Abstract:
Objective To investigate the influencing factors of prognosis of patients with diabetic ketoacidosis(DKA) combined with severe acute pancreatitis(SAP) and to construct a nomogram prediction model.
Methods A total of 136 patients with DKA combined with SAP were selected as the research subjects by convenience sampling method.According to the prognosis of patients, the patients were divided into the survival group and death group.The clinical data of two groups were collected for univariate analysis and binary logistic regression analysis to construct a nomogram prediction model, and the predictive performance and clinical utility of model were verified.
Results Among the 36 patients with DKA combined with SAP, 66 died, with a mortality rate of 48.53%.The results of univariate analysis showed that the gender, septic shock, circulatory failure, acute physiology and chronic health (APACHE Ⅱ) score, sequential organ failure assessment(SOFA) score, C reactive protein(CRP), white blood cell count(WBC), triglyceride(TG), blood urea nitrogen (BUN), and Ca2+ were the nfluencing factors of the death of patients with DKA combined with SAP.The results of binary logistic regression analysis showed that septic shock, APACHE Ⅱ score, CRP, TG, and Ca2+ were the independent risk factors of prognosis and death in patients with DKA combined with SAP.A nomogram prediction model was constructed.When the total score was above 104 points, the risk of patient death was 90%.The model validation results showed that the AUC for predicting prognostic death in the training set was 0.991(95%CI: 0.979-1.000), and that in the test set was 0.961 (95%CI: 0.883-1.000).Hosmer-Lemeshow test found χ2=1.10, P=1.00, the MAE of the training set and test set were 0.021 and 0.171, respectively, and the MSE of the training set and test set were 0.001 and 0.035, respectively.Both the decision curve analysis and clinical impact curve analysis proved that this prediction model had good clinical practicability.
Conclusions DKA combined with SAP patients has a higher risk of mortality.By contructing a column chart prediction model to enhance patient prognosis evaluation, it can provide reference for the formulation and optimization of patient treatment plants.