O-014 An expected benefit analysis of using an interpretable machine learning model for gonadotropin starting dose selection during ovarian stimulation
Authors: Tang J , Fanton M , Maeder-York P , Hariton E , Barash O , Weckstein L , Sakkas D , Copperman A , Loewke K 
Published: June 2022

Study question

What is the expected benefit of using a machine learning model for predicting the optimal starting dose of gonadotropin during ovarian stimulation?

Summary answer

Patients who had an optimal starting gonadotropin dose had improved outcomes and used significantly less total FSH compared to propensity matched patients who did not.

What is known already

The relationship between the starting dose of follicle-stimulating hormones (FSH) and ovarian response is complex. In general, too little starting FSH may lead to inadequate follicle recruitment, while too much may lead to excessive response. In completed cycles, there exists conflicting evidence of whether higher doses are beneficial or detrimental to the number of oocytes retrieved. The field of assisted reproduction has begun to apply machine learning techniques to clinical decision support for ovarian stimulation, but no studies have specifically investigated optimizing starting FSH dose selection.

Study design, size, duration

We performed a retrospective analysis of patients undergoing autologous, non-cancelled IVF cycles from 2014 - 2020 (n = 18,591) 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 starting and total doses of FSH.

Participants/materials, setting, methods

A K-nearest neighbor similarity model was trained on all cycles and used to identify the 100 most similar patients to a patient-of-interest using age, BMI, baseline anti-mullerian hormone (AMH), and baseline antral follicle count (AFC). For each patient, a patient-specific dose response curve was created by fitting a constrained second order polynomial to the number of MII oocytes relative to the starting dose of FSH across all of the neighbors.

Main results and the role of chance

For each patient, their individual dose response curve was used to determine if there was an optimal dose that maximizes the prediction of MIIs (called dose-responsive patients), or if the dose response curve shows no optimal dose (called non-responsive patients). 30% of cycles were identified as dose-responsive, 64% were identified as non-responsive, and 6% were inconclusive and excluded from analysis. Dose-responsive patients who received an optimal starting dose had, on average, 1.5 more MIIs, 1.0 more 2PNs, and 0.5 more usable blastocysts using 10 IU’s less of starting FSH and 195 IU’s less of total FSH compared to propensity-matched patients with non-optimal doses. Non-responsive patients who received a low starting dose had, on average, 0.3 more MIIs, 0.4 more 2PNs, and 0.3 more usable blastocysts using 150 IU’s less of FSH and 1375 IU’s less of total FSH compared to propensity-matched patients with a high starting dose.

Limitations, reasons for caution

The primary limitation is the retrospective nature of this study. Further, our calculations of starting FSH combined the contribution of pure FSH plus the FSH component of FSH/LH medication, rather than evaluating each separately. We also did not differentiate between types of protocols, as the majority were antagonist cycles.

Wider implications of the findings

Our results suggest that a patient similarity model for selecting starting FSH can help increase MII outcomes while reducing the amount of FSH given to a patient. 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

Not applicable