天津医药

天津医药 ›› 2019, Vol. 47 ›› Issue (9): 975-978.doi: 10.11958/20182210

• 临床研究 • 上一篇    下一篇

预测输尿管结石进展为尿脓毒血症的列线图模型的外部验证

胡明，杨云杰△，赵振华，石明，徐勋，张湛英，关礼贤，冯权尧   

  1. 基金项目：佛山市卫计局医学科研课题（20180271） 作者单位：南方医科大学附属南海医院泌尿外科（邮编528200） 作者简介：胡明（1981），男，博士，主任医师，主要从事泌尿外科微创手术治疗方面研究 △通讯作者 E-mail: pop20000@foxmail.com
  • 收稿日期:2019-01-02 修回日期:2019-07-19 出版日期:2019-09-15 发布日期:2019-09-18
  • 通讯作者: 胡明 E-mail:3110478@qq.com
  • 基金资助:
    佛山市卫计局医学科研课题立项

External validation of nomogram for predicting the probability of urosepsis progressed from ureteral calculi

HU Ming, YANG Yun-jie△, ZHAO Zhen-hua, SHI Ming, XU Xun, ZHANG Zhan-ying, GUAN Li-xian, FENG Quan-yao   

  1. Department of Urology, the Affiliated Nanhai Hospital of Southern Medical University, Foshan 528200, China △Corresponding Author E-mail: pop20000@foxmail.com
  • Received:2019-01-02 Revised:2019-07-19 Published:2019-09-15 Online:2019-09-18

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摘要： 目的 对已建立的输尿管结石进展为尿脓毒血症的预测模型进行外部验证，明确该预测模型是否适用于 临床实践。方法 收集2016年1—12月我院收治的输尿管结石患者317例，其中进展为尿脓毒血症者29例（尿脓毒 血症组），未进展为尿脓毒血症者288例（非尿脓毒血症组）。采用我科建立的预测模型，通过患者性别、功能性孤立 肾、肾积液平均CT值、尿白细胞计数（WBC）及尿亚硝酸盐等指标对2组患者进行尿脓毒血症风险预测，比较预测结 果与实际观测结果之间的差异。分别利用受试者工作特征（ROC）曲线和GiViTI校准曲线带验证预测模型的区分度 和校准度。结果 预测模型外部验证的ROC曲线下面积（AUC）=0.874（95%CI：0.804~0.945），能较好地将尿脓毒血 症结局患者区分出来。GiViTI校准曲线带的95%CI区域均未穿过45°对角平分线（P=0.176），预测模型的预测概率与 实际观测概率接近，校准度良好。结论 模型预测输尿管结石进展为尿脓毒血症风险概率的准确性高，有助于提高 此类高危患者的早期识别和筛选能力。

关键词: 输尿管结石, 尿脓毒血症, 预测模型, 外部验证

Abstract: Objective To determine whether the nomogram for predicting the probability of urosepsis progressed from ureteral calculi was generally applicable to clinical practice. Methods The clinical data of 317 patients with ureteral calculi in our department from January 2016 to December 2016 were collected in this study. A total of 29 patients with urosepsis were selected into the urosepsis group, and the other 288 patients without urosepsis in the same period were selected into the non-urosepsis group. Using the prediction model developed before, the probability of urosepsis was predicted by gender, functional solitary kidney, average CT value of hydronephrosis, urine WBC count and urine nitrite in two groups, and the difference between the predicted and observed probabilities was compared. The discrimination and calibration of the prediction model were validated by using ROC and GiViTI calibration curve belts, respectively. Results The area under ROC curve (AUC) was 0.874 (95%CI: 0.804-0.945), suggesting that the prediction model could distinguish the patients with urosepsis. The 95%CI region of GiViTI calibration belt did not cross the 45-degree diagonal bisector line (P=0.176). Therefore, the prediction probability of the model was consistent with the actual probability, which suggested that prediction model had strong concordance performance. Conclusion The accuracy of the model in predicting the risk of urosepsis in ureteral calculi is improved, and it was helpful to improve the early identification and screening ability of those high-risk patients.

Key words: ureteral calculi, urosepsis, prediction model, external validation