A new method for predicting SIRS after percutaneous transhepatic gallbladder drainage

Abstract

The occurrence of systemic inflammatory response after percutaneous transhepatic gallbladder drainage brings great risks to patients and is one of the challenges faced by clinicians. Therefore, it is of great significance to find a suitable prediction method for clinicians to intervene early and reduce the transformation of serious complications. Easy-to-obtain and objectively measured clinical features were screened, and logistic regression was used to construct a prediction model. The predictive ability of the model was evaluated by using the receiver operating characteristic curve and the decision curve in the validation set and the training set, respectively. Nine clinical features (CRP, Fever, DBIL, Obstruction, Bile properties, PCT, Length, Width, and Volume factor) were used to construct the prediction model, and the validation results showed that the prediction model had good performance in the training set (AUC = 0.83) and the validation set (AUC = 0.81). The decision curve also showed that the predictive ability of the model incorporating nine clinical features is better than that of a single clinical feature. The model we constructed can accurately predict the occurrence of SIRS, which can guide clinicians to take treatment measures and avoid the deterioration of complications.