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基于发热预测模型预测软镜碎石术后发热的研究 被引量:1

Study on predicting postoperative fever of RIRS based on fever prediction model
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摘要 目的:验证逆行输尿管软镜碎石术(Retrograde intrarenal surgery,RIRS)术后发热预测模型的有效性和可靠性。方法:选取2021年5月至2022年10月在我院接受RIRS治疗的114例患者作为研究对象。所有预测指标均由术前常规检查获得。通过受试者操作特性曲线(ROC)和曲线下面积(AUC)比较验证模型的预测能力。结果:114例患者中,72例男性,42例女性,年龄M(Q1,Q3)为51(43,58)岁,体重指数M(Q1,Q3)为22.6(20.6,24.5)kg·m^(-2)。预测模型总评分为83(66,94)。19例患者(16.7%)出现术后发热。术后发热组与术后无发热组比较,除尿亚硝酸盐阳性(P=0.041)、尿培养阳性(P=0.011)和预测模型的总分差异有统计学意义(P<0.001)外,其余指标差异均无统计学意义。ROC结果显示,模型具有较好的预测准确性和有效性,曲线下面积(AUC)值为0.872。结论:预测模型对RIRS术后发热的预测能力有效可靠。 Objective:To verify the validity and reliability of the fever prediction model after retrograde ureteroscopic lithotripsy(RIRS).Methods:A total of 114 patientsreceived RIRS treatment in our hospital from May 2021 to October 2022 were included.All predictors were obtained by routine preoperative examination.The prediction ability of the model was verified by comparing ROC and AUC.Results:There were 72 males and 42 females,with a median age of 51(43,58)yearsold and a median BMI of 22.6(20.6,24.5)kg·m^(-2).The total score of the prediction model was 83(66,94).Finally,19 patients(16.7%)had postoperative fever.There was no statistical difference between the postoperative fever group and the postoperative non-fever group,except for the urine nitrite positive(P=0.041),the urine culture positive(P=0.011)and the total score of the prediction model(P<0.001),with statistical significe.The ROC results showed that the model had good prediction accuracy and effectiveness,and the AUC value was 0.872.Conclusion:It is believedthatthe prediction model is effective and reliable for the prediction of fever after RIRS.
作者 吴检发 祝家兴 罗雪理 李优苼 廖文成 徐开鑫 钟功荣 林峰 WU Jian-fa;ZHU Jia-xing;LUO Xue-i;LI You-sheng;LIAO Wen-cheng;XU Kai-xin;ZHONG Gong-rong;LIN Feng(Department of Urology,People's Hospital of Ganxian,Ganzhou,Jiangxi 341000)
出处 《赣南医学院学报》 2023年第5期493-496,共4页 JOURNAL OF GANNAN MEDICAL UNIVERSITY
基金 赣州市指导性科技计划项目(GZ2021ZSF403)。
关键词 输尿管软镜碎石术 泌尿系结石 术后发热 预测模型 Retrograde intrarenal surgery Urinary calculus Postoperative fever Prediction model
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