Prediction of the risks of mechanical ventilation within 48 hours among sepsis patients with acute kidney injury
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 benefit 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 finally 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 benefit.The AUCs of the models in the training and validation sets based on SOFA score were 0.703 and 0.763,respectively,significantly lower than the logistic model(P less than 0.05).In addition,the AUCs of the models based on the NEWs score were significantly 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 efficiently predict the risk for invasive mechanical ventilation within 48 hours among the sepsis patients with AKI.
SepsisAcute kidney injuryMechanical ventilationPrediction modelMachine learning model
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