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Development and validation predictive models of sperm retrieval for azoospermic men undergoing testicular sperm aspiration: a multicentre, retrospective, cohort study

  • Assisted Reproduction Technologies
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Abstract

Purpose

Testicular sperm aspiration (TESA) is widely used to retrieve sperm from testis. Diagnostic testicular biopsy should not be routinely performed for azoospermia. Therefore, a good predictive model is needed before TESA.

Methods

A total of 1972 azoospermia patients constituted the modelling set, and 260 azoospermia patients from two other centres constituted the validation set. An integrated predictive model was built using logistic regression. Receiver operating characteristic (ROC), calibration and decision curve analyses were performed to evaluate the performance of follicle-stimulating hormone (FSH), semen volume, testicular volume and the integrated model.

Results

The FSH level was the best univariate predictor for successful sperm retrieval (SSR) and was better than semen volume and testicular volume alone (p<0.001, threshold 6.17 IU/L, modelling set area under receiver operating characteristic curve (AUC) 0.80, accuracy 0.79; validation set AUC 0.87, accuracy 0.78). The integrated predictive model had excellent accuracy for predicting SSR (modelling set: AUC 0.93, accuracy 0.89; validation set: AUC 0.96, accuracy: 0.89). Calibration curve analysis indicated that the integrated model calibration was good and better than that of FSH, semen volume and testicular volume alone. Decision curve analysis indicated with a threshold probability between 0.05 and 0.98, the integrated model added more benefit than treating either all or no patients.

Conclusions

The integrated model has excellent discrimination and good calibration. It can help azoospermic men make better decisions before TESA. It should be noted that TESA is not the first-line treatment for non-obstructive azoospermia because of a low sperm retrieval rate.

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Data availability

Data are available from the corresponding author on reasonable request.

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Funding

This study was funded by the National Key Research and Development Plan (2018YFC1002702) and the National Natural Science Foundation (81471495).

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Authors and Affiliations

  1. Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China, 310006

    Jing-Ping Li, Feng-Bin Zhang, Le-Jun Li, Jing-Gen Wu, Yong-Hong Tian, Zhong-Yan Liang & Fan Jin

  2. Reproductive Medicine Centre, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, People’s Republic of China

    Cong-Qi Du

  3. Reproductive Medicine Centre, Jinhua People’s Hospital, Jinhua, People’s Republic of China

    Zu-Bo Liu

  4. Department of Ultrasound, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou, People’s Republic of China

    Chong Chen

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  1. Jing-Ping Li
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Correspondence to Fan Jin.

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Ethics approval

This study was approved by the Ethics Committee of Women’s Hospital, School of Medicine, Zhejiang University (Project number: IRB-20210115-R).

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Due to the retrospective nature of the study, informed consent could not be obtained from the patients involved. But clinical data were analyzed anonymously.

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Supplementary information

Supplemental Fig. 1

Flow chart of patient selection in this retrospective cohort study. (PNG 67 kb)

High resolution image (TIF 103 kb)

Supplemental Fig. 2

Clinical variable selection using the LASSO binary logistic regression model. (A) Selection of the optimal parameter (lambda) in the LASSO model used a 10-fold cross-validation error curve and was based on 1 standard error of the minimum criteria (1-SE criteria). The partial likelihood deviance (binomial deviance) curve was plotted versus log (lambda). The dotted lines were drawn at the optimal values based on the minimum criteria and 1-SE criteria. (B) A coefficient profile plot was constructed against the log (lambda) parameters. Five variables with nonzero coefficients were selected by deriving the optimal lambda (PNG 791 kb)

High resolution image (TIF 5559 kb)

Supplemental Fig. 3

ROC curve analysis of FSH, semen volume, testicular volume and integrated model in the modelling set and external validation set. (A) ROC curve for the modelling set; (B) ROC curve for the external validation set. The integrated model was excellent for predicting SSR and better than FSH, semen volume and testicular volume (p< 0.001) both in modelling and external validation sets. ROC: receiver operating characteristic, AUC: area under the curve (PNG 431 kb)

High resolution image (TIF 4094 kb)

Supplemental Fig. 4

Decision curve analysis of FSH, testicular volume and integrated model in the modelling set and external validation set. (A) Decision curve analysis for the modelling set. (B) Decision curve analysis for the external validation set. The net benefit of the integrated model was superior to FSH, semen volume and testicular volume in both the modelling and validation sets (PNG 527 kb)

High resolution image (TIF 308 kb)

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Li, JP., Du, CQ., Liu, ZB. et al. Development and validation predictive models of sperm retrieval for azoospermic men undergoing testicular sperm aspiration: a multicentre, retrospective, cohort study. J Assist Reprod Genet 39, 1779–1787 (2022). https://doi.org/10.1007/s10815-022-02531-y

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