P-600 A combined expected benefit analysis of using two machine learning models for optimizing starting gonadotropin dose and day of trigger during ovarian stimulation. (30th June 2022)

• Record Type: Journal Article
• Authors: Fanton, M; Tang, J; Maeder-York, P; Hariton, E; Barash, O; Weckstein, L; Sakkas, D; Copperman, A; Loewke, K
• Is Part Of: Human reproduction. Volume 37(2022)Supplement 1
• Publication Date: 2022-06-30
• Subjects: Human reproduction -- Periodicals; 618
• DOI: 10.1093/humrep/deac107.552
• Languages: English
• ISSNs: 0268-1161

Abstract

Abstract: Study question: What is the combined expected benefit of using machine learning algorithms to optimize the starting gonadotropin dose and day of trigger during ovarian stimulation? Summary answer: Patients who had an optimal starting dose and optimal day of trigger had significantly improved outcomes compared to propensity matched patients who did not. What is known already: Choosing the starting dose of follicle-stimulating hormones (FSH) and deciding when to inject the final trigger shot are two critical decisions made during an ovarian stimulation protocol. Although studies have investigated the effect of these decisions on patient outcomes, in practice, they remain subjective and can vary significantly across providers. Recently, machine learning techniques to support these decisions have been investigated, providing evidence that following model recommendations can improve outcomes. However, the combined effect of multiple clinical decision support tools on patient outcomes has not been studied. Study design, size, duration: We performed a retrospective analysis of patients undergoing autologous, non-cancelled IVF cycles from 2014 - 2020 (n = 15, 522) at three different IVF clinics in the United States. The primary outcomes were the average number of MIIs, 2PNs, and usable blastocysts in relation to total doses of FSH. Participants/materials, setting, methods: To select the optimal starting FSH dose, a K-nearest neighbor model identified 100 similar patients and a dose response curve was created by plotting the number of MIIs retrieved relative to starting FSH across all neighbors. To select the optimal trigger day, linear regression models used daily follicles sizes and estradiol levels to predict MIIs retrieved today versus tomorrow, and a trigger day was identified by looking at day-by-day predicted MII trends. Main results and the role of chance: Across all cycles, 27% were given the recommended optimal starting FSH dose. 51% of patients were triggered earlier and 13% were triggered later than the recommendation. Combining both algorithms, 11% of patients were given both the optimal starting dose and optimal trigger day, while the remaining 89% of patients had cycles that did not follow both recommendations. Patients following both model recommendations had on average 3.2 more MIIs, 2.3 more 2PNs, and 1.2 more usable blastocysts, using 730 IU's less of total FSH, compared to propensity-matched patients with cycles that did not match both recommendations Limitations, reasons for caution: The primary limitation is the retrospective nature of this study. Clinicians did not use either decision support tool when planning patients' ovarian stimulation protocol. Further, we did not differentiate between different protocol or medication types in our analyses, which will be the focus of future work. Wider implications of the findings: Our results suggest that following the combined recommendations of two clinical decision support tools can improve outcomes and reduce total FSH used in ovarian stimulation. Future work will include continuing to increase the diversity of our dataset and performing validation studies to show improved outcomes with model use. Trial registration number: NA