprod. 2015; 93(3):74. PMC: 4710192. DOI: 10.1095/biolreprod.115.129114. View

2.
. Definitions of infertility and recurrent pregnancy loss: a committee opinion. Fertil Steril. 2012; 99(1):63. DOI: 10.1016/j.fertnstert.2012.09.023. View

3.
Islam M, Mustafina S, Mahmud T, Khan N . Machine learning to predict pregnancy outcomes: a systematic review, synthesizing framework and future research agenda. BMC Pregnancy Childbirth. 2022; 22(1):348. PMC: 9097057. DOI: 10.1186/s12884-022-04594-2. View

4.
Kurzawa R, Ciepiela P, Baczkowski T, Safranow K, Brelik P . Comparison of embryological and clinical outcome in GnRH antagonist vs. GnRH agonist protocols for in vitro fertilization in PCOS non-obese patients. A prospective randomized study. J Assist Reprod Genet. 2008; 25(8):365-74. PMC: 2582126. DOI: 10.1007/s10815-008-9249-7. View

5.
Liu L, Jiao Y, Li X, Ouyang Y, Shi D . Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor. Comput Methods Programs Biomed. 2020; 196:105624. DOI: 10.1016/j.cmpb.2020.105624. View