基于Lasso-Logistic回归分析全子宫切除术后盆底功能障碍的危险因素并构建nomogram预测模型-东南大学学报医学版

基于Lasso-Logistic回归分析全子宫切除术后盆底功能障碍的危险因素并构建nomogram预测模型

单位：1. 邢台医学院第二附属医院东院区/邢台市第一医院妇科, 河北邢台 054001;
2. 邢台医学院第二附属医院东院区/邢台市第一医院产科, 河北邢台 054001

关键词：全子宫切除术盆底功能障碍 Lasso-Logistic回归影响因素 nomogram模型

分类号：R713.42

出版年·卷·期（页码）：2026·45·第一期（60-68）

摘要：

目的:基于Lasso-Logistic回归分析全子宫切除术后盆底功能障碍(PFD)的危险因素,并构建nomogram预测模型。方法: 将本院2023年8月至2024年12月收治的行全子宫切除术的310例患者随机分为建模组(n=217)和验证组(n=93),根据术后6个月内PFD发生情况将建模组患者分为非PFD组和PFD组。术后PFD的影响因素通过Lasso-Logistic回归分析筛选;nomogram模型的预测效能通过受试者工作特征(ROC)曲线及校准曲线评估,临床应用价值通过决策曲线分析(DCA)评估。结果: 310例患者全子宫切除术后PFD发生率为26.77%。建模组非PFD组与PFD组在年龄、体质量指数(BMI)、产次、经阴道分娩次数、术后短期并发症发生情况、术后盆底功能训练情况及术后是否早期负重方面差异均有统计学意义(P＜0.05)。Lasso-Logistic回归分析表明,年龄升高(OR=1.200,95%CI:1.110~1.297)和BMI升高(OR=1.479,95%CI:1.215~1.800)、产次＞2次(OR=3.502,95%CI:1.506~8.144)、术后发生短期并发症(OR=4.553,95%CI:1.896~10.936)及术后未有效进行盆底功能训练(OR=5.770,95%CI:2.447~13.606)为全子宫切除术后PFD的危险因素(P＜0.05)。nomogram模型显示,模型总得分越高,术后PFD发生风险越高。ROC分析显示,建模组、验证组的AUC分别为0.853(95%CI:0.801~0.904)、0.850(95%CI:0.763~0.938),且校准曲线提示模型的预测一致性较高。DCA曲线显示,模型的模型具有较高的临床实用性。结论: 全子宫切除术后PFD的发生与年龄、BMI、产次、术后有无短期并发症及术后是否有效进行盆底功能训练密切相关,基于这几个因素构建的nomogram模型具有较高的预测效能及临床应用价值。

Objective: To analyze the risk factors of pelvic floor dysfunction(PFD) after total hysterectomy based on Lasso-Logistic regression, and construct a nomogram prediction model. Methods: A total of 310 patients who underwent total hysterectomy in our hospital from August 2023 to December 2024 were randomly allocated into a modeling group(n=217) and a validation group(n=93). Based on the occurrence of PFD within 6 months after the surgery, the patients in the modeling group were divided into non-PFD group and PFD group. The influencing factors of postoperative PFD were screened through Lasso-Logistic regression analyses. The predictive efficacy of the nomogram model was evaluated by ROC and calibration curve, the clinical application value was evaluated through the decision curve analysis(DCA). Results: The incidence of PFD after total hysterectomy in 310 patients was 26.77%. There were significant differences between the non-PFD group and the PFD group in terms of age and body mass index(BMI), parity, number of vaginal deliveries, occurrence of short-term postoperative complications, postoperative pelvic floor function training, and whether having early weight-bearing after surgery(P<0.05). Lasso-Logistic regression analyses indicated that increased age(OR=1.200, 95%CI: 1.110-1.297), increased BMI(OR=1.479, 95%CI: 1.215-1.800), parity>2 times(OR=3.502, 95%CI: 1.506-8.144), short-term complications after surgery(OR=4.553, 95%CI: 1.896-10.936), and absence of effective postoperative pelvic floor function training after surgery(OR=5.770, 95%CI: 2.447-13.606) were risk factors for PFD after total hysterectomy(P<0.05). The nomogram model showed that the higher the total score of the model, the higher the risk of postoperative PFD. ROC analysis showed that the AUC of the modeling group and the validation group was 0.853(95%CI: 0.801-0.904) and 0.850(95%CI: 0.763-0.938), respectively, and the calibration curve indicated that the model had good predictive consistency. DCA showed that the model had high clinical practicability. Conclusion: The occurrence of PFD after total hysterectomy is closely related to age, BMI, parity, presence or absence of short-term postoperative complications, and whether pelvic floor function training was effectively performed after the operation. The nomogram model constructed based on these factors exhibits high predictive performance and clinical utility.

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