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非梗阻性无精子症男性成功取精的综合预测模型。

An integrative prediction model of successful sperm retrieval for men with non-obstructive azoospermia.

机构信息

Department of Urology, the First Affiliated Hospital of Nanjing Medical University, 210029 Nanjing, Jiangsu, China.

出版信息

Rev Int Androl. 2024 Sep;22(3):48-56. doi: 10.22514/j.androl.2024.021. Epub 2024 Sep 30.

Abstract

Microdissection testicular sperm extraction (micro-TESE) is an efficient method for obtaining spermatozoa from patients with non-obstructive azoospermia, but the overall success rate of this surgery is only approximately one-third. This study aimed to construct an integrative prediction model for andrologists to assess the preoperative success retrieval rate. A total of 217 patients diagnosed with non-obstructive azoospermia at the First Affiliated Hospital of Nanjing Medical University were included, in whom sperm was successfully retrieved in 71 patients. We retrospectively analyzed their clinical characteristics and pathological features. Single factor analysis and logistic regression analysis were utilized to validate the predictive performance, and the area under the curve (AUC) analysis was conducted to further assess the clinical diagnostic value of the model. The results showed that a history of Klinefelter syndrome or cryptorchidism, FSH (Follicle Stimulating Hormone) levels, and testicular pathology contributed differently to the nomogram prediction model. Relatively normal FSH levels, a history of Klinefelter syndrome or cryptorchidism, and favorable testicular pathological types were assigned higher scores, with higher scores often accompanying a preferable success rate of sperm retrieval. The integrated model showed good prediction performance, with an AUC (Area Under the Curve) of 0.781 (95% CI (confidence interval) 0.713-0.849). Overall, our integrative model demonstrates excellent prediction performance and may assist andrologists in balancing the benefits of surgery preoperatively.

摘要

睾丸组织精子提取的显微外科手术(micro-TESE)是从非梗阻性无精子症患者中获取精子的有效方法,但该手术的总体成功率仅约为三分之一。本研究旨在构建一个综合预测模型,供男科医生评估术前成功取精率。共纳入 217 例在南京医科大学第一附属医院诊断为非梗阻性无精子症的患者,其中 71 例成功取精。我们回顾性分析了他们的临床特征和病理特征。采用单因素分析和逻辑回归分析验证预测性能,并进行曲线下面积(AUC)分析进一步评估模型的临床诊断价值。结果表明,克氏综合征或隐睾病史、FSH(卵泡刺激素)水平和睾丸病理对列线图预测模型有不同的贡献。相对正常的 FSH 水平、克氏综合征或隐睾病史和有利的睾丸病理类型赋予更高的分数,较高的分数通常伴随着更好的精子获取成功率。综合模型显示出良好的预测性能,AUC(曲线下面积)为 0.781(95%CI 0.713-0.849)。总的来说,我们的综合模型显示出良好的预测性能,可能有助于男科医生在术前权衡手术的获益。

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