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

Race and ethnicity expression in reproductive endocrinology and infertility research studies compared with other obstetrics and gynecology subspecialty studies

Objective

To compare the percentage of patients per race and ethnicity group in the most cited reproductive endocrinology and infertility studies with the most cited studies in 3 other obstetrics and gynecology (OBGYN) subspecialties: gynecologic oncology, urogynecology (URO), and maternal-fetal medicine.

Design

Retrospective cohort study.

Setting

Not applicable.

Patients

Patients previously recruited in research studies.

Interventions

None.

Main Outcome Measures

Expression of minorities in research studies.

Results

Individual searches were conducted for the most cited articles in OBGYN subspecialties until 50 studies met the inclusion criteria for each OBGYN subspecialty. A total of 29,821,148 patients were included and compared between subspecialty and US Census data. Reproductive endocrinology and infertility studies had the highest percentage of White patients (80.5%), although URO studies had fewer Black patients (6.6%) compared with other subspecialties. Reproductive endocrinology and infertility studies had the lowest percentage of Hispanic patients (4.9%), yet more Asian patients were present in URO studies (3.3%) than in other subspecialties. Gynecologic oncology studies were most likely to have missing data in race expression (19.3%). Comparing study types, retrospective studies had the highest percentage of White patients (61.9%), although randomized controlled trials had the lowest expression of Hispanic patients (8.8%).

Conclusions

Reproductive endocrinology and infertility studies featured the highest rates of White patients compared with other OBGYN subspecialty studies, although URO studies had the lowest rates of Black patients. Randomized controlled trials featured higher rates of White patients and lower levels of Hispanic patients compared with US Census data.

Optimizing trigger timing in minimal ovarian stimulation for In Vitro fertilization using machine learning models with random search hyperparameter tuning

Various studies have emphasized the importance of identifying the optimal Trigger Timing (TT) for the trigger shot in In Vitro Fertilization (IVF), which is crucial for the successful maturation and release of oocytes, especially in minimal ovarian stimulation treatments. Despite its significance for the ultimate success of IVF, determining the precise TT remains a complex challenge for physicians due to the involvement of multiple variables. This study aims to enhance TT by developing a machine learning multi-output model that predicts the expected number of retrieved oocytes, mature oocytes (MII), fertilized oocytes (2 PN), and useable blastocysts within a 48-h window after the trigger shot in minimal stimulation cycles. By utilizing this model, physicians can identify patients with possible early, late, or on-time trigger shots. The study found that approximately 27 % of treatments administered the trigger shot on a suboptimal day, but optimizing the TT using the developed Artificial Intelligence (AI) model can potentially increase useable blastocyst production by 46 %. These findings highlight the potential of predictive models as a supplementary tool for optimizing trigger shot timing and improving IVF outcomes, particularly in minimal ovarian stimulation. The experimental results underwent statistical validation, demonstrating the accuracy and performance of the model. Overall, this study emphasizes the value of AI prediction models in enhancing TT and making the IVF process safer and more efficient.

Predicting pregnancy chances leading to term live birth in oligo/anovulatory women diagnosed with PCOS

RESEARCH QUESTION

Which patient features predict the time to pregnancy (TTP) leading to term live birth in infertile women diagnosed with polycystic ovary syndrome (PCOS)?

DESIGN

Prospective cohort follow-up study was completed, in which initial standardized phenotyping was conducted at two Dutch university medical centres from January 2004 to January 2014. Data were linked to the Netherlands Perinatal Registry to obtain pregnancy outcomes for each participant. All women underwent treatment according to a standardized protocol, starting with ovulation induction as first-line treatment. Predictors of pregnancies (leading to term live births) during the first year after PCOS diagnosis were evaluated.

RESULTS

A total of 1779 consecutive women diagnosed with PCOS between January 2004 and January 2014 were included. In the first year following screening, 659 (37%) women with PCOS attained a pregnancy leading to term birth (≥37 weeks of gestational age). A higher chance of pregnancy was associated with race, smoking, body mass index (BMI), insulin, total testosterone and sex hormone-binding globulin (SHBG) concentrations (c-statistic = 0.59).

CONCLUSIONS

Predictors of an increased chance of a live birth include White race, no current smoking, lower BMI, insulin and total testosterone concentrations, and higher SHBG concentrations. This study presents a nomogram to predict the chances of achieving a pregnancy (leading to a term live birth) within 1 year of treatment.

Predicting success of intrauterine insemination using a clinically based scoring system

Purpose

To develop a predictive score for the success of intrauterine insemination (IUI) based on clinical parameters.

Methods

We performed a retrospective cohort study evaluating the homologous IUI cycles performed at a single university-based reproductive medical center between 2009 and 2017. The primary outcome measure was pregnancy, defined as positive serum human chorionic gonadotropin (hCG) 12–14 days after IUI. Predictive factors for pregnancy after IUI were identified, and a predictive score was developed using a multivariable continuation ratio model.

Results

Overall, 1437 IUI cycles in 758 couples were evaluated. We found a per cycle pregnancy rate of 10.9% and a cumulative pregnancy rate of 19.4%. In a multivariable analysis, the probability of pregnancy was negatively associated with female age ≥ 35 years (OR 0.63, 95% CI 0.41–0.97, p = 0.034), endometriosis, unilateral tubal factor, or anatomical alteration (OR 0.54, 95% CI 0.33–0.89, p = 0.016), anti-Mullerian hormone (AMH) < 1 ng/ml (OR 0.50, 95% CI 0.29–0.87, p = 0.014), and total progressive motile sperm count (TPMSC) < 5 mil (OR 0.47, 95% CI 0.19–0.72, p = 0.004). We developed a predictive clinical score ranging from 0 to 5. Following 3 cycles, couples in our cohort with a score of 5 had a cumulative probability of achieving pregnancy of nearly 45%. In contrast, couples with a score of 0 had a cumulative probability of only 5%.

Conclusion

IUI success rates vary widely depending on couples’ characteristics. A simple to use score could be used to estimate a couple’s chance of achieving pregnancy via IUI, facilitating individualized counseling and decision-making.