脓毒症伴急性肾损伤患者48h内有创通气风险的预测模型的建立与验证

Prediction of the risks of mechanical ventilation within 48 hours among sepsis patients with acute kidney injury

目的识别出48 h内有创通气风险大的脓毒症伴急性肾损伤患者,尽早进行干预,改善预后。方法收集2011年1月-2023年10月东阳市人民医院收治的脓毒症伴急性肾损伤患者的资料,分成建模及验证人群,在建模人群中分析与患者入院后48 h内需有创通气的独立危险因素,以此建立列线图预测模型。在建模人群和验证人群中用ROC曲线下面积(AUC)评估模型的判别能力,用GiViTI校准图评估模型的校准度,用决策曲线(DCA)评估预测模型的临床有效性。此外,用SOFA评分及NEWS评分建立的模型、多种机器学习(SVM、C5.0、XGBoos和集成方法)模型与诺模图模型进行比较(Delong检验)。结果共有773例患者纳入研究,住院期间出现呼吸衰竭的独立危险因素为乳酸、B型脑钠肽前体(pro-BNP)、D二聚体、经皮血氧饱和度和肺部感染。建模人群和验证人群中的AUC值分别为0.845和0.880,有良好的校准度和临床有效性。SOFA评分建立的模型在建模人群中AUC值为0.703,验证人群中为0.763,低于诺模图模型(P<0.05);NEWS评分建立的模型,在建模人群中AUC值为0.665,在验证人群中为0.718,均显著低于本研究的诺模图模型(P值分别<0.001和0.002)。在验证人群中建立的机器模型中,SVM的AUC值为0.780,C5.0的AUC值为0.835,XGBoost的AUC值为0.798,集成建模的AUC值为0.813,上述模型的区分能力均与诺模图模型相当(P>0.05)。结论基于乳酸、pro-BNP、D二聚体、经皮血氧饱和度和肺部感染的列线图模型可有效预测脓毒症伴急性肾损伤患者入院后48 h内有创通气的风险。

Objective Early identification of the sepsis patients accompanied by acute kidney injury(AKI)who were at high risk of invasive mechanical ventilation within 48 hours would help to improve the prognosis.Methods The clinical information was collected from the sepsis patients with AKI who were admitted at Dongyang People’s Hospital from January 2011 to Octber 2023.The enrolled cases were divided into training set and validation set.The independent risk factors related to invasive mechanical ventilation within 48 hours were obtained by univariable analysis and multivariable logistic regression analysis in the training set,and then a nomogram model was established.The model was evaluated for its discrimination power by the area under the receiver operating characteristic curve(AUC),calibration degree by GiViTI calibration graph and clinical benefi t by decision curve analysis both in the training set and validation set.Also,two models(based on SOFA score and NEWs score,respectively)in two patient sets and four models based on machine learning methods(SVM,C5.0,XGBoos and integration method)in validation set were established and compared to logistic model for AUCs by Delong test.Results A total of 773 cases were fi nally enrolled in this study.The risk factors for invasive mechanical ventilation within 48 hours were lactate,pro-bnp,D-dimer,saturation of peripheral oxygen and lung infection.The AUC of logistic nomogram model was 0.845 in the training set.In the validation set,the AUC was 0.880,also with good calibration degree and clinical benefi t.The AUCs of the models in the training and validation sets based on SOFA score were 0.703 and 0.763,respectively,signifi cantly lower than the logistic model(P less than 0.05).In addition,the AUCs of the models based on the NEWs score were signifi cantly lower both in the training set(AUC of 0.665,P<0.001)and validation set(AUC of 0.718,P=0.002)than the logistic model.Finally,the AUCs of the models were 0.780 for SVM method,0.835 for C5.0,0.798 for XGBoost and 0.813 for integration method,comparable to logistic model(P>0.05).Conclusions The logistic prediction model based on lactate,pro-bnp,D-dimer,saturation of peripheral oxygen and lung infection can effi ciently predict the risk for invasive mechanical ventilation within 48 hours among the sepsis patients with AKI.