细菌性血行性感染的脓毒症患者预后影响因素分析及预测模型建立

Analysis of prognostic risk factors and predictive prognostic modeling in septic patients with bacterial blood stream infections

目的对细菌性血行性感染脓毒症患者的预后危险因素进行分析,确定与死亡相关的独立危险因素,为临床提供有指导意义的预测模型。方法采用非干预回顾性研究,收集大连医科大学附属第一医院电子病案系统数据库中2018年1月1日—2019年12月31日成人细菌性血培养阳性(含中心静脉导管尖端培养阳性)的脓毒症患者入院48 h内的相关指标,包括人口学特征、生命体征、实验室检验数据等。根据患者在院结局划分为存活组和死亡组。通过多因素Logistic回归对危险因素进行分析建立预测模型,以受试者操作特征曲线下面积(area under the ROC curve,AUC)代表模型的判别能力。采用R语言绘制列线图以可视化预测模型,并对预测模型进行内部验证。结果共检索到1189例患者,最终符合条件并纳入研究的病例数为563例,其中存活组398例,死亡组165例。组间单因素比较性别及病原体种类差异无统计学意义,其他指标差异均有统计学意义。纳入Logistic预测模型的独立危险因素为:年龄[P=0.000,95%置信区间(confidential interval,CI)0.949~0.982],心率[P=0.000,95%CI 0.966~0.987],血小板计数(P=0.009,95%CI 1.001~1.006),纤维蛋白原(P=0.036,95%CI 1.010~1.325);血清钾离子(P=0.005,95%CI 0.426~0.861),血清氯离子(P=0.054,95%CI 0.939~1.001),谷草转氨酶(P=0.03,95%CI 0.996~1.000),血清球蛋白(P=0.025,95%CI 1.006~1.086),平均动脉压(P=0.250,95%CI 0.995~1.021)。预测模型AUC=0.779(95%CI 0.737~0.821)。该模型列线图总分得分在210~320区间预测效能良好,预测模型内部验证平均绝对误差为0.011,均方误差为0.00018。结论细菌性血行性感染脓毒症患者入院48 h内的基础生命体征指标、凝血功能、肝功能及肾功能导致的内环境紊乱与预后高度相关,在临床工作中应早期设定预警阈值,做到早期发现及时干预进而挽救患者的生命。

Objective To analyze the prognostic factors of patients with bacterial bloodstream infection sepsis and to identify independent risk factors related to death,so as to potentially develop one predictive model for clinical practice.Method A non-intervention retrospective study was carried out.The relative data of adult sepsis patients with positive bacterial blood culture(including central venous catheter tip culture)within 48 hours after admission were collected from the electronic medical database of the First Affiliated Hospital of Dalian Medical University from January 1,2018 to December 31,2019,including demographic characters,vital signs,laboratory data,etc.The patients were divided into a survival group and a death group according to in-hospital outcome.The risk factors were analyzed and the prediction model was established by means of multi-factor logistics regression.The discriminatory ability of the model was shown by area under the receiver operating characteristic curve(AUC).The visualization of the predictive model was drawn by nomogram and the model was also verified by internal validation methods with R language.Results A total of 1189 patients were retrieved,and 563 qualified patients were included in the study,including 398 in the survival group and 165 in the death group.Except gender and pathogen type,other indicators yielded statistical differences in single factor comparison between the survival group and the death group.Independent risk factors included in the logistic regression prediction model were:age[P=0.000,95%confidence interval(CI)0.949-0.982],heart rate(P=0.000,95%CI 0.966-0.987),platelet count(P=0.009,95%CI 1.001-1.006),fibrinogen(P=0.036,95%CI 1.010-1.325),serum potassium ion(P=0.005,95%CI 0.426-0.861),serum chloride ion(P=0.054,95%CI 0.939-1.001),aspartate aminotransferase(P=0.03,95%CI 0.996-1.000),serum globulin(P=0.025,95%CI 1.006-1.086),and mean arterial pressure(P=0.250,95%CI 0.995-1.021).The AUC of the prediction model was 0.779(95%CI 0.737-0.821).The prediction efficiency of the total score of the model's nomogram was good in the 210-320 interval,and mean absolute error was 0.011,mean squared error was 0.00018.Conclusions The basic vital signs within 48 h admitting into hospital,as well those homeostasis disordering index indicated by coagulation,liver and renal dysfunction are highly correlated with the prognosis of septic patients with bacterial bloodstream infection.Early warning should be set in order to achieve early detection and rescue patients’lives.