基于随机森林模型的CT引导下肺穿刺活检出血风险的危险因素分析  

Analysis of risk factors for hemorrhage during CT-guided lung biopsy based on a random forest model

作　　者：李勇 赵晓辉 刘方 邢文阁 李凤娟[2] 史晋海 刘嘉馨[1] 杨成民[1,2] LI Yong;ZHAO Xiaohui;LIU Fang;XING Wenge;LI Fengjuan;SHI Jinhai;LIU Jiaxin;YANG Chengmin(Institue of Blood Transfusion,Chinese Academy of Medical Sciences&Peking Union Medical College,Chengdu 610052,China;Tianjin Xiehe Biotechnology Development Co Ltd,Tianjin 300457,China;Department of Interventional Therapy,Tianjin Medical University Cancer Hospital,Tianjin 300060,China)

机构地区：[1]中国医学科学院北京协和医学院输血研究所,四川成都610052 [2]天津协和生物科技发展有限公司,天津300457 [3]天津医科大学肿瘤医院介入治疗科,天津300060

出　　处：《中国输血杂志》2024年第10期1110-1114,1121,共6页Chinese Journal of Blood Transfusion

基　　金：中国医学科学院医学与健康科技创新工程项目“血液安全保障及新产品新技术研究”(2021-I2M-1-060)。

摘　　要：目的通过随机森林(random forest,RF)模型与传统Logistics回归分析相结合的方法,系统分析并筛选影响术后肺出血的关键危险因素,为临床实践提供数据支持。方法本研究纳入了2020年1月—2023年12月在天津医科大学肿瘤医院介入治疗科进行肺肿物穿刺活检术的844例患者(男性387名、女性457名),年龄39~82岁。研究收集患者的一般临床资料和穿刺相关特征,包括肿瘤大小、穿刺深度、穿刺角度、肺气肿情况、病灶位置、穿刺体位、是否经过叶间裂及穿刺次数等变量。利用RF模型对所有变量的重要性进行评分排序,识别出最具预测价值的变量。随后,采用多因素Logistics回归模型对排名靠前的重要变量进行进一步分析,评估其对术后肺出血的独立影响。结果RF模型结果显示,肿瘤大小和穿刺深度在预测术后肺出血风险中具有最高的重要性。多因素Logistics回归分析进一步证实,较小的肿瘤大小(HR:0.980,95CI%:0.971~0.989,P<0.05)与较低的出血风险显著相关,而较深的穿刺深度(HR:1.146,95CI%:1.063~1.235,P<0.05)则与较高的出血风险密切相关。此外,其他因素如穿刺角度、年龄、病灶位置及肺气肿情况等在分析中显示有一定影响,但在多因素回归分析中未显示出显著性。结论本研究结合RF模型和多因素Logistics回归分析,成功识别了肿瘤大小和穿刺深度为术后肺出血的独立危险因素。RF模型的应用提高了特征选择的准确性,帮助我们聚焦于对预测最具贡献的变量。这些发现为术前风险评估提供了重要的依据,建议临床医生在术前评估中重点考虑这些关键因素,以制定更安全、有效的手术计划,降低术后出血等并发症的风险。Objective To systematically analyze and identify key risk factors for postoperative pulmonary hemorrhage using a combination of the random forest(RF)model and traditional logistic regression analysis,so as to provide data support for clinical practice.Methods This study included patients who underwent needle biopsy of lung masses from January 2020 to December 2023 in the Department of Interventional Therapy,Cancer Hospital,Tianjin Medical University.There were 844 cases,including 387 males and 457 females,ranging in age from 39 to 82 years.Clinical data and puncture-related characteristics were collected,including tumor size,puncture depth,puncture angle,presence of emphysema,lesion location in the lung,body position during puncture,whether the puncture passed through the interlobar fissure,and the number of punctures.The RF model was used to rank the importance of all variables,identifying those with the highest predictive value.Subsequently,a multivariate logistic regression model was applied to the top-ranked important variables to further evaluate their independent impact on postoperative pulmonary hemorrhage.Results The RF model results showed that tumor size and puncture depth had the highest importance in predicting the risk of postoperative pulmonary hemorrhage.Multivariate logistic regression analysis further confirmed that smaller tumor size(HR:0.980,95%CI:0.971-0.989,P<0.05)was significantly associated with a lower risk of hemorrhage,while greater puncture depth(HR:1.146,95%CI:1.063-1.235,P<0.05)was closely related to a higher risk of hemorrhage.Additionally,other factors such as puncture angle,age,lesion location in the lung and presence of emphysema showed some influence but did not reach statistical significance in the multivariate analysis.Conclusion This study successfully identified tumor size and puncture depth as independent risk factors for postoperative pulmonary hemorrhage by combining the RF model with multivariate logistic regression analysis.The application of the RF model improved the ac

关 键 词：穿刺活检 出血 随机森林 风险预测 

分 类 号：R734.2[医药卫生—肿瘤] R457.1[医药卫生—临床医学]