The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique

Abstract

Purpose: Several mathematical models have been developed to estimate individualized chances of assisted reproduction techniques (ART) success, although with limited clinical application. Our study aimed to develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS) phase, helping the physician to decide whether to perform oocytes pick-up continuing the ongoing ART path. Methods: A single-center retrospective analysis of real-world data was carried out including all fresh ART cycles performed in 1998–2020. Baseline characteristics, ART parameters and biochemical/clinical pregnancies and live birth rates were collected. A seven-steps systematic approach for model development, combining linear regression analyses and decision trees (DT), was applied for biochemical, clinical pregnancy, and live birth rates. Results: Of fresh ART cycles, 12,275 were included. Linear regression analyses highlighted a relationship between number of ovarian follicles > 17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates (p < 0.001), but not live birth rate. DT were created for biochemical pregnancy (statistical power–SP:80.8%), clinical pregnancy (SP:85.4%), and live birth (SP:87.2%). Thresholds for OF17 entered in all DT, while sperm motility entered the biochemical pregnancy’s model, and female age entered the clinical pregnancy and live birth DT. In case of OF17 < 3, the chance of conceiving was < 6% for all DT. Conclusion: A systematic approach allows to identify OF17, female age, and sperm motility as pre-retrieval predictors of ART outcome, possibly reducing the socio-economic burden of ART failure, allowing the clinician to perform or not the oocytes pick-up.