基于常规检验数据挖掘构建脓毒性休克患者28 d内死亡列线图预测模型  

Establishment of a nomogram prediction model for 28-day mortality of septic shock patients based on routine laboratory data mining

作　　者：郭启芬 丁涛 曾冉 邵敏[2] Guo Qifen;Ding Tao;Zeng Ran;Shao Min(Department of Critical Care Medicine,Anhui Medical University Affiliated Fuyang Hospital,Fuyang 236000,Anhui,China;Department of Intensive Care Medicine,the First Affiliated Hospital of Anhui Medical University,Hefei 230022,Anhui,China)

机构地区：[1]安徽医科大学附属阜阳医院重症医学科,安徽阜阳236000 [2]安徽医科大学第一附属医院重症医学科,安徽合肥230022

出　　处：《中华危重病急救医学》2024年第11期1127-1132,共6页Chinese Critical Care Medicine

基　　金：安徽省医学会多中心研究项目(2023620);安徽省转化医学研究院科研基金项目(2022zhyx-C26)。

摘　　要：目的基于常规检验数据挖掘构建脓毒性休克患者28 d内死亡列线图预测模型并验证其预测价值。方法回顾性分析2018年1月至2023年11月安徽医科大学附属阜阳医院收治的脓毒性休克患者的临床资料,按照8∶2比例随机分为训练集和验证集。收集患者性别、年龄、体质量指数、基础疾病、吸烟史、饮酒史、感染部位,入住重症监护病房(ICU)首日急性生理学与慢性健康状况评分Ⅱ(APACHEⅡ)、序贯器官衰竭评分(SOFA)、呼吸频率、心率、平均动脉压、血乳酸、降钙素原、C-反应蛋白、白细胞计数、血小板计数、血清丙氨酸转氨酶、天冬氨酸转氨酶、尿素氮、血肌酐、纤维蛋白原、D-二聚体、白蛋白,以及机械通气时间、ICU住院时间等。根据28 d预后将脓毒性休克患者分为生存者和死亡组。分析影响脓毒性休克患者28 d内死亡的因素,挖掘常规检验数据建立预测脓毒性休克患者28 d内死亡风险的列线图模型,并用Bootstrap法、校准曲线、受试者工作特征曲线(ROC曲线)对模型进行验证及效能评估。结果最终纳入128例脓毒性休克患者,其中训练集103例患者28 d内死亡32例(31.07%),验证集25例患者28 d内死亡8例(32.00%)。Logistic回归分析显示,APACHEⅡ〔优势比(OR)=5.254,95%可信区间(95%CI)为2.161~12.769〕、SOFA评分(OR=4.909,95%CI为2.020~11.930)、血乳酸(OR=4.419,95%CI为1.818~10.741)、降钙素原(OR=4.358,95%CI为1.793~10.591)是脓毒性休克患者28 d内死亡的影响因素(均P<0.01)。以上述影响因素作为预测变量,建立列线图模型,总分89~374分,对应死亡风险0.07~0.89。列线图模型验证结果显示,C-index为0.801(95%CI为0.759~0.832);预测脓毒性休克患者28 d内死亡的校正曲线趋近于理想曲线,Hosmer-Lemeshow检验χ2=0.263,P=0.512。训练集ROC曲线结果显示,列线图模型预测脓毒性休克患者28 d内死亡的敏感度为78.13%(95%CI为59.57%~90.06%),特异度为80.28%(9Objective To construct a nomogram prediction model for 28-day mortality in septic shock patients based on routine laboratory data mining and verify its predictive value.Methods The clinical data of patients with septic shock admitted to Anhui Medical University Affiliated Fuyang Hospital from January 2018 to November 2023 were retrospectively analyzed.The patients were randomly divided into training set and validation set according to the ratio of 8∶2.The patient's gender,age,body mass index,underlying disease,smoking history,alcohol history,infection site,acute physiology and chronic health evaluationⅡ(APACHEⅡ),sequential organ failure assessment(SOFA),respiratory rate,heart rate,mean arterial pressure,blood lactate,procalcitonin,C-reactive protein,white blood cell count,platelet count,serum alanine aminotransferase,aspartate aminotransferase,urea nitrogen,serum creatinine,fibrinogen,D-dimer,albumin on the first day of admission to the intensive care unit(ICU),duration of mechanical ventilation,and length of ICU stay were collected.The patients were divided into survival and death groups based on their 28-day prognosis.The factors influencing 28-day mortality were analyzed,and routine laboratory data were used to develop a nomogram model for predicting the risk of 28-day mortality in septic shock patients.The model was validated and assessed using the Bootstrap method,calibration curve,and receiver operator characteristic curve(ROC curve).Results Finally,128 patients with septic shock were enrolled,and 32(31.07%)death within 28-day of 103 patients in the training set,8(32.00%)death within 28-day of 25 patients in the validation set.Logistic regression analysis showed that APACHEⅡscore[odds ratio(OR)=5.254,95%confidence interval(95%CI)was 2.161-12.769],SOFA score(OR=4.909,95%CI was 2.020-11.930),blood lactate(OR=4.419,95%CI was 1.818-10.741),procalcitonin(OR=4.358,95%CI was 1.793-10.591)were significant factors influencing 28-day mortality in septic shock patients(all P<0.01).Taking the above influencing

关 键 词：数据挖掘 脓毒性休克 死亡 列线图 预测 

分 类 号：R459.7[医药卫生—急诊医学]