基于中文版便秘风险评估量表构建早中期肝细胞癌患者便秘风险的预测模型

Establishment of a risk prediction model for constipationin patients with early- and intermediate-stage hepatocellular carcinoma based on Chinese version of the Constipation Risk Assessment Scale

  • 摘要:
    目的 构建基于中文版便秘风险评估量表(CRAS-C)的早中期肝细胞癌(HCC)患者术后便秘预测模型,并进行效能验证。
    方法 本研究为前瞻性研究,采用连续抽样法,选取2023年1月—2024年5月162例就诊于南京大学医学院附属鼓楼医院肝胆外科首次行肝切除术或经导管动脉化疗栓塞术(TACE)的HCC患者为研究对象,在手术当日使用CRAS-C评估术后便秘风险,通过医院信息系统收集患者临床资料。使用最小绝对收缩与选择算子回归(LASSO)筛选预测变量,通过Firth惩罚最大似然估计建立预测模型,绘制列线图。通过Bootstrap法进行内部验证; 采用受试者工作特征(ROC)曲线、校准曲线和Hosmer-Lemeshow检验评估模型区分度和校准度; 采用决策曲线分析(DCA)评估模型的临床实用性。
    结果 早中期HCC患者术后便秘发生率为56.79%, 结直肠疾病、不习惯使用便盆、女性、阿片类药物和治疗方式是术后便秘的独立危险因素(P < 0.05)。构建的预测模型的曲线下面积(AUC)值为0.849, 敏感度为0.794, 特异度为0.814; 经Bootstrap内部验证后, AUC值为0.838。校准曲线显示预测概率与实际概率接近, Hosmer-Lemeshow检验结果提示模型校准度良好(P=0.057)。DCA曲线表明预测模型具备良好的临床实用价值。
    结论 本研究构建的早中期HCC患者便秘预测模型具有较好的预测效能,可辅助临床医护人员识别术后便秘的高危患者,为尽早采取防控措施提供依据。

     

    Abstract:
    Objective To construct a postoperative constipation prediction model for patients with early- and intermediate-stage hepatocellular carcinoma (HCC) based on the Chinese version of the Constipation Risk Assessment Scale (CRAS-C) and validate its efficacy.
    Methods This prospective study employed consecutive sampling to select 162 HCC patients who underwent their first hepatic resection or transcatheter arterial chemoembolization (TACE) in the Department of Hepatobiliary Surgery at Nanjing Drum Tower Hospital Affiliated to Medical School of Nanjing University from January 2023 to May 2024. The CRAS-C was used on the day of surgery to assess the risk of postoperative constipation, and clinical data of the patients were collected through the hospital information system. The least absolute shrinkage and selection operator regression (LASSO) was used to screen predictive variables. A prediction model was established using Firth's penalized maximum likelihood estimation, and a nomogram was plotted. Internal validation was performed using the Bootstrap method. The model's discrimination and calibration were evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow test. The clinical utility of the model was assessed using decision curve analysis (DCA).
    Results The incidence of postoperative constipation in patients with early- and intermediate-stage HCC was 56.79%. Colorectal diseases, unfamiliarity withusing bedpans, female, opioid use, and treatment modality were independent risk factors for postoperative constipation (P < 0.05). The constructed prediction model had an area under the curve (AUC) value of 0.849, a sensitivity of 0.794, and a specificity of 0.814. After Bootstrap internal validation, the AUC value was 0.838. The calibration curve showed that the predicted probabilities were close to the actual probabilities, and the Hosmer-Lemeshow test result indicated good model calibration (P=0.057). The DCA curve demonstrated that the prediction model had good clinical practical value.
    Conclusion The postoperative constipation prediction model for patients with early- and intermediate-stage HCC constructed in this study exhibits good predictive efficacy, which can assist clinical healthcare professionals in identifying patients at high risk of postoperative constipation and provide a basis for taking preventive and control measures as early as possible.

     

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