ArticleThe world journal of men's health2022
Algorithms for Predicting the Probability of Azoospermia from Follicle Stimulating Hormone: Design and Multi-Institutional External Validation.
Article in The world journal of men's health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Anti-Müllerian hormone predicts positive sperm retrieval in men with idiopathic non-obstructive azoospermia-findings from a multi-centric cross-sectional study.Human reproduction (Oxford, England) · 2023Pooled it
- Differentiating between obstructive and non-obstructive azoospermia: A machine learning-based approach.BJUI compass · 2025Article
- A new model for determining risk of male infertility from serum hormone levels, without semen analysis.Scientific reports · 2024Article
- Establishment and validation of a 5-factor diagnostic model for obstructive and non-obstructive azoospermia based on routine clinical parameters.Experimental biology and medicine (Maywood, N.J.) · 2024Observational
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11 authors.
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Abstract
purposeTo predict the probability of azoospermia without a semen analysis in men presenting with infertility by developing an azoospermia prediction model. MATERIALS AND
methodsTwo predictive algorithms were generated, one with follicle stimulating hormone (FSH) as the only input and another logistic regression (LR) model with additional clinical inputs of age, luteinizing hormone, total testosterone, and bilateral testis volume. Men presenting between 01/2016 and 03/2020 with semen analyses, testicular ochiodemetry, and serum gonadotropin measurements collected within 120 days were included. An azoospermia prediction model was developed with multi-institutional two-fold external validation from tertiary urologic infertility clinics in Chicago, Miami, and Milan.
resultsTotal 3,497 participants were included (n=Miami 946, Milan 1,955, Chicago 596). Incidence of azoospermia in Miami, Milan, and Chicago was 13.8%, 23.8%, and 32.0%, respectively. Predictive algorithms were generated with Miami data. On Milan external validation, the LR and quadratic FSH models both demonstrated good discrimination with areas under the receiver-operating-characteristic (ROC) curve (AUC) of 0.79 and 0.78, respectively. Data from Chicago performed with AUCs of 0.71 for the FSH only model and 0.72 for LR. Correlation between the quadratic FSH model and LR model was 0.95 with Milan and 0.92 with Chicago data.
conclusionsWe present and validate algorithms to predict the probability of azoospermia. The ability to predict the probability of azoospermia without a semen analysis is useful when there are logistical hurdles in obtaining a semen analysis or for reevaluation prior to surgical sperm extraction.
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