ArticleAmerican journal of translational research2025
Predictive model construction for evaluating treatment outcomes of microscopic left internal spermatic vein reflux reconstruction plus left spermatic vein high ligation for grade III left varicocele and influencing factor prioritization.
Article in American journal of translational research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo investigate the factors influencing the efficacy of microscopic left internal spermatic vein reflux reconstruction (LISVRR) plus left spermatic vein high ligation (LSVHL) for grade III left varicocele.
methodsA total of 278 patients with degree III left-sided varicocele who underwent surgery at Ganzhou People's Hospital between January 2023 and December 2024 were retrospectively enrolled. Patients were randomly divided into a training cohort (70%, n = 195) and a validation cohort (30%, n = 83). General and clinical parameters were collected. Univariate analysis was first performed to identify potential predictors, followed by multivariate logistic regression to determine independent risk factors. Variable importance was ranked using the random forest algorithm. A nomogram prediction model was then developed, and its performance and clinical utility were evaluated using the concordance index (C-index) and decision curve analysis (DCA).
resultsMultivariate analysis revealed that internal diameter of the spermatic vein, testosterone level and intraoperative blood loss were independent predictors of postoperative efficacy (P<0.05). The nomogram showed good discrimination in both the training (C-index = 0.794) and validation (C-index = 0.748) cohorts. DCA validated the model's clinical applicability.
conclusionInternal diameter of the spermatic vein, testosterone level, blood flow velocity of the spermatic vein, and intraoperative blood loss are key factors affecting surgical efficacy of LISVRR plus LSVHL for grade III left varicocele. The developed prediction model demonstrates good predictive performance and potential clinical value.
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