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开发和验证用于脑肿瘤切除术术中输血预测的工具:回顾性分析。

Development and validation of a prediction tool for intraoperative blood transfusion in brain tumor resection surgery: a retrospective analysis.

机构信息

Institution of Brain Disease and Neuroscience, Department of Anesthesiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.

Department of Laboratory, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, China.

出版信息

Sci Rep. 2023 Oct 13;13(1):17428. doi: 10.1038/s41598-023-44549-x.

Abstract

Early identification of a patient with a high risk of blood transfusion during brain tumor resection surgery is difficult but critical for implementing preoperative blood-saving strategies. This study aims to develop and validate a machine learning prediction tool for intraoperative blood transfusion in brain tumor resection surgery. A total of 541 patients who underwent brain tumor resection surgery in our hospital from January 2019 to December 2021 were retrospectively enrolled in this study. We incorporated demographics, preoperative comorbidities, and laboratory risk factors. Features were selected using the least absolute shrinkage and selection operator (LASSO). Eight machine learning algorithms were benchmarked to identify the best model to predict intraoperative blood transfusion. The prediction tool was established based on the best algorithm and evaluated with discriminative ability. The data were randomly split into training and test groups at a ratio of 7:3. LASSO identified seven preoperative relevant factors in the training group: hemoglobin, diameter, prothrombin time, white blood cell count (WBC), age, physical status of the American Society of Anesthesiologists (ASA) classification, and heart function. Logistic regression, linear discriminant analysis, supporter vector machine, and ranger all performed better in the eight machine learning algorithms with classification errors of 0.185, 0.193, 0.199, and 0.196, respectively. A nomogram was then established, and the model showed a better discrimination ability [0.817, 95% CI (0.739, 0.895)] than hemoglobin [0.663, 95% CI (0.557, 0.770)] alone in the test group (P = 0.000). Hemoglobin, diameter, prothrombin time, WBC, age, ASA status, and heart function are risk factors of intraoperative blood transfusion in brain tumor resection surgery. The prediction tool established using the logistic regression algorithm showed a good discriminative ability than hemoglobin alone for predicting intraoperative blood transfusion in brain tumor resection surgery.

摘要

早期识别脑肿瘤切除术中输血风险高的患者具有挑战性,但对于实施术前血液保存策略至关重要。本研究旨在开发和验证一种用于脑肿瘤切除术中输血的机器学习预测工具。本研究回顾性纳入了 2019 年 1 月至 2021 年 12 月在我院接受脑肿瘤切除术的 541 例患者。我们纳入了人口统计学、术前合并症和实验室危险因素。使用最小绝对收缩和选择算子(LASSO)选择特征。比较了八种机器学习算法,以确定预测术中输血的最佳模型。基于最佳算法建立了预测工具,并评估了其判别能力。数据随机分为训练组和测试组,比例为 7:3。LASSO 在训练组中识别出七个与术前相关的因素:血红蛋白、直径、凝血酶原时间、白细胞计数(WBC)、年龄、美国麻醉师协会(ASA)分类的身体状况和心脏功能。逻辑回归、线性判别分析、支持向量机和 ranger 在 8 种机器学习算法中的分类误差分别为 0.185、0.193、0.199 和 0.196,表现更好。然后建立了一个列线图,模型在测试组中显示出更好的判别能力[0.817,95%置信区间(0.739,0.895)],优于血红蛋白[0.663,95%置信区间(0.557,0.770)]单独使用(P=0.000)。血红蛋白、直径、凝血酶原时间、WBC、年龄、ASA 状态和心脏功能是脑肿瘤切除术中输血的危险因素。使用逻辑回归算法建立的预测工具在预测脑肿瘤切除术中输血方面比单独使用血红蛋白具有更好的判别能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2df9/10575918/22c590a4639a/41598_2023_44549_Fig1_HTML.jpg

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