Predictor Of Pregnancy Outcome Research Articles

Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus

Clinical Significance of miR-339-5p in Early Diagnosis and Predicting Pregnancy Outcome of Chinese Patients with Liver Injury in Pregnancy

Clinical Significance of miR-339-5p in Early Diagnosis and Predicting Pregnancy Outcome of Chinese Patients with Liver Injury in Pregnancy

Prediction of Pregnancy Outcomes in Women with Threatened Abortion Using Ultrasound Parameters, Uterine Artery Doppler, and Serum Progesterone Levels: A Prospective Cohort Study

Prediction of Pregnancy Outcomes in Women with Threatened Abortion Using Ultrasound Parameters, Uterine Artery Doppler, and Serum Progesterone Levels: A Prospective Cohort Study

P-174 Are new machine learning (ML) models worth it: Equivalence of iDA, KIDscore, and Gardner Scoring in Predicting IVF Pregnancy

Abstract Study question Is there a significant difference in predicting pregnancy outcomes among iDAScore 1.0, KIDScoreD5, and Gardner Score in elective blastocyst transfers? Summary answer Contrary to previous studies, iDAScore v1.0 does not demonstrate a significant Receiver Operating Characteristic (ROC) curve advantage over the traditional Scores in predicting pregnancy outcomes. What is known already iDAScore 1.0, a deep neural network integrated into the EmbryoScope system, and KIDscoreD5 have shown potential in ML-driven embryo assessment, potentially easing the workload of embryologists. While recent studies have highlighted their superior performance in predicting pregnancy outcomes, our study offers a contrasting perspective. Study design, size, duration This retrospective analysis encompassed 1762 embryos from 1112 cycles with 1424 fresh and 338 frozen single embryo transfer cycles conducted from November 2018 to September 2023 across three IVF clinics in New Zealand. No PGT cases were included. All embryos that fulfilled the defined study criteria and transferred were included in this analysis. An extended Gardner Scoring was used at our clinics: A, B, C and X, where X being poorest grades, get discarded. Participants/materials, setting, methods Retrospectively computed KID and iDAScores were analyzed alongside the pregnancy prediction accuracy of conventional grading. Gardner scores were converted to a ranked ordinal value with ‘AA’ (5), ‘AB’ and ‘BA’ at 4, ‘BB’ at 3, and both ‘AC’, ‘BC’ as 2 and ‘CC’ as 1 among the age groups; <35, 35-37, 38-40, 41-42, >42. Differences in overall predictive capacity were compared via AUC curves within age groups via paired Delong tests. P-values reported are nominal. Main results and the role of chance The study found no statistically significant differences in AUC between any of the 3 tests examined even before adjusting for multiple comparisons. Notably, in 35-37 years olds (n = 495), AUCs were 0.63 for iDAScore, 0.62 for KIDScore, and 0.61 for Gardner criteria, showing a robust ability to predict pregnancy across all methods. Similar patterns were observed in all other age groups, with no significant disparities in pregnancy prediction models (P-value > 0.05). Limitations, reasons for caution Given its retrospective nature, this study has inherent limitations. A prospective, controlled study would provide more definitive insights. Wider implications of the findings ML-based models are innovative, but they do not significantly exceed the predictive ability of traditional embryo assessment across a range of patient ages. Such models are not a panacea yet, but a data tool to increase reproducibility that might have over-promised and under delivered on clinical impact. Trial registration number not applicable

P-444 The battle between a machine learning model, a gynecologist and an embryologist to predict pregnancy outcomes after single day-5 embryo transfer

Abstract Study question Are algorithms more effective than clinicians/embryologists at predicting pregnancy outcomes in the context of Assisted Reproductive Technology (ART) following the transfer of a single blastocyst? Summary answer Evaluation of a pregnancy prediction algorithm, trained on local data and assessed by two ART professionals, yielded an accuracy comparable to a coin toss. What is known already Artificial Intelligence’s ability to synthesize multifaceted embryological and clinical data and process extensive datasets holds promise for elevating IVF success rates through data-driven, individualized treatments. Refining embryo selection through deep learning and computer vision, individualizing treatment protocols, predicting oocyte retrieval after Controlled Ovarian Hyperstimulation (COH) and ultimately predicting Live Birth (LB) following embryo transfer are one of the early promises of this technology. Study design, size, duration A retrospective analysis was conducted on 475 stimulation cycles that resulted in a single day-5 blastocyst transfer (fresh or frozen, classical IVF or ICSI) from January 2015 to January 2022 at the Poissy/Saint-Germain-En-Laye hospital, France. The study encompassed 32 clinical and biological parameters, ranging from general characteristics of patients to controlled ovarian hyperstimulation (COH) details and embryo grading. Exclusively, embryos fertilized with autologous oocytes and sperm were considered, excluding any donor-fertilized embryos. Participants/materials, setting, methods Preliminary testing with Machine Learning algorithms (XGboost, LightGBM, Random Forest (RF), Support Vector Machine (SVM), Multi-layer Perceptron (MLP)) were conducted before selecting XGboost model for this study. Data was split into a training (n = 331) and testing (n = 144) set. Pregnancy outcome predictions (binary) were compared between the model, a clinician, and an embryologist. Feature extraction was conducted, and both professionals were asked to give the top 5 most relevant parameters used to make the pregnancy predictions. Main results and the role of chance The study analyzed 475 stimulation cycles, including 362 frozen and 114 fresh transfers. Participants’ mean age was 33, with a Body Mass Index (BMI) of 24.9. The average Antral Follicle Count (AFC) was 16.8, and the mean starting gonadotropin dose was 225 UI, yielding 8.3 MII oocytes on average. 36 patients were active smokers. Preliminary AUC results showed XGboost leading (AUC = 0.57) compared to LightGBM (AUC = 0.56), Random Forest (AUC = 0.56), SVM (AUC = 0.49), and MLP (AUC = 0.47) in predicting pregnancy outcomes. After hyperparameter optimization via GridSearch, the XGboost model enhanced its AUC to 0.61, outperforming the embryologist (AUC = 0.58) and the gynecologist (AUC = 0.44). Top predictive features identified after feature extraction included endometrial thickness at or before ovulation trigger, AMH levels at patient admission, AFC, BMI, and age. The gynecologist primarily prioritized age, BMI, embryo grading (TE and ICM), and the number of mature oocytes retrieved. The embryologist, valued embryo grading (TE and ICM) foremost, followed by age, smoking status, BMI, and COH duration. Limitations, reasons for caution The study’s limited dataset due to dynamic practices and difficulty maintaining curated data over the years, necessitates caution. The selected 32 parameters from 762 available reflect typical incomplete datasets in such settings, highlighting the importance of careful interpretation and potential need for broader data integration to enhance representativeness and reliability. Wider implications of the findings The findings advocate for developing ART-specific AI, integrating multimodal data and novel parameters, to enhance live birth predictions. This approach underscores the complex synergy between algorithmic precision and clinical expertise in ART, highlighting the stochastic nature of pregnancy prediction and the potential of tailored, data-driven strategies to improve outcomes. Trial registration number not applicable

O-150 Identification and validation of RPL7 and RCCD1 as potential biomarkers associated with immune infiltration in patients with thin endometrium

Abstract Study question For patients with thin endometrium (TE), what potential genes could be utilized for exploring the underlying mechanisms and clinical therapy? Summary answer Ribosomal protein L7 (RPL7) and RCC1 domain containing 1 (RCCD1) were suggested as specific biomarkers for TE. What is known already TE is an important contributor to embryo implantation failure and female infertility. However, the pathogenesis and mechanism of TE are not clear, and consequently, there is a paucity of data on effective targeted treatments for patients with TE. Study design, size, duration Endometrial cells were collected from 18 women with TE and 18 normal controls for transcriptional sequencing. Experimental validation was based on other 10 endometrium samples, consisting of 6 TE samples and 4 controls. Participants/materials, setting, methods After performing transcriptional sequencing, differentially expressed genes (DEGs) were extracted between the two groups via bioinformatic analysis. Based on DEGs, we applied functional enrichment and LASSO analysis to identify potential biomarkers, whose expressions were then validated by quantitative real-time PCR and western blot. Immune infiltration as well as the correlation with immune cells was also explored in the study. Main results and the role of chance RPL7 and RCC1 were identified and validated to be potential biomarkers, which exhibited an overexpression in TE at both mRNA and protein levels. An abnormal immune environment was found in TE, and correlation analysis showed that the two candidate genes correlated with atypical regulation of immune cells. Limitations, reasons for caution It might not be adequate to evaluate TE by only considering the immunologic aspects. Moreover, prospective studies are required to determine whether RPL7 and RCC1 may be used for early clinical diagnosis and prediction of pregnancy outcomes. Wider implications of the findings Further understanding and better knowledge of the functions of RPL7 and RCCD1 can promote the development of targeted therapeutics to improve clinical outcomes of patients with TE. Trial registration number not applicable

O-297 Prediction of pregnancy outcomes of single vitrified-warmed blastocyst transfer using combination of an automatic classification algorithm applied on cleavage stage embryos and blastocyst morphology

Abstract Study question Does combination of embryo assessment algorithm on cleavage-stage based on the automatic annotation of morphokinetic and blastocyst morphology in embryo selection improve implantation outcomes? Summary answer Combination of embryo assessment algorithm on cleavage-stage based on the automatic morphokinetic annotation and blastocyst morphological evaluation was showed significantly predictive for implantation outcomes. What is known already Conventional morphological assessment is still the gold standard for embryo selection. Timelapse monitoring of embryo development may represent a superior way to culture and select embryos in vitro. Nowadays, time-lapse culture systems are becoming more popular, many algorithms have been developed for embryo selection based on embryo morphokinetics, providing additional information for morphological assessment. However, manual annotations of developmental events and application of algorithms can be time-consuming and subjective processes. Automation of morphokinetic annotation can be a potential approach that reduces subjectivity in embryo selection. Study design, size, duration The observational, retrospective study was conducted in a single centre between 2020-2023 and included 511 single vitrified-warmed blastocyst transfer cycles. Blastocyst selected for transfer on Day 5,6 based on conventional morphological evaluation. Embryos were evaluated on Day 3 with a score from 1 (best) to 5 (worst) (from highest to lowest developmental potential) by the automatic embryo assessment algorithm. The correlation of the algorithms scoring and pregnancy outcomes was qualified by generalized estimating equations (GEEs). Participants/materials, setting, methods Embryos were cultured in timelapse GERI incubator and classified on day 3 by automatic embryo assessment algorithm depending on four parameters: P2 (t3-t2), P3 (t4-t3), oocyte age, and number of cells. The correlation of the algorithms scoring and pregnancy outcomes was qualified by generalized estimating equations (GEEs). Three GEE model using embryo assessment algorithm, conventional morphological assessment, and a combination of both classification system as the predictor were compared. Main results and the role of chance The implantation rate was higher with lower the scores generated by the embryo assessment algorithm. A GEE model showed the negative association between higher embryo score and lower odds of implantation. The OR of Score 3; 4; 5 vs 1 were 0.335; 0.259; 0.245 (95% CI 0.194-0.580; 0.138-0.485; 0.111-0.544, P = 0.000), respectively, for implantation. The AUC of the model using embryo assessment algorithm for implantation potential = 0.641. The AUC of the model combining both embryo assessment algorithm and conventional morphological assessment for implantation potential = 0.733. The differences were statistically significant (z-statistic = 3.896, p = 0.0001). Limitations, reasons for caution The sample size was enough to confirm the ability of the model for embryo selection. However, the retrospective nature of this study may be a limitation. Wider implications of the findings Using combination of timelapse technology with automated embryo assessment and conventional morphological assessment can predict the success rates of assisted reproduction cycles. To our knowledge, this is the first study using embryo dataset from single vitrified-warmed blastocyst transfer cycles with this embryo assessment algorithm. Trial registration number not applicable

P-032 Prospective and multi-site analysis of pregnancy outcomes in 488 patients who completed the Sperm Quality Test (SpermQT) measuring the utility of sperm epigenetics

Abstract Study question Do SpermQT results prospectively correlate with fertility outcomes of couples undergoing intrauterine insemination (IUI)? Summary answer We observe a decrease in IUI pregnancy rates in men with Abnormal compared to Normal SpermQT results irrespective of their sperm total motile count (TMC). What is known already As the mainstay of male infertility diagnosis, the standard semen analysis provides limited value for predicting pregnancy outcomes. New technologies are needed for a more comprehensive assessment of male fertility. A novel, epigenetic assessment of sperm quality, SpermQT, was implemented clinically in 2022. Retrospective studies have shown that sperm health, as identified through DNA methylation patterns, is predictive of IUI outcomes in a subset of infertile men. Additional prospective research is needed for validation of these data. Study design, size, duration Over an 18-month period (June 2022 - January 2024), 23 reproductive endocrinologists over eight clinics ordered 488 SpermQT tests on male patients. For these patients, physicians and clinical support staff were asked to provide the sperm total motile count (TMC) from the semen analysis, male and female ages, procedure information and outcomes. SpermQT results were categorized as Normal or Abnormal. Associations between SpermQT results, outcomes, age, and TMC were analyzed. Participants/materials, setting, methods The selection of patients for SpermQT assessment was based on the clinical decision of the ordering physician. SpermQT requires a semen sample which was collected by the patient either at the andrology laboratory or at home using an at-home collection kit. All sample types were shipped directly to the vendor’s laboratory for analysis and report generation. Following the completion of the epigenetic analysis, all SpermQT results were provided to the ordering physician for patient consultation. Main results and the role of chance 488 patients received a SpermQT result. 83.6% of men had a Normal SpermQT (n = 408), and 16.4% had an Abnormal SpermQT (n = 80). 180 couples had pregnancy outcomes from at least one IUI. Of the 180, 14% of men had an Abnormal SpermQT (n = 25) and 86% had a Normal SpermQT (n = 155). Analysis of pregnancy outcomes from all 180 couples who completed one, two, or three IUI cycles showed a 15% IUI cumulative pregnancy rate. The men with a Normal SpermQT had a 17.42% cumulative IUI pregnancy rate within the first three IUI cycles while men with an Abnormal SpermQT (N = 25) had no reported pregnancies and a 0% cumulative IUI pregnancy rate. Within this patient cohort, 338 patients had reported TMC where 82% had a TMC ≥20 million (n = 276). 72% of men with an Abnormal SpermQT had a TMC ≥20M, while 84% of men with a Normal SpermQT had a TMC ≥20M. Median male and female ages in the Normal SpermQT category were both 34, while median male and female ages in the Abnormal SpermQT category were 32 and 33 respectively, indicating age did not play a role in IUI pregnancy outcomes. Limitations, reasons for caution Limitations within this study include the sample size of men with an Abnormal SpermQT result. In order to identify a statistical significance between IUI pregnancy outcomes, a total sample size of 500 patients will be needed. Data and outcomes will continue to be collected to continue the validation of SpermQT. Wider implications of the findings There is strong data showing that sperm epigenetics play an important role in IUI outcomes. Here we show in a prospective and multi-site population that a new assessment of sperm epigenetics (SpermQT) identifies men less likely to achieve an IUI pregnancy, and also identifies subfertile men with normal TMC. Trial registration number Non Applicable

O-028 DNA methylation testing with cervical secretion can serve as a non-invasive biomarker for predicting successful implantation in frozen-thawed embryo transfer

Abstract Study question Is cervical secretion gene methylation profile associated with embryo implantation outcome, and can be used as a non-invasive biomarker for pregnancy prediction in FET cycles? Summary answer Cervical secretion gene methylation profiles show significant associations with implantation outcomes, and can serve as a non-invasive predictor of successful pregnancy in FET cycles. What is known already Cause of implantation failure remains a black box in reproductive medicine, and the exact mechanism of how endometrial receptivity is still behind the curtain. Epigenetic modifications including DNA methylation regulate gene expression and may alter endometrium receptivity. Endometrial DNA methylation changes vigorously only during the reconstruction of a new endometrium throughout each menstrual cycle, suggesting a unique methylation landscape in each regenerated endometrium. Cervical secretions contain implantation-related cytokines and genetic material from endometrium, and the DNA methylation profiles of cervical secretions is highly correlated with endometrium. Therefore, cervical secretions can be used as non-invasive surrogates for investigating endometrial condition. Study design, size, duration This multi-centered, case-control study enrolled 72 infertile females who entered the FET cycle with at least one high-quality blastocyst available between Aug 2017 and Dec 2022. Embryos assessed by Gardner scoring system with grade 3BB or above were defined as high-quality. Cervical secretions were collected on the day right before embryo transfer, and then DNA methylation profile were assessed. Participants/materials, setting, methods These females aged between 20-45 years-old with regular menstruation cycle (24-35 days) and normal BMI (17.5–32.0 kg/m2). Uterine cavity lesions and hydrosalpinx without treatment, adenomyosis, large uterine myoma that distorted endometrium, and thin endometrium (<7mm) were excluded. The association between the cervical secretion gene methylation profile and pregnancy outcome was evaluated via quantitative methylation-specific PCR (qMSP), methylation array, and genome-wide analysis. Different machine learning algorithms were utilized for differential methylation analysis and implantation prediction. Main results and the role of chance Cervical secretion DNA methylome differed between receptive (pregnant) and non-receptive (non-pregnant) FET cycle with genome-wide methylation array. The accuracy of pregnancy outcome prediction using top 2000 differential methylation probes (DMPs) in promoter region can be as high as 86% in all tested sample and 96.4% in pregnant samples. Bioinformatic analysis of six selected candidate genes related to implantation from literature review showed significant differences in DNA methylation between receptive and non-receptive endometrium. The methylation level of the six candidate genes with eight regions were measured by the qMSP platform and showed varying degrees of differential methylation status between the pregnancy and non-pregnancy groups. The AUCs for a single gene/region ranged from 0.53–0.67 for implantation prediction. Ten different machine learning models were applied and the results showed that the combination of candidate genes’ DNA methylation profiles could differentiate pregnant from non-pregnant samples with an accuracy as high as 86.67% and an AUC of 0.81 by logistic regression model. The best model from different machine learning algorithms using selected DMPs of genes related to window of implantation (WOI) can predict ongoing pregnancy with accuracy rates as high as 86.78% and AUC values up to 0.91. Limitations, reasons for caution The results obtained from a relatively small cohort size, and the timing of sample collection is on the day right before embryo transfer. Further investigation of methylation profiles in late follicular phase along with large sample size with euploid embryo transfer are mandatory for validation and clinical applications. Wider implications of the findings We firstly demonstrated the feasibility of cervical secretion methylation profile as a non-invasive testing for pregnancy prediction. The promising result may contribute to predicting endometrial receptivity for cycles with a favorable endometrium and maximize the successful pregnancy rate of embryo transfer. Trial registration number not applicable

The Effect of Progesterone Elevation on the Day of Trigger Administration: A Review of the Literature

Since the advent of assisted reproductive technology, different variables have been shown to affect pregnancy outcomes. One of the most prevalent studied events is the premature rise in serum progesterone concentrations on the day of trigger administration during cycles of ovarian stimulation. This phenomenon, classically known as premature luteinization, has been observed significantly for decades and has been linked to adverse pregnancy outcomes and lower live birth rates. Ultimately, a quest to find a precise serum progesterone concentration cut-off value that can be effectively used to predict pregnancy outcomes prior to trigger administration is still underway. The purpose of the current research was to study the available literature on the relationship between serum progesterone on the day of trigger administration in controlled ovarian stimulation cycles used for IVF in an attempt to identify a cut-off serum progesterone concentration that can be used to effectively predict future pregnancy outcomes in fresh transfers. This study is a review of the literature and is based on information and data gathered from 36 published articles. The majority of the literature shows that a serum progesterone concentration cut-off of 1.5 ng/ml (4.77 nmol/L) can be used prior to trigger administration to effectively predict pregnancy outcomes. Premature progesterone elevation on the day or prior to the trigger administration is associated with adverse pregnancy outcomes in IVF cycles. Other factors such as follicle number, serum concentration of other hormones, and ovarian response to ovarian stimulation should also be considered to predict the success of IVF protocols.

Decreased complexity of glucose time series index associated with adverse pregnancy outcomes in gestational diabetes mellitus.

We investigated the relationship between the complexity of the glucose time series index (CGI) during pregnancy and adverse pregnancy outcomes in women with gestational diabetes mellitus (GDM). In this retrospective cohort study, 388 singleton pregnant women with GDM underwent continuous glucose monitoring (CGM) at a median of 26.86 gestational weeks. CGI was calculated using refined composite multiscale entropy based on CGM data. The participants were categorized into tertiles according to their baseline CGI (CGI <2.32, 2.32-3.10, ≥3.10). Logistic regression was used to assess the association between CGI and composite adverse outcomes or large for gestational age (LGA). The discrimination performance of CGI was estimated using receiver operating characteristic analysis. Of the 388 participants, 71 (18.3%) had LGA infants and 63 (16.2%) had composite adverse outcomes. After adjustments were made for confounders, compared with those with a high CGI (CGI ≥3.10), participants with a low CGI (CGI <2.32) had a higher risk of composite adverse outcomes (odds ratio: 12.10, 95% confidence interval: 4.41-33.18) and LGA (odds ratio: 12.68, 95% confidence interval: 4.04-39.75). According to the receiver operating characteristic analysis, CGI was significantly better than glycated haemoglobin and conventional CGM indicators for the prediction of adverse pregnancy outcomes (all p < .05). A lower CGI during pregnancy was associated with composite adverse outcomes and LGA. CGI, a novel glucose homeostasis predictor, seems to be superior to conventional glucose indicators for the prediction of adverse pregnancy outcomes in women with GDM.

The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS.

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder syndrome with an incidence of 6% to 10% in women of reproductive age. Women with PCOS not only exhibit abnormal follicular development and fertility disorders, but also have a greater tendency to develop anxiety and depression. Our aim was to evaluate the ability of inflammatory factors in follicular fluid to predict embryonic developmental potential and pregnancy outcome and to construct a machine learning model that can predict IVF pregnancy outcomes based on indicators such as basic sex hormones, embryonic morphology, the follicular microenvironment, and negative emotion. In this study, inflammatory factors (CRP, IL-6, and TNF-α) in follicular fluid samples obtained from 225 PCOS and 225 non-PCOS women were detected via ELISA. For patients with PCOS, the levels of CRP and IL-6 in the follicular fluid in the pregnant group were significantly lower than those in the nonpregnant group. For non-patients with PCOS, only the level of IL-6 in the follicular fluid was significantly lower in the pregnant group than in the nonpregnant group. In addition, for both PCOS and non-patients with PCOS, compared with those in the pregnant group, patients in the nonpregnant group showed more pronounced signs of anxiety and depression. Finally, the factors that were significantly different between the two subgroups (pregnancy and nonpregnancy) of patients with or without PCOS were identified by an independent sample t test first and further analysed by multilayer perceptron (MLP) and random forest (RF) models to distinguish the two clinical pregnancy outcomes according to the classification function. The accuracy of the RF model in predicting pregnancy outcomes in patients with or without PCOS was 95.6% and 91.1%, respectively. The RF model is more suitable than the MLP model for predicting pregnancy outcomes in IVF patients. This study not only identified inflammatory factors that can affect embryonic development and assessed the anxiety and depression tendencies of PCOS patients, but also constructed an AI model that predict pregnancy outcomes through machine learning methods, which is a beneficial clinical tool.

MUC16 can Predict the Pregnancy Outcomes in Human and Intraperitoneal Administration of MUC16 can Rescue Pregnancy Losses in Mouse Models.

Mucin 16 (MUC16) participates in the process of embryo implantation, but few studies have examined the association between MUC16 and pregnancy loss. To investigate this association, the expression of MUC16 in serum and decidua was compared between women with pregnancy loss and ongoing pregnancies. In vitro experiments and animal models were used to explore the role and underlying mechanisms of MUC16 in pregnancy loss. In human study, the expression of MUC16 in serum and decidua was both consistently lower in the women with pregnancy loss compared with those in women with ongoing pregnancies. In vitro experiments revealed the interaction of MUC16 with peripheral blood natural killer (pNK) cells. MUC16 changed the phenotype and reduced the pro-inflammation ability of pNK cells. MUC16 also inhibited the cytotoxicity of pNK cells through the Src homology region 2 domain-containing phosphatase-1/extracellular signal-regulated kinase (SHP-ERK) pathway. Furthermore, MUC16 promoted the migration, invasion and tube formation of trophoblast cells by co-culturing together with pNK cells. In vivo experiments, the mouse model of abortion was used to further confirm that intraperitoneal administration of MUC16 could rescue the pregnancy loss. This study reveals the still-unknown connection between MUC16 and pNK cells and indicates that MUC16 provides a novel method for future prediction and treatment of unfavorable pregnancy outcomes.

Prediction of embryo euploidy and pregnancy outcome by blastocyst morphology and development speed for women receiving single embryo transfer

Background Selection of high-quality blastocysts is the most important factor determining the success of assisted reproductive technology. The objective of this study is to assess the values of blastocyst morphological quality and development speed for predicting euploidy and clinical pregnancy outcome. Methods A total of 155 preimplantation genetic testing cycles including 959 blastocysts and 154 euploid blastocyst transfer cycles conducted between January 2018 and December 2019 were retrospectively analysed. The associations of blastocyst morphological quality and development speed (D) with chromosomal status, clinical pregnancy rate, early miscarriage rate, and ongoing pregnancy rate were evaluated by univariate and multivariate regression. Results The euploidy rate of development speed D5 blastocysts was significantly greater than that of D6 blastocysts (61.4% vs. 38.1%, P < 0.001), and the euploid rate of morphologically high-grade blastocysts was significantly greater than that of non-high-grade blastocysts. Development speed D5 (OR = 1.6, 95% CI 1.2–2.2, P = 0.02) and high-grade morphology (OR = 2.1, 95% CI 1.5–2.9, P = 0.01) were independent predictors of euploidy. The ongoing pregnancy rate of D5 blastocysts was significantly higher than that of D6 blastocysts (62.3% vs. 43.8%, P = 0.04). Transfer of euploid blastocysts with high-grade morphology resulted in a greater ongoing pregnancy rate than transfer of non-high-grade euploid blastocysts (60.7% vs. 43.2%, P = 0.049). Alternatively, D6 development speed was an independent risk factor for early pregnancy loss after euploid blastocyst transfer. Multivariate regression analysis adjusting for confounding factors identified maternal age, blastocyst development speed, and blastocyst morphological grade as independent predictors of euploidy but not of clinical pregnancy. Conclusion The recommended sequence of embryo transfer based on the present study is D5 high-grade > D6 high-grade > D5 non-high-grade > D6 non-high-grade.

Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows†.

The objective was to identify a set of genes whose transcript abundance is predictive of a cow's ability to become pregnant following artificial insemination. Endometrial epithelial cells from the uterine body were collected for RNA sequencing using the cytobrush method from 193 first-service Holstein cows at estrus prior to artificial insemination (day 0). A group of 253 first-service cows not used for cytobrush collection were controls. There was no effect of cytobrush collection on pregnancy outcomes at day 30 or 70 or on pregnancy loss between days 30 and 70. There were 2 upregulated and 214 downregulated genes (false discovery rate < 0.05, absolute fold change >2-fold) for cows pregnant at day 30 versus those that were not pregnant. Functional terms overrepresented in the downregulated genes included those related to immune and inflammatory responses. Machine learning for fertility biomarkers with the R package BORUTA resulted in identification of 57 biomarkers that predicted pregnancy outcome at day 30 with an average accuracy of 77%. Thus, machine learning can identify predictive biomarkers of pregnancy in endometrium with high accuracy. Moreover, sampling of endometrial epithelium using the cytobrush can help understand functional characteristics of the endometrium at artificial insemination without compromising cow fertility. Functional characteristics of the genes comprising the set of biomarkers is indicative that a major determinant of cow fertility, at least for first insemination after calving, is immune status of the uterus, which, in turn, is likely to reflect the previous history of uterine disease.

Gestational weight gain and pregnancy outcomes in Chinese women with type 2 diabetes mellitus: evidence from a tertiary hospital in Beijing.

To examine the effects of gestational weight gain on pregnancy outcomes and determine the optimal range of weight gain during pregnancy for Chinese women with type 2 diabetes mellitus. This retrospective cohort study included 691 Chinese women with type 2 diabetes mellitus from 2012 to 2020. The study utilized a statistical-based approach to determine the optimal range of gestational weight gain. Additionally, multivariate logistic regression analysis was conducted to assess the impact of gestational weight gain on pregnancy outcomes. (1) In the obese subgroup, gestational weight gain below the recommendations was associated with decreased risks of large for gestational age (adjusted odds ratio [aOR] 0.19; 95% confidence interval [CI] 0.06-0.60) and macrosomia (aOR 0.18; 95% CI 0.05-0.69). In the normal weight subgroup, gestational weight gain below the recommendations of the Institute of Medicine was associated with decreased risks of preeclampsia (aOR 0.18; 95% CI 0.04-0.82) and neonatal hypoglycemia (aOR 0.38; 95% CI 0.15-0.97). (2) In the normal weight subgroup, gestational weight gain above the recommendations of the Institute of Medicine was associated with an increased risk of large for gestational age (aOR 4.56; 95% CI 1.54-13.46). In the obese subgroup, gestational weight gain above the recommendations was associated with an increased risk of preeclampsia (aOR 2.74; 95% CI 1.02, 7.38). (3) The optimal ranges of gestational weight gain, based on our study, were 9-16kg for underweight women, 9.5-14kg for normal weight women, 6.5-12kg for overweight women, and 3-10kg for obese women. (4) Using the optimal range of gestational weight gain identified in our study seemed to provide better prediction of adverse pregnancy outcomes. For Chinese women with type 2 diabetes, inappropriate gestational weight gain is associated with adverse pregnancy outcomes, and the optimal range of gestational weight gain may differ from the Institute of Medicine recommendations.

Role of admission cardiotocography in predicting pregnancy outcome in postdated pregnancies

Role of admission cardiotocography in predicting pregnancy outcome in postdated pregnancies

Semi-quantitative metalloproteinase-8 rapid test for the prediction of adverse pregnancy outcomes in patients with preterm premature rupture of membranes

ObjectiveWe aimed to determine whether the semi-quantitative metalloproteinase-8 (MMP-8) bedside test is a worthwhile indicator in reflecting the severity of of intra-amniotic inflammation (IAI) and in predicting adverse pregnancy outcomes. Study DesignThis retrospective cohort study comprised 76 singleton-pregnant women admitted to the Seoul National University Bundang Hospital with a diagnosis of preterm premature rupture of membranes (preterm PROM) between 20 weeks 0 days and 33 weeks 6 days of gestation who underwent trans-abdominal amniocentesis to confirm intra-amniotic infection by positive results for aerobic/anaerobic bacteria, fungi, and genital mycoplasma and evaluate lung maturity. The semi-quantitative MMP-8 rapid test kit employs a colourimetric assay to quantify MMP-8 levels in amniotic fluid (AF), expressing results from 0 to 100 percent. Participants were divided into three groups: group 1, including negative MMP-8 test with colour scale of 0 % (negative, n = 17); group 2, including positive MMP-8 test with colour scale < 51 % (weak positive, n = 21); and group 3, including positive MMP-8 test with colour scale of 51 %–100 % (strong positive, n = 38). ResultsApproximately 78 % (59/76) of the participants showed a positive MMP-8 test result; all culture-proven AF samples (33.3 % [25/75]) yielded positive MMP-8 test, categorizing these patients into either group 2 or group 3. A significant trend was observed where the rate of positive culture-proven samples increased with the progression from group 1 (negative) to group 3 (strong positive). Both white blood cell counts in AF and maternal serum C-reactive protein levels were found to escalate with the progression of test results from negative to strong positive. This progression was associated with an increased risk of spontaneous preterm birth within 48 h, 7 days, and 14 days from amniocentesis and within 34 weeks of gestation. ConclusionThe more the test results progress from negative to strong positive, the shorter the interval from amniocentesis to delivery becomes, and the higher the risk of intra-amniotic infection, spontaneous preterm delivery, and other perinatal complications. This relationship highlights the critical value of the semi-quantitative MMP-8 rapid test in predicting adverse pregnancy outcomes in patients with preterm PROM.

Association between maternal hemoglobin concentration and educational attainment in mid-childhood in a high-resource obstetric setting: a prospective cohort study

BackgroundAlthough maternal Hb levels during pregnancy are commonly associated with perinatal outcomes, their link to childhood neurodevelopment remains uncertain. ObjectiveThis study aimed to examine the associations between maternal hemoglobin (Hb) in early and late pregnancy with offspring mid-childhood educational attainment in a high-resource obstetric setting. Study DesignPregnancy data from a prospective birth cohort (Pregnancy Outcome Prediction Study, Cambridge, UK, 2008-2012, N=3285) were linked to mid-childhood educational outcomes (Department for Education, UK). Regression models adjusted for maternal, child, and socioeconomic factors were used to determine associations between maternal Hb, pregnancy complications, and offspring educational outcomes (ages 5-7). ResultsNo association was observed between maternal Hb at 12 weeks and the likelihood of either adverse pregnancy outcomes or children meeting expected educational standards between ages 5-7. Higher maternal Hb at 28 weeks was associated with an increased risk of small-for-gestational age infants (aOR1.26, 95%CI 1.11-1.59; p=0.002) and preterm birth (aOR1.38, 95%CI 1.11-1.81; p=0.005). There were no adverse birth outcomes associated with anemia. However children of mothers who were anaemic at 28 weeks had ∼40% increased risk of not attaining expected educational standards at age 5 (aOR1.42, 95%CI 1.03-1.95; p=0.03). There was no association between maternal anemia at 28 weeks and educational performance at ages 6-7. No associations were found between high maternal Hb concentrations (top decile) or change Hb concentrations between 12-28 weeks and childhood educational attainment. ConclusionsMaternal anemia at 28 weeks of pregnancy is associated with reduced educational attainment aged 5, but not at older ages (6-7 years). A proactive approach to increasing maternal hemoglobin in high-resource settings is unlikely to impact on long-term childhood educational attainment.

Role of Ductus Venosus Doppler Sonography for the Prediction of Perinatal Outcome in Term Pregnancies Complicated by Gestational Diabetes Mellitus.

Ductus venosus (DV) Doppler velocimetry reflects fetal cardiac function. Gestational diabetes mellitus (GDM) is assumed to impair cardiac function due to fetal hyperglycemia. The purpose of this study was to assess the ability of DV Doppler to predict an adverse perinatal outcome (APO) in term pregnancies with GDM. This is a retrospective cohort study including GDM pregnancies of singleton, non-anomalous fetuses without any signs of placental dysfunction. All GDM women who primarily had a vaginal delivery attempt and in which DV Doppler was examined from 37+0 weeks on were included. Receiver operating characteristic curve (ROC) analyses were performed to assess the predictive value of DV pulsatility index (DV-PI) regarding a composite APO (CAPO). Furthermore, a subgroup analysis was performed regarding the presence of a large-for-gestational-age (LGA) newborn. A total of n=89 cases were included. Overall, CAPO occurred in 26 out of 89 cases (29.2%). All DV Doppler examinations showed a positive A wave. DV-PI was>95th percentile in 8 out of 89 cases (9%). Overall, ROC analysis showed no significant association of DV-PI with CAPO (AUC=0.523, p=0.735). However, regarding individual APO parameters, ROC analysis showed a significant association of DV-PI with 5th-min AGPAR (AUC=0.960, p=0.027), which was not confirmed after exclusion of LGA cases. In GDM pregnancies at term, DV Doppler sonography seems to have no benefit for APO prediction.