HbA1c as a Key Metabolic Marker in Predicting Myomectomy Requirement in Women with Uterine Fibroids: A Machine Learning Study

<i>Background and Objectives</i>: Uterine fibroids are common benign tumors that frequently require surgical management, particularly myomectomy, in women of reproductive age. Metabolic dysfunction and insulin resistance have been implicated in fibroid biology; however, the clinical relevance of glycated hemoglobin (HbA1c) in predicting myomectomy requirement remains unclear. This study aimed to evaluate the predictive role of HbA1c for myomectomy requirement in women with uterine fibroids using conventional statistical analyses and machine learning-based models under real-world clinical decision-making conditions. <i>Materials and Methods</i>: This study evaluated data from a retrospective multicenter cohort comprising 618 women with a diagnosis of uterine fibroids. Patients were stratified according to myomectomy status (performed vs. not performed). Comparative analyses, univariate and multivariate logistic regression, and machine learning modeling were conducted using demographic, laboratory, hormonal, and fibroid-related variables. A total of 155 machine learning models were trained, and the top 20 models with the highest accuracy were evaluated. Blinded concordance analysis was conducted on 50 independent, anonymized cases evaluated by a gynecologist who was blinded to the study data. <i>Results</i>: Patients undergoing myomectomy (38.5%) had significantly higher HbA1c levels than non-surgical patients (5.57 ± 0.32 vs. 5.03 ± 0.61, <i>p</i> < 0.001). HbA1c showed a strong association with myomectomy requirement in univariate analysis (OR 0.026, 95% CI 0.012-0.055) but lost significance in multivariate models, while ferritin remained independently associated. Machine learning models incorporating HbA1c, ferritin, hormonal, and fibroid parameters achieved accuracies between 0.99 and 1.00. Blinded concordance analysis demonstrated 94% concordance between model predictions and expert clinical judgment. <i>Conclusions</i>: HbA1c is a valuable integrative marker in predicting myomectomy requirement when evaluated within multidimensional machine learning frameworks, although its independent effect is confounded by iron-related parameters. These findings support the use of HbA1c as part of a comprehensive decision-support approach in uterine fibroid management.