老年冠心病合并肠道恶性肿瘤患者的出血风险预测研究

Prediction of bleeding risk in elderly patients with coronary heart disease and intestinal malignancies

目的基于单中心临床数据建立老年冠心病合并肠道恶性肿瘤患者的出血事件预警评分。方法回顾性选取中国人民解放军总医院大数据中心临床数据库自2008年1月至12月入院治疗的老年冠心病合并肠道恶性肿瘤患者的临床数据作为模型训练组。以临床显著出血事件为研究终点事件,对临床数据进行基线分析,并建立决策树、支持向量机、逻辑回归和随机森林模型。进一步前瞻性纳入2019年1月到2020年12月入院的冠心病合并肠道恶性肿瘤患者作为验证组,通过对准确度、灵敏度、特异度和受试者工作特征曲线下面积(AUC)评估进行模型性能比较,并基于最优模型建立出血预测评分。采用SPSS 15.0和R 3.6.1软件进行数据分析。根据数据类型,组间比较分别采用t检验、χ^(2)检验或Wilcoxon检验。结果训练组患者511例,发生临床显著出血事件患者35例;验证组患者205例,发生临床显著出血事件患者11例。采用递归特征消除法对变量进行筛选,选取包含5个变量(脑利钠肽前体、总胆红素、天冬氨酸氨基转移酶、癌胚抗原、尿素)时的逻辑回归模型作为最优模型,训练组模型AUC值、准确度、灵敏度和特异度分别为0.791、0.757、0.714和0.800;验证组AUC值、准确度、灵敏度和特异度分别为0.748、0.747、0.500和0.760。我们基于该模型构建了出血预测评分以便于临床应用。结论出血风险预测模型及评分可用于预测老年冠心病合并肠道恶性肿瘤患者临床显著出血事件的发生。

Objective To establish an individualized bleeding risk assessment system for the elderly coronary heart disease(CHD)patients complicated with intestinal malignant tumor based on single-center clinical big data.Methods Clinical data of the elderly CHD patients with intestinal cancer and being treated in the Chinese PLA General Hospital during January 2008 and December 2018 were collected retrospectively from the Clinical Database in the Big Data Center of the hospital,and they were subjected as the validation cohort.Taking the occurrence of major as the research endpoints,baseline analysis,decision tree model,support vector machine,logistic regression model and random forest model were performed on the clinical data.And then the CHD patients with intestinal tumor admitted into the hospital from January 2019 to December 2020 were prospectively recruited and served as derivation cohort.Finally,the performance of above models were evaluated and verified based on the accuracy,sensitivity,specificity,and area under the receiver operating characteristic curve(AUC).A predictive system for bleeding risk was established on the obtained optimal model.SPSS statistics 15.0 and R 3.6.1 were used for statistical analysis.Data comparison between two groups was performed using student′s t test,Chi-square test or Wilcoxon test depending on different data types.Results There were 511 patients in the derivation cohort and 35 patients with clinically significant bleeding events;205 patients in the validation cohort and 11 patients with clinically significant bleeding events.Recursive feature elimination was used to screen the variables,and the logistic regression model containing 5 variables(brain natriuretic peptide precursor,total bilirubin,aspartate aminotransferase,carcinoembryonic antigen and urea)was selected as the optimal model.In the training set,the AUC value,accuracy,sensitivity,and specificity of the model were 0.791,0.757,0.714,and 0.800,respectively.In the verification set,the AUC value,accuracy,sensitivity,and specificity were 0.748,0.747,0.500 and 0.760,respectively.Based on this model,we constructed the bleeding prediction score for clinical application.Conclusion Our establised risk model and score system can predict bleeding events in the elderly CHD patients with intestinal malignant tumor.