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作 者:曹娟[1] 李方[1] 于跃[1] 戴丽 杨丹丹[1] 李志华[1] 徐欣怡 戴琪 陈柯宇 CAO Juan;LI Fang;YU Yue;DAI Li;YANG Dan-dan;LI Zhi-hua;XU Xin-yi;DAI Qi;CHEN Ke-yu(Dept.of Thoracic Surgery,The First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China;School of Nursing,Nanjing Medical University,Nanjing 211166,China)
机构地区:[1]南京医科大学第一附属医院胸外科,江苏南京210029 [2]南京医科大学护理学院,江苏南京211166
出 处:《护理学报》2024年第8期63-68,共6页Journal of Nursing(China)
基 金:国家自然科学基金资助项目(81902453);江苏省人民医院临床能力提升工程项目(JSPH-NC-2021-22)。
摘 要:目的构建食管癌患者术后静脉血栓栓塞症风险预测模型。方法采用便利抽样选取2020年1月-2022年4月在某三级甲等医院行食管静脉血栓栓塞症癌根治术的220例患者作为建模组,分为静脉血栓栓塞症组60例和非静脉血栓栓塞症组160例。采用Logistic回归分析建立食管癌患者术后静脉血栓栓塞症风险预测模型并对模型验证。结果年龄≥60岁、基础疾病、新辅助化疗、手术时长、术后使用凝血酶原复合物(prothrombin complex concentrates,PCC)、术后12 h D-D是食管癌患者术后静脉血栓栓塞症危险因素。HL检验P=0.250,受试者工作曲线特征曲线下面积(area under curve,AUC)为0.766,最佳临界值为0.445,灵敏度为0.783,特异度为0.662。验证组AUC为0.738,灵敏度为0.682,特异度为0.769,正确率为80.0%。结论该预测模型的预测效果良好,可为临床评估食管癌患者术后静脉血栓栓塞症发生风险提供参考。Purpose To construct a venous thromboembolism(VTE)risk prediction model for postoperative patients with esophageal cancer.Methods Convenience sampling was used to select 220 patients who underwent esophageal cancer surgery in a tertiary grade-A hospital from January 2020 to April 2022 as the modeling group,with 60 in the VTE group and 160 in the non-VTE group.Logistic regression analysis was used to establish the VTE risk prediction model,which was validated by a separate validation group.Results Risk factors for postoperative VTE in patients with esophageal cancer included age≥60 years,comorbidities,neoadjuvant chemotherapy(NACT),operative duration,postoperative use of prothrombin complex concentrates(PCC),and postoperative D-D at 12 hours.The model showed good predictive accuracy.In the modeling group,HL test showed P=0.250,area under receiver operating characteristic curve(AUC)was 0.766,the best cutoff value was 0.445,the sensitivity was 0.783,the specificity was 0.662.In the validation group,AUC was 0.738,the sensitivity was 0.682,the specificity was 0.769,the prediction accuracy was 80%.Conclusion The prediction model has demonstrated good predictive performance and can provide reference for assessing VTE risk in postoperative patients with esophageal cancer.
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