Ovarian biomarkers predict controlled ovarian stimulation for in vitro fertilisation treatment in Singapore

Predicting the ovarian response: towards a determinant model and implications for practice

OBJECTIVE

To improve the reliability of prediction models for ovarian response to stimulation in ART.

DESIGN

A multicenter retrospective cohort study.

SETTING

Twelve reproductive centers.

PATIENTS

A total of 25,854 controlled ovarian stimulations between 2005 and 2016, including cycles cancelled for inadequate response, were included.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

Precision of the prediction of the number of oocytes at ovarian pickup and of cancellation rate for poor ovarian response.

RESULTS

Both AMH and antral follicle count exhibit a non-linear effect on the oocyte yield, with a linear relationship after log-transformation. After adjustment for age, BMI, and center, ovarian response observed in a previous stimulation was found to be the best predictor, followed by AMH and AFC. The zero-inflated binomial negative model showed that predictors of cycle cancellation and number of oocytes at retrieval were different, and assimilating cancellation to zero oocyte greatly reduces the determination of the model. Our model was characterized by the best ever reached determination (R2=0.505 for non-naïve women, 0.313 for all the women) and provided evidence of a very strong difference among centers. The results can be easily converted in the prediction of response levels (poor-medium-good-high). Finally, in case of partial report of the above predictors, we show that the univariate prediction based on the best predictor provides a good approximation.

CONCLUSION(S)

A substantial improvement of the ovarian response prediction is possible in modelling the possible cancellation decision, followed by the oocyte retrieval itself, according to an appropriate model based on previous stimulation and non-linear effects of AMH and AFC.

Thyroid and COVID-19: a review on pathophysiological, clinical and organizational aspects

BACKGROUND

Thyroid dysfunction has been observed in patients with COVID-19, and endocrinologists are requested to understand this clinical issue. Pandemic-related restrictions and reorganization of healthcare services may affect thyroid disease management.

OBJECTIVE AND METHODS

To analyze and discuss the relationship between COVID-19 and thyroid diseases from several perspectives. PubMed/MEDLINE, Google Scholar, Scopus, ClinicalTrial.gov were searched for this purpose by using free text words and medical subject headings as follows: "sars cov 2", "covid 19", "subacute thyroiditis", "atypical thyroiditis", "chronic thyroiditis", "hashimoto's thyroiditis", "graves' disease", "thyroid nodule", "differentiated thyroid cancer", "medullary thyroid cancer", "methimazole", "levothyroxine", "multikinase inhibitor", "remdesivir", "tocilizumab". Data were collected, analyzed, and discussed to answer the following clinical questions: "What evidence suggests that COVID-19 may induce detrimental consequences on thyroid function?"; "Could previous or concomitant thyroid diseases deteriorate the prognosis of COVID-19 once the infection has occurred?"; "Could medical management of thyroid diseases influence the clinical course of COVID-19?"; "Does medical management of COVID-19 interfere with thyroid function?"; "Are there defined strategies to better manage endocrine diseases despite restrictive measures and in-hospital and ambulatory activities reorganizations?".

RESULTS

SARS-CoV-2 may induce thyroid dysfunction that is usually reversible, including subclinical and atypical thyroiditis. Patients with baseline thyroid diseases are not at higher risk of contracting or transmitting SARS-CoV-2, and baseline thyroid dysfunction does not foster a worse progression of COVID-19. However, it is unclear whether low levels of free triiodothyronine, observed in seriously ill patients with COVID-19, may worsen the disease's clinical progression and, consequently, if triiodothyronine supplementation could be a tool for reducing this burden. Glucocorticoids and heparin may affect thyroid hormone secretion and measurement, respectively, leading to possible misdiagnosis of thyroid dysfunction in severe cases of COVID-19. High-risk thyroid nodules require a fine-needle aspiration without relevant delay, whereas other non-urgent diagnostic procedures and therapeutic interventions should be postponed.

DISCUSSION

Currently, we know that SARS-CoV-2 could lead to short-term and reversible thyroid dysfunction, but thyroid diseases seem not to affect the progression of COVID-19. Adequate management of patients with thyroid diseases remains essential during the pandemic, but it could be compromised because of healthcare service restrictions. Endocrine care centers should continuously recognize and classify priority cases for in-person visits and therapeutic procedures. Telemedicine may be a useful tool for managing patients not requiring in-person visits.

Predictive factors of ovarian response to GnRH antagonist stimulation protocol: AMH and age are potential candidates

Background

Prediction of ovarian response prior to the ovarian stimulation cycle is useful in determining the optimal starting dose of recombinant follicle-stimulating hormone (r-FSH). This study was designed to (I) evaluate which of the following parameters (age, AMH, and FSH) can be used as a predictor of ovarian response to GnRH antagonist stimulation protocol, (II) determine the cutoff value of AMH and age for predicting poor and high ovarian response, and (III) investigate the relationship between age, AMH level, and other clinical parameters. It is a retrospective study. A total of 318 women with a mean age of 28.2 ± 5.9 years old were included in this study. Hormone levels (FSH, LH, PRL, E2, and AMH) and the number of collected oocytes were determined. Based on the number of retrieved oocytes, the participants were divided into three groups: poor response (oocytes < 4, n= 51), normal response (oocytes 4–14, n= 192), and high response (oocytes > 14, n= 75).

Results

A significant increase has been found in AMH level and number of retrieved oocytes and mature oocytes from low to normal and high ovarian response group (P < 0.001). Also, the age in the poor ovarian response group was significantly greater than normal and high ovarian response groups (P < 0.001). A significant positive correlation has been found between the number of retrieved oocytes and mature oocytes and level of AMH (P < 0.001). The receiver operating characteristic (ROC) curves showed that both AMH and age had the highest accuracy in the prediction of poor ovarian response with a cutoff value < 1.45 and > 31.5 years, respectively. Additionally, the ROC analysis has shown that the AMH had the highest accuracy, followed by age in the prediction of high ovarian response with a cutoff value > 3.55 and < 27.5 years, respectively.

Conclusions

This study demonstrates that AMH level and women’s age may be used as potential predictors of ovarian response to GnRH antagonist stimulation protocol.

Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology

The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical complexity. Therefore, this study conducted extensive selection using models that were derived from a variety of machine learning algorithms, including random forest (RF), decision trees, eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and artificial neural networks (ANN) for the development of two models called the COS pre-launch model (CPLM) and the hCG pre-trigger model (HPTM) to assess POR based on different requirements. The results demonstrated that CPLM constructed using ANN achieved the highest AUC result of all the algorithms in COS pre-launch (AUC=0.859, C-index=0.87, good calibration), and HPTL constructed using random forest was found to be the most effective in hCG pre-trigger (AUC=0.903, C-index=0.90, good calibration). It is notable that CPLM and HPTM exhibited better performance than common clinical characteristics (0.895 [CPLM], and 0.903 [HPTM] in comparison to 0.824 [anti-Müllerian hormone (AMH)], and 0.799 [antral follicle count (AFC)]). Furthermore, variable importance figure elucidated the values of AMH, AFC, and E₂ level and follicle number on hCG day, which provides important theoretical guidance and experimental data for further application. Generally, the CPLM and HPTM can offer effective POR prediction for patients who are receiving assisted reproduction technology (ART), and has great potential for guiding the clinical treatment of infertility.