• 中国科学论文统计源期刊
  • 中国科技核心期刊
  • 美国化学文摘(CA)来源期刊
  • 日本科学技术振兴机构数据库(JST)

临床输血与检验 ›› 2023, Vol. 25 ›› Issue (6): 743-748.DOI: 10.3969/j.issn.1671-2587.2023.06.005

• 临床输血 • 上一篇    下一篇

食管癌围手术期患者输血预测模型的建立*

陈珊, 刘景福, 李珍, 陈玉娟, 叶先仁   

  1. 福建医科大学肿瘤临床医学院,福建省肿瘤医院输血科, 350014
  • 收稿日期:2023-07-26 出版日期:2023-12-20 发布日期:2024-01-15
  • 通讯作者: 叶先仁,主要从事免疫血液学和输血医学的研究,(E-mail)yexianren260117@fjmu.edu.cn。
  • 作者简介:陈珊,主要从事输血医学研究,(E-mail)csacjl@163.com。
  • 基金资助:
    *本项目受福建省科技创新联合资金项目(No.2021Y9205)资助

Establishment of A Blood Transfusion Prediction Model for Perioperative Patients with Esophageal Cancer

CHEN Shan, LIU Jingfu, LI Zhen, et al   

  1. Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital 350014
  • Received:2023-07-26 Online:2023-12-20 Published:2024-01-15

摘要: 目的 构建食管癌患者围手术期输血预测模型,为肿瘤患者血液管理的实施提供理论依据。方法 回顾性收集2008年1月—2020年12月在福建省肿瘤医院首次行食管癌切除术的患者资料,按7∶3的比例将收集的临床数据分为建模集和验证集。利用建模集单因素和多因素Logistic回归分析筛选变量,构建列线图预测模型。使用两组数据的ROC曲线和校准曲线来评估模型性能,最后通过决策曲线分析证实该模型的临床效用。结果 共980名患者纳入研究,多因素Logistic分析发现术前白蛋白、血红蛋白、手术方式、胸腔引流液的量是影响食管癌围手术期患者输血的独立危险因素(P<0.05)。基于危险因素建立列线图模型,建模集的ROC曲线下面积为0.712,验证集的ROC曲线下面积为0.706。两组的校正曲线均与标准曲线拟合较好,模型具有良好的预测精准性,最后通过决策曲线分析证实该模型具有良好的临床效用。结论 本研究通过筛选风险因素,构建列线图预测模型,能有效的预测食管癌围手术期患者输血高风险人群,为食管癌患者的血液管理提供参考,提高临床用血的合理性、安全性和准确性。

关键词: 围手术期输血, 食管癌, 预测模型, 患者血液管理

Abstract: Objective In this study, we built a blood transfusion prediction model for perioperative patients with esophageal cancer to provide reference for patient blood management. Methods We retrospectively collected the data of patients esophageal cancer resection at Fujian Cancer Hospital from January 2008 to December 2020. The data was divided into a modeling set and a validation set in a 7∶3 ratio. Univariable and multivariable logistic regression analysis were performed to screen variables, and a nomogram prediction model was constructed. Receiver operating characteristic (ROC) and calibration curve were used to evaluate the performance of the model. Finally, the clinical utility of the model was confirmed through the decision curve. Results A total of 980 patients were enrolled in the study. Multivariable logistic regression analysis showed that preoperative albumin, hemoglobin, surgical method, and the amount of pleural drainage fluid were independent risk factors for perioperative blood transfusion in patients with esophageal cancer. The nomogram model is established based on risk factors. The area under the ROC of the modeling set and the validation set is 0.712 and 0.706, respectively. The calibration curves of both groups fit well with the standard curve, and the model has good predictive accuracy. Finally, decision curve analysis confirms that the model has good clinical utility. Conclusion We constructed a nomogram prediction model by screening risk factors. It can effectively predict the high-risk population for perioperative blood transfusion in patients with esophageal cancer, provided reference for patient blood management, and improved the rationality, safety, and accuracy of clinical blood use.

Key words: Perioperative blood transfusion, Esophageal cancer, Prediction model, Patient blood management

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