An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation.

RESEARCH QUESTION: Can we develop an interpretable machine learning model that optimizes starting gonadotrophin dose selection in terms of mature oocytes (metaphase II [MII]), fertilized oocytes (2 pronuclear [2PN]) and usable blastocysts? DESIGN: This was a retrospective study of patients undergoing autologous IVF cycles from 2014 to 2020 (n = 18,591) in three assisted reproductive technology centres in the USA. For each patient cycle, an individual dose-response curve was generated from the 100 most similar patients identified using a K-nearest neighbours model. Patients were labelled as dose-responsive if their dose-response curve showed a region that maximized MII oocytes, and flat-responsive otherwise. RESULTS: Analysis of the dose-response curves showed that 30% of cycles were dose-responsive and 64% were flat-responsive. After propensity score matching, patients in the dose-responsive group who received an optimal starting dose of FSH had on average 1.5 more MII oocytes, 1.2 more 2PN embryos and 0.6 more usable blastocysts using 10 IU less of starting FSH and 195 IU less of total FSH compared with patients given non-optimal doses. In the flat-responsive group, patients who received a low starting dose of FSH had on average 0.3 more MII oocytes, 0.3 more 2PN embryos and 0.2 more usable blastocysts using 149 IU less of starting FSH and 1375 IU less of total FSH compared with patients with a high starting dose. CONCLUSIONS: This study demonstrates retrospectively that using a machine learning model for selecting starting FSH can achieve optimal laboratory outcomes while reducing the amount of starting and total FSH used.

An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation.

OBJECTIVE: To develop an interpretable machine learning model for optimizing the day of trigger in terms of mature oocytes (MII), fertilized oocytes (2PNs), and usable blastocysts. DESIGN: Retrospective study. SETTING: A group of three assisted reproductive technology centers in the United States. PATIENT(S): Patients undergoing autologous in vitro fertilization cycles from 2014 to 2020 (n = 30,278). INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Average number of MII oocytes, 2PNs, and usable blastocysts. RESULT(S): A set of interpretable machine learning models were developed using linear regression with follicle counts and estradiol levels. When using the model to make day-by-day predictions of trigger or continuing stimulation, possible early and late triggers were identified in 48.7% and 13.8% of cycles, respectively. After propensity score matching, patients with early triggers had on average 2.3 fewer MII oocytes, 1.8 fewer 2PNs, and 1.0 fewer usable blastocysts compared with matched patients with on-time triggers, and patients with late triggers had on average 2.7 fewer MII oocytes, 2.0 fewer 2PNs, and 0.7 fewer usable blastocysts compared with matched patients with on-time triggers. CONCLUSION(S): This study demonstrates that it is possible to develop an interpretable machine learning model for optimizing the day of trigger. Using our model has the potential to improve outcomes for many in vitro fertilization patients.

Impacts of Psychopharmaceuticals on the Neurodevelopment of Aquatic Wildlife: A Call for Increased Knowledge Exchange across Disciplines to Highlight Implications for Human Health.

The global use of psychopharmaceuticals such as antidepressants has been steadily increasing. However, the environmental consequences of increased use are rarely considered by medical professionals. Worldwide monitoring efforts have shown that pharmaceuticals are amongst the multitude of anthropogenic pollutants found in our waterways, where excretion via urine and feces is thought to be the primary mode of pharmaceutical contamination. Despite the lack of clarity surrounding the effects of the unintentional exposure to these chemicals, most notably in babies and in developing fetuses, the US Environmental Protection Agency does not currently regulate any psychopharmaceuticals in drinking water. As the underlying reasons for the increased incidence of mental illness-particularly in young children and adolescents-are poorly understood, the potential effects of unintentional exposure warrant more attention. Thus, although links between environmental contamination and physiological and behavioral changes in wildlife species-most notably in fish-have been used by ecologists and wildlife biologists to drive conservation policy and management practices, we hypothesize that this knowledge may be underutilized by medical professionals. In order to test this hypothesis, we created a hierarchically-organized citation network built around a highly-cited "parent" article to explore connections between aquatic toxicology and medical fields related to neurodevelopment. As suspected, we observed that studies in medical fields such as developmental neuroscience, obstetrics and gynecology, pediatrics, and psychiatry cite very few to no papers in the aquatic sciences. Our results underscore the need for increased transdisciplinary communication and information exchange between the aquatic sciences and medical fields.