An interpretable machine learning model for predicting the optimal day of trigger during ovarian stimulation
Michael Fanton, Veronica Nutting, Funmi Solano, Paxton Maeder-York, Eduardo Hariton, Oleksii O. Barash, Louis N. Weckstein, Denny Sakkas, Alan B. Copperman, Kevin Loewke
Abstract
ObjectiveTo develop an interpretable machine learning model for optimizing the day of trigger in terms of mature oocytes (MII), fertilized oocytes (2PNs), and usable blastocysts.DesignRetrospective study.SettingA 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. 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. Retrospective study. A group of three assisted reproductive technology centers in the United States. Patients undergoing autologous in vitro fertilization cycles from 2014 to 2020 (n = 30,278). None. Average number of MII oocytes, 2PNs, and usable blastocysts. 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. 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.