非肌层浸润性膀胱癌患者经尿道膀胱肿瘤电切术后发生膀胱灌注并发症的预测模型构建与验证

Construction and validation of a predictive model for bladder perfusion-related complications after transurethral resection of non-muscle-invasive bladder cancer

摘要:

目的 构建并验证非肌层浸润性膀胱癌（NMIBC）患者经尿道膀胱肿瘤电切术（TURBT）后发生膀胱灌注并发症的预测模型。方法 回顾性分析2021年6月—2025年2月在连云港市第二人民医院接受TURBT治疗的106例NMIBC患者，根据术后是否发生膀胱灌注并发症分为并发症组（22例）和非并发症组（84例）。收集两组患者临床特征、手术指标及实验室指标，采用LASSO回归方法筛选变量，通过多因素一般Logistic回归模型分析独立危险因素，构建预测模型并绘制列线图，通过受试者工作特征（ROC）曲线、Bootstrap内部验证及决策曲线分析（DCA）评估模型的判别力、校准性与临床实用性。结果 并发症组糖尿病患病率、手术时间、术中出血量和肿瘤直径均高于非并发症组（P <0.05），白蛋白（Alb）水平低于非并发症组（P <0.05）。多因素一般Logistic回归分析结果显示，糖尿病［O^R=6.923（95% CI：1.110，43.167）］、术前Alb水平低［O^R=0.576（95% CI：0.418，0.794）］、手术时间长［O^R=1.063（95% CI：1.003，1.127）］、术中出血量大［O^R=1.152（95% CI：1.041，1.274）］、肿瘤直径大［O^R=2.857（95% CI：1.076，7.588）］均为NMIBC患者TURBT术后发生膀胱灌注并发症的危险因素（P <0.05）。ROC曲线结果显示，列线图预测NMIBC患者TURBT术后发生膀胱灌注并发症的曲线下面积为0.939（95% CI：0.887，0.991），敏感性为81.8%（95% CI：0.597，0.948），特异性为90.5%（95% CI：0.821，0.958）；决策曲线分析结果显示，该模型在阈值概率为10%～95%，能够提供显著的临床净收益，临床影响曲线结果表明该模型具有良好的临床适用性与风险分层能力。结论 基于术前、术中关键指标构建的预测模型可有效识别NMIBC患者TURBT术后发生膀胱灌注并发症的高危人群，具有较高的预测准确性和临床应用价值。

Abstract:

Objective To construct and validate a predictive model for bladder perfusion-related complications following transurethral resection of bladder tumor (TURBT) in patients with non-muscle-invasive bladder cancer (NMIBC).Methods A retrospective cohort of 106 NMIBC patients undergoing TURBT at our hospital between June 2021 and February 2025 was selected. Patients were divided into a complication group (n = 22) and a non-complication group (n = 84) based on the occurrence of postoperative bladder perfusion-related complications. Clinical characteristics, surgical parameters, and laboratory indicators were collected. Variables were screened using LASSO regression, and independent risk factors were identified via multivariable logistic regression analysis. A predictive model was constructed with a nomogram. The discriminative power, calibration, and clinical utility of the model were assessed through receiver operating characteristic (ROC) curve analysis, Bootstrap internal validation, and decision curve analysis (DCA).Results The complication group exhibited higher prevalence of diabetes mellitus, longer operative duration, greater intraoperative blood loss, and larger tumor diameter compared to the non-complication group (P < 0.05), while presenting lower albumin levels (P < 0.05). Multivariable logistic regression analysis revealed that concurrent diabetes mellitus [O^R = 6.923 (95% CI: 1.110, 43.167) ], low preoperative albumin levels [O^R = 0.576 (95% CI: 0.418, 0.794) ], prolonged surgery duration [O^R = 1.063 (95% CI: 1.003, 1.127) ], increased intraoperative blood loss [O^R = 1.152 (95% CI: 1.041, 1.274) ], and large tumor diameter [O^R = 2.857 (95% CI: 1.076, 7.588) ] were all risk factors for postoperative bladder perfusion-related complications in NMIBC patients undergoing TURBT (P < 0.05). The ROC curve analysis demonstrated that the AUC of the nomogram for predicting postoperative bladder perfusion-related complications was 0.939 (95% CI: 0.887, 0.991), with a sensitivity of 81.8% (95% CI: 0.597, 0.948) and a specificity of 90.5% (95% CI: 0.821, 0.958). The DCA revealed that the model provided significant clinical net benefit across a threshold probability range of 10% to 95%. Clinical impact curve analysis demonstrated that the model had good clinical applicability and risk stratification ability.Conclusion This study established a predictive model based on preoperative and intraoperative indicators, effectively identifying high-risk populations for postoperative bladder perfusion-related complications following TURBT in patients with NMIBC. The model exhibits high predictive accuracy and clinical utility.