P-434 Predicting endometrial thickness in hormone replacement therapy - frozen embryo transfer (HRT-FET): An AI algorithm for optimizing treatment management
Authors: Baum M , Moran E , Luz A , Reuvenny S , Itzhak N , Hourvitz R , Maman E , Hourvitz A , Youngster M 
Published: July 2024

Abstract

Study question
Can an AI-based algorithm assist in determining the optimal duration and dosage of estradiol supplementation to attain ideal endometrial width (EW) for non-responder HRT-FET patients?

Summary answer
An AI based algorithm can predict the probability of non-responder patients to achieve EW &gt; 8mm given the current EWs’ estradiol dosage and duration.

What is known already
A thin endometrium is considered sub-optimal for transfer and is associated with reduced pregnancy rates. Various optional treatments have been proposed, including extended estrogen administration if time allows. Predicting whether patients will respond to an extended treatment may assist in patient selection, thus preventing prolonged treatment for non-responsive patients. Furthermore, as estradiol treatment may cause side effects and discomfort, the lowest effective dose is desired.

Study design, size, duration
A retrospective study consisting 683 FET HRT cycles (split to 80% train and 20% test), performed between 2018 and 2022. Each cycle had an initial treatment protocol of 6-10 days of estradiol resulting in an endometrial thickness of less than 8mm with at least one more additional ultrasound scan.

Participants/materials, setting, methods
A logistic regression classifier was trained to predict if the next ultrasound’s EW would exceed 8mm following any combination of oral/vaginal estradiol doses administered for 2, 3 or 4 days. The model was trained and calibrated using patients’ demographics, estradiol dose, endometrial thickness, and estradiol and progesterone levels. The model was evaluated using sensitivity and positive predictive values.

Main results and the role of chance
The most significant predicting factors were the first endometrial thickness, estradiol dosage - before and after the first ultrasound, the number of days until the next ultrasound scan, and the patient’s weight.
The model’s performance was influenced by the number of days until the next ultrasound scan; therefore, the model was evaluated separately for 2, 3, and 4 days of following treatment for all estradiol doses.

The model’s performance in predicting whether the EW will be higher than 8 is
2 day treatment prediction: Positive predictive value: 0.95, Sensitivity: 0.64
3 day treatment prediction: Positive predictive value: 0.88, Sensitivity: 0.68
4 day treatment prediction: Positive predictive value: 0.84, Sensitivity: 0.5
Average prediction: Positive predictive value: 0.9, Sensitivity: 0.64

Limitations, reasons for caution
This is a retrospective study analyzing existing sonographic data with an inter-observer variability. The accuracy of the model decreased with longer intervals between ultrasound scans. A prospective study with designated sonographers is warranted to validate the results.

Wider implications of the findings
This decision-support algorithm can enhance the accuracy of estradiol dosing and may reduce the number of treatment days and ultrasound scans required to reach the optimal endometrial thickness during FET-HRT treatments.

Trial registration number
Not applicable