机器学习对创伤合并急性呼吸窘迫综合征患者院内死亡的预测价值--一项基于MIMICⅢ数据库的资料分析

Predictive value of machine learning for in-hospital mortality for trauma-induced acute respiratory distress syndrome patients: an analysis using the data from MIMICⅢ

目的:探讨机器学习方法对创伤合并急性呼吸窘迫综合征(ARDS)患者院内死亡的预测价值。方法:采用回顾性非干预性病例对照研究。从于美国重症监护医学信息数据库Ⅲ(MIMICⅢ)数据库中提取符合柏林ARDS标准定义的创伤合并ARDS患者,收集患者的基本信息〔包括性别、年龄、体质量指数(BMI)、pH值、氧合指数、实验室指标、重症监护病房(ICU)住院时间、行机械通气(MV)或连续性肾脏替代治疗(CRRT)比例、急性生理学评分Ⅲ(APSⅢ)、序贯器官衰竭评分(SOFA)和简化急性生理学评分Ⅱ(SAPSⅡ)〕、并发症和合并症〔包括高血压、糖尿病、感染、急性失血性贫血、脓毒症、休克、酸中毒和肺炎〕以及预后情况。采用多因素Logistic回归分析筛选有意义的变量(P<0.05),构建Logistic回归模型、XGBoost模型和人工神经网络模型,并绘制受试者工作特征曲线(ROC曲线)评估3个模型对创伤合并ARDS患者院内死亡的预测价值。结果:共纳入760例创伤合并ARDS患者,其中轻度346例,中度301例,重度113例;院内存活618例,院内死亡142例;736例接受MV,65例接受CRRT。多因素Logistic回归分析筛选出有意义的变量,包括年龄〔优势比(OR)=1.035,95%可信区间(95%CI)为1.020~1.050,P<0.001〕、BMI(OR=0.949,95%CI为0.917~0.983,P=0.003)、血尿素氮(BUN;OR=1.019,95%CI为1.004~1.033,P=0.010)、血乳酸(Lac;OR=1.213,95%CI为1.124~1.309,P<0.001)、红细胞分布宽度(RDW;OR=1.249,95%CI为1.102~1.416,P<0.001)、血细胞比容(HCT;OR=1.057,95%CI为1.019~1.097,P=0.003)、高血压(OR=0.614,95%CI为0.389~0.968,P=0.036)、感染(OR=0.463,95%CI为0.289~0.741,P=0.001)、急性肾衰竭(OR=2.021,95%CI为1.267~3.224,P=0.003)、脓毒症(OR=2.105,95%CI为1.265~3.502,P=0.004),使用上述变量构建模型。Logistic回归模型、XGBoost模型和人工神经网络模型预测创伤合并ARDS患者院内死亡的ROC曲线下面积(AUC)分别为0.737(95%CI为0.659~0.815)、0.745(95%CI为0.672~0.819)和0.757(95%CI为0.680~0.884),任意两个模型之间比较差异均无统计学意义(均P>0.05)。结论:纳入年龄、BMI、BUN、Lac、RDW、HCT、高血压、感染、急性肾衰竭、脓毒症变量的Logistic回归模型、XGBoost模型和人工神经网络模型对创伤合并ARDS患者院内死亡有良好的预测价值。

Objective To investigate the value of machine learning methods for predicting in-hospital mortality in trauma patients with acute respiratory distress syndrome(ARDS).Methods A retrospective non-intervention case-control study was performed.Trauma patients with ARDS met the Berlin definition were extracted from the the Medical Information Mart for Intensive CareⅢ(MIMICⅢ)database.The basic information[including gender,age,body mass index(BMI),pH,oxygenation index,laboratory indexes,length of stay in the intensive care unit(ICU),the proportion of mechanical ventilation(MV)or continuous renal replacement therapy(CRRT),acute physiology scoreⅢ(APSⅢ),sequential organ failure score(SOFA)and simplified acute physiology scoreⅡ(SAPSⅡ)],complications(including hypertension,diabetes,infection,acute hemorrhagic anemia,sepsis,shock,acidosis and pneumonia)and prognosis data of patients were collected.Multivariate Logistic regression analysis was used to screen meaningful variables(P<0.05).Logistic regression model,XGBoost model and artificial neural network model were constructed,and the receiver operator characteristic curve(ROC)was performed to evaluate the predictive value of the three models for in-hospital mortality in trauma patients with ARDS.Results A total of 760 trauma patients with ARDS were enrolled,including 346 mild cases,301 moderate cases and 113 severe cases;618 cases survived and 142 cases died in hospital;736 cases received MV and 65 cases received CRRT.Multivariate Logistic regression analysis screened out significant variables,including age[odds ratio(OR)=1.035,95%confidence interval(95%CI)was 1.020-1.050,P<0.001],BMI(OR=0.949,95%CI was 0.917-0.983,P=0.003),blood urea nitrogen(BUN;OR=1.019,95%CI was 1.004-1.033,P=0.010),lactic acid(Lac;OR=1.213,95%CI was 1.124-1.309,P<0.001),red cell volume distribution width(RDW;OR=1.249,95%CI was 1.102-1.416,P<0.001),hematocrit(HCT,OR=1.057,95%CI was 1.019-1.097,P=0.003),hypertension(OR=0.614,95%CI was 0.389-0.968,P=0.036),infection(OR=0.463,95%CI was 0.289-0.741,P=0.001),acute renal failure(OR=2.021,95%CI was 1.267-3.224,P=0.003)and sepsis(OR=2.105,95%CI was 1.265-3.502,P=0.004).All the above variables were used to construct the model.Logistic regression model,XGBoost model and artificial neural network model predicted in-hospital mortality with area under the curve(AUC)of 0.737(95%CI was 0.659-0.815),0.745(95%CI was 0.672-0.819)and 0.757(95%CI was 0.680-0.884),respectively.There was no significant difference between any two models(all P>0.05).Conclusion Logistic regression model,XGBoost model and artificial neural network model including age,BMI,BUN,Lac,RDW,HCT,hypertension,infection,acute renal failure and sepsis have good predictive value for in-hospital mortality of trauma patients with ARDS.