糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征预测模型的建立与验证

Establishment and verification of invasion syndrome prediction model in patients with diabetes complicated with Klebsiella pneumoniae liver abscess

目的分析糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征的相关因素,以此构建在线列线图预测模型并进行验证。方法病例对照研究。回顾性分析2015年1月1日至2021年12月31日在苏州大学附属第三医院治疗的213例糖尿病合并肺炎克雷伯菌肝脓肿患者的临床资料。运用分层随机抽样法按7∶3将患者分为训练集(149例)和测试集(64例)。使用人工少数类过采样法(SMOTE)过采样处理不平衡数据,再运用Lasso回归在训练集中筛选出最优特征变量并利用多因素logistic回归模型构建糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征的预测模型,并在训练集及测试集中进行验证。采用受试者工作特征(ROC)曲线、校准曲线及决策曲线分析(DCA)评估模型的预测效能,并构建简易列线图及在线交互动态网页列线图。结果入选的213例患者中,男60例,女153例,年龄(61.4±12.0)岁。共有25例(11.74%)糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征,入肺炎克雷伯菌肝脓肿侵袭综合征(IKPLAS)组,其他188例为未发生肺炎克雷伯菌肝脓肿侵袭综合征(NIKPLAS)组。使用SMOTE算法进行过采样处理,使正负样本比例为1∶1,在过采样后的训练集中基于Lasso回归筛选出5个主要危险因素,分别为空腹血糖(λ=0.063)、血红蛋白(λ=-0.042)、血尿素氮(λ=-0.050)、脓肿大小(λ=-0.025)和序贯性器官功能衰竭评分(SOFA)(λ=0.450)。多因素logistic回归模型结果显示,空腹血糖(OR=1.20,95%CI:0.98~1.48,P=0.006)、血红蛋白(OR=0.90,95%CI:0.86~0.95,P<0.001)、血尿素氮(OR=1.22,95%CI:1.03~1.43,P=0.017)、脓肿直径(OR=0.76,95%CI:0.61~0.94,P=0.010)、SOFA评分(OR=3.08,95%CI:2.18~4.36,P<0.001)为糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征的相关因素。训练集中ROC的曲线下面积为0.966(95%CI:0.943~0.989),灵敏度为90.5%,特异度为91.3%;验证集ROC的曲线下面积为0.946(95%CI:0.902~0.991),灵敏度为79.6%,特异度为88.9%。分别在训练集和测试集绘制的校准曲线与理想曲线拟合较好。DCA显示列线图预测模型在预测糖尿病合并肺炎克雷伯菌肝脓肿患者发生IKPLAS风险为0.10~0.40时,有较好的临床净收益。结论空腹血糖、血红蛋白、尿素氮、脓肿大小、SOFA评分是糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征的相关因素,基于此构建的列线图可有效预测糖尿病合并肺炎克雷伯菌肝脓肿患者发生侵袭综合征的风险。

Objective To analyze the correlative factors of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess,and to construct and verify the online nomographic prediction model.Methods A case control study.The clinical data of 213 diabetic patients with Klebsiella pneumoniae liver abscess admitted to the Third Affiliated Hospital of Soochow University from January 1,2015 to December 31,2021 were retrospectively analyzed.The patients were divided into the training set(149 cases)and the test set(64 cases)by stratified random sampling method at a ratio of 7∶3.Synthetic minority over-sampling technique(SMOTE)was used to process the imbalanced data,then Lasso regression was used to screen out the optimal feature variables in the training set and multivariate logistic regression model was used to construct the prediction model of invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess,and verify it in the training set and test set.Receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to evaluate the prediction efficiency of the model,and the simple and online interactive dynamic web page column graph was constructed.Results Among the 213 patients,60 were males and 153 were females,aged of(61.4±12.0)years.A total of 25(11.74%)diabetic patients with Klebsiella pneumoniae liver abscess developed invasion syndrome,which were included in divided into invasive K.pneumoniae liver abscesses syndrome(IKPLAS)group,and the other 188 cases were in without invasive K.pneumoniae liver abscesses syndrome(NIKPLAS)group.SMOTE algorithm was used for oversampling processing,so that the ratio of positive and negative samples was 1∶1.In the oversampling training set,5 main risk factors were screened based on Lasso regression,namely fasting blood glucose(λ=0.063),hemoglobin(λ=-0.042),blood urea nitrogen(λ=-0.050),abscess size(λ=-0.025)and sequential organ failure assessment(SOFA)score(λ=0.450),respectively.Multivariate logistic regression model showed that fasting blood glucose(OR=1.20,95%CI:0.98-1.48,P=0.006),hemoglobin(OR=0.90,95%CI:0.86-0.95,P<0.001),blood urea nitrogen(OR=1.22,95%CI:1.03-1.43,P=0.017),abscess diameter(OR=0.76,95%CI:0.61-0.94,P=0.010),SOFA score(OR=3.08,95%CI:2.18-4.36,P<0.001)were associated with invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess.The area under the curve of ROC in the training set was 0.966(95%CI:0.943-0.989),the sensitivity was 90.5%,and the specificity was 91.3%.The area under the curve of the validation set ROC was 0.946(95%CI:0.902-0.991),with a sensitivity of 79.6%and a specificity of 88.9%.The calibration curves drawn in the training set and the test set fit well with the ideal curve.DCA showed that the neomorph prediction model had a good clinical net benefit when predicting the risk of IKPLAS in patients with diabetes complicated with Klebsiella pneumoniae liver abscess was 0.10-0.40.Conclusions Fasting blood glucose,hemoglobin,urea nitrogen,abscess size and SOFA score are the related factors for invasion syndrome in patients with diabetes complicated with Klebsiella pneumoniae liver abscess.The constructed column graph can effectively predict the risk of invasion syndrome in patients with diabetes complicated with Klebsiae pneumoniae liver abscess.