P-585 The (decision) tree of fertility: an innovative decision-making algorithm in assisted reproduction technique
Authors: Morini D , Melli B , Spaggiari G , Furini C , Nicoli A , Valli B , Di Girolamo R.A.M , Iannotti F , Citro M.C , La Sala G.B , Simoni M , Aguzzoli L , Santi D , Villani M.T 
Published: June 2022

Abstract

Study question
To develop a decisional algorithm able to predict pregnancy and live birth rates after controlled ovarian stimulation (COS), helping to decide whether to perform oocytes pick-up.

Summary answer
A systematic approach allows to identify ovarian follicles, female age and sperm motility as predictors of ART outcome, reducing the socio-economic burden of ART failure

What is known already
The physician needs to estimate a priori the female response after the COS phase, leaving an extreme variability in the proposed therapeutic regimens. Indeed, the most cost-effective ART management in terms of pregnancy and live birth rates is still far to be achieve and the clinical application of predictive models is still scanty, considering their limited predictive ability. A predictive model able to estimate the chances of success in the time point after ovarian stimulation and before the pick-up could guide the decision to prosecute or not the ongoing ART path

Study design, size, duration
A single centre, retrospective analysis of data was carried out, considering all couples attending the Fertility Centre of the Department of Obstetrics and Gynaecology of Reggio Emilia (Italy). All consecutive ART cycles performed from 1998 to December 2020 were retrospectively extracted and couples fulfilling inclusion criteria were included in the final dataset with final included 12,275 ART cycles. The strong ART outcomes were considered, i.e. biochemical and clinical pregnancy and live birth rates.

Participants/materials, setting, methods
Couple with both partners older than 18 years and attending fresh ART cycles are available are included in the study. The ART procedure was evaluated collecting several variables, considering male parameters, COS approach and variables of COS response. The fertilization rate was calculated as the ratio between the number of fertilized oocytes and the number of either injected (ICSI method) or inseminated (IVF cycles) oocytes.

Main results and the role of chance
The final database included 12,275 ART cycles, consisting of 7,826 ICSI (63.8%) and 4,449 IVF (36.2%) procedures. The 87.5% of the entire cohort (10,375 couples) were treated for primary couple infertility. Linear regression analyses highlighted a relationship between number of ovarian follicles &gt;17 mm detected at ultrasound before pick-up (OF17), embryos number and fertilization rate, and biochemical and clinical pregnancy rates (p &lt; 0.001), but not live birth rate. Decisional Tree (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&lt;3, the chance of conceiving was &lt;6% for all DT. Logistic regression analyses confirmed the relationship between strong ART outcomes and those variables detected before pick-up. Interestingly, these connections appeared only when pregnancy rates were considered, suggesting that the classical statistical approach is not able to overcome the higher number of biases influencing live birth rates. In the biochemical pregnancy decision tree, alongside to OF17, sperm motility entered the model introducing the threshold of 34%.

Limitations, reasons for caution
In the study were included only those cycles in which all ART variables were available. During the long interval of data collection, ART technologies evolved, as well as the regulatory rules for ART access. This data heterogeneity over the years could mitigate the reliability of results.

Wider implications of the findings
The identification of three decision trees helping the clinician to decide whether or not to perform oocytes pick-up, continuing the ongoing ART path. In mathematical models, three predictors of ART success at a very early stage emerged, such as ovarian follicles higher than 17 mm, sperm motility and female age.

Trial registration number
not applicable