PD18-01 A MACHINE LEARNING-DERIVED NOMOGRAM TO PREDICT PREGNANCY IN INFERTILE COUPLES WITH MALE FACTOR INFERTILITY UNDERGOING MEDICALLY ASSISTED REPRODUCTION TECHNIQUES

Abstract

You have accessJournal of UrologyCME1 Apr 2023PD18-01 A MACHINE LEARNING-DERIVED NOMOGRAM TO PREDICT PREGNANCY IN INFERTILE COUPLES WITH MALE FACTOR INFERTILITY UNDERGOING MEDICALLY ASSISTED REPRODUCTION TECHNIQUES Federico Belladelli, Edoardo Pozzi, Giuseppe Fallara, Simone Cilio, Christian Corsini, Francesco Lanzaro, Luigi Candela, Alessandro Bertini, Massimiliano Raffo, Fausto Negri, Ludovica Cella, Margherita Fantin, Paolo Capogrosso, Luca Boeri, Alessia D'Arma, Michael Eisenberg, Luca Pagliardini, Francesco Montorsi, and Andrea Salonia Federico BelladelliFederico Belladelli More articles by this author , Edoardo PozziEdoardo Pozzi More articles by this author , Giuseppe FallaraGiuseppe Fallara More articles by this author , Simone CilioSimone Cilio More articles by this author , Christian CorsiniChristian Corsini More articles by this author , Francesco LanzaroFrancesco Lanzaro More articles by this author , Luigi CandelaLuigi Candela More articles by this author , Alessandro BertiniAlessandro Bertini More articles by this author , Massimiliano RaffoMassimiliano Raffo More articles by this author , Fausto NegriFausto Negri More articles by this author , Ludovica CellaLudovica Cella More articles by this author , Margherita FantinMargherita Fantin More articles by this author , Paolo CapogrossoPaolo Capogrosso More articles by this author , Luca BoeriLuca Boeri More articles by this author , Alessia D'ArmaAlessia D'Arma More articles by this author , Michael EisenbergMichael Eisenberg More articles by this author , Luca PagliardiniLuca Pagliardini More articles by this author , Francesco MontorsiFrancesco Montorsi More articles by this author , and Andrea SaloniaAndrea Salonia More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003273.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: It is challenging to predict the probability of a successful assisted reproduction (ART) cycle with adequate accuracy. We sought to develop a predictive nomogram applying Machine Learning to predict the probability of pregnancy in couples with male infertility undergoing ART METHODS: Data from 442 primary infertile couples with pure male factor infertility evaluated at a single academic center from April 2004 to May 2018 and submitted to at least one cycle of ART were included in this study. Male patients were randomly subdivided into a training set (70% of all patients) and a test set (the remaining 30%). Using the training set, fourteen variables were selected for the prediction models: patient’s age, BMI, CCI, sex hormonal levels (i.e., FSH, LH, prolactin, tT), semen parameters (i.e., volume, concentration, total motility, morphology, and sperm DNA fragmentation [SDF]), smoking and alcohol intake. A Random Survival Forest-based classifier was built to predict ART outcomes. The mean decrease in accuracy - defined as the decrease in model accuracy from permuting the values in each variable - was used as a variable importance score. tHUS, A nomogram was developed to predict pregnancy based on a multivariable Cox regression model including the 5ìfive most relevant variables. The Harrel's concordance index (c-index) was used to evaluate the accuracy of ML prediction model and the final nomogram. RESULTS: Overall, 97 (22%) patients had a successful ART cycle. Median (IQR) age, number of cycles, and time-to-the-last-cycle were 38 (34-41) years, 1 (1-2) cycles, and 1.3 (0.7-2.3), respectively. The ML model’s c-index was 83%. The five most relevant variables selected by the ML model to predict pregnancy were: patients’ age, tT, sperm concentration, sperm morphology, and SDF. Figure 1 depicts the nomogram derived from the cox-regression model using the five relevant variables. The nomogram’s c-index was 71%. CONCLUSIONS: We developed a novel nomogram based on user-friendly infertile men’s clinical parameters to predict ART outcomes by applying a ML algorithm. This nomogram might be useful in patients counselling before ART cycle in the everyday clinical practice. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e502 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Federico Belladelli More articles by this author Edoardo Pozzi More articles by this author Giuseppe Fallara More articles by this author Simone Cilio More articles by this author Christian Corsini More articles by this author Francesco Lanzaro More articles by this author Luigi Candela More articles by this author Alessandro Bertini More articles by this author Massimiliano Raffo More articles by this author Fausto Negri More articles by this author Ludovica Cella More articles by this author Margherita Fantin More articles by this author Paolo Capogrosso More articles by this author Luca Boeri More articles by this author Alessia D'Arma More articles by this author Michael Eisenberg More articles by this author Luca Pagliardini More articles by this author Francesco Montorsi More articles by this author Andrea Salonia More articles by this author Expand All Advertisement PDF downloadLoading ...