基于神经网络的肺癌根治术后胸水乳糜定性试验阳性预警模型的构建与应用
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1.南京医科大学第一附属医院胸外科;2.南京医科大学

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Development and validation of an early-warning model based on neural network for predicting positive chyle test in pleural effusion of patients accepted lung cancer surgery
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    摘要:

    目的:建立肺癌术后胸水乳糜试验阳性自动预警模型并评价其预警效能。方法:前瞻性纳入2018年1-12月在南京医科大学第一附属医院胸外科拟行肺癌手术治疗的患者700例,收集其一般资料、术后生理相关指标、胸水引流情况等,通过单因素分析、多因素分析筛选出术后胸水乳糜试验阳性的独立危险因素,带入SPSS 19.0软件构建二元Logistic回归模型、神经网络模型、决策树模型,并通过Medcalc软件比较三者曲线下面积大小,以选取最优模型。前瞻性纳入2019年1-3月在南京医科大学第一附属医院胸外科拟行肺癌手术治疗的患者131例患者进行模型验证,在模型验证阶段通过计算灵敏度、特异度、阳性预警值、阴性预警值和总体正确率来判断模型的预警效能。结果:二元 Logistic 回归、神经网络、决策树模型的曲线下面积分别为0.854、0.980、0.835,两两比较后神经网络模型为最优模型,差异具有统计学意义。最终模型共纳入8个变量,重要性由大到小依次为胸水甘油三酯、BMI、肺切除范围、肿瘤N分期、术后白蛋白、年龄、淋巴结清扫发生、肿瘤T分期。通过验证其灵敏度89.9%、特异度91.9%、阳性预测值92.5%、阴性预测值89.1%、总正确率90.8%。结论:BP神经网络构建的乳糜阳性预警模型科学严谨,具有良好的预测效能,可早期筛选高危人群,为肺癌患者围术期营养管理模式的构建奠定基础。

    Abstract:

    Objective: To establish an automatic early-warning model for predicting positive chyle test in pleural effusion of patients accepted lung cancer surgery and evaluate its early warning efficiency. Methods: 700 patients who underwent lung cancer surgery in the Department of Thoracic Surgery, the First Affiliated Hospital of Nanjing Medical University from January to December 2018 were enrolled for a prospective analysis. The general data, postoperative physiological indicators and drainage of pleural fluid were collected,independent risk factors of positive chyle test in postoperative pleural effusion were screened out by univariate and multivariate analysis. The binary Logistic regression model, neural network model and decision tree model were constructed by using SPSS 19.0 software,and the values of their area under curve (AUC) were compared by Medcalc software to select an optimal model. Finally, 131 patients accepted lung cancer surgery from the same hospital were included to test the optimal model. In the validation stage, the early-warning efficiency of the optimal model was judged by calculating sensitivity, specificity, positive predictive value, negative predictive value and overall accuracy. Results: The AUC for the binary logistic regression model,BP neural network model and decision tree model was 0.854,0.980,0.835, respectively. The neural network model was selected as the optimal model, and the difference was statistically significant after pairwise comparison. A total of 8 variables were included, in descending order of importance: pleural triglyceride, BMI, pulmonary resection range, N stage of tumor, postoperative albumin, age, occurrence of lymph node dissection, and T stage of tumor. In the validation group, its sensitivity was 89.9%, specificity was 91.9%, positive predictive value was 92.5%, negative predictive value was 89.1%, and the total accuracy was 90.8%. Conclusion: The early-warning model constructed by neural network for predicting positive chyle test in pleural effusion is scientific and effective, which can be used for screening high-risk patients with chylothorax early. Our study lays a foundation for the construction of perioperative nutrition management for lung cancer patients.

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  • 收稿日期:2020-01-06
  • 最后修改日期:2021-09-06
  • 录用日期:2021-10-29
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