RESUMO
Objective:To study the effect of diabetes mellitus (DM) on sepsis in patients with pyogenic liver abscess (PLA).Methods:The clinical data of 116 patients with PLA treated in the Affiliated Hospital of Jiangnan University from January 2021 to May 2022 were retrospectively analyzed, including 64 males and 52 females, aged (62.3±12.6) years old. Patients were divided into DM group ( n=56) and non-DM group ( n=60), which were also divided into the sepsis group ( n=29) and the non-sepsis group ( n=87). The clinical features were compared among the groups, the risk factors of PLA complicated with sepsis were analyzed by multivariate logistic regression. Mediation model was used to analyze how DM affects the development of sepsis. Results:Compared with the non-DM group, patients in DM group had higher incidences of hypertension and acute physiology and chronic health evaluation II, a higher proportion of blood neutrophil count, a higher serum levels of triglyceride, urea nitrogen, fasting blood glucose and glycated hemoglobin at admission. The DM group also higher incidences of hypoproteinemia, pleural effusion, and sepsis, with longer hospital stay and higher hospitalization cost (all P<0.05). The levels of hemoglobin, albumin and hematocrit were lower in DM group (all P<0.05). Multivariate logistic regression analysis showed that comorbidity of DM ( OR=3.431, 95% CI: 1.245-9.455) and abscess with a larger diameter ( OR=1.664, 95% CI: 1.258-2.220) were associated with a higher risk of developing sepsis (all P<0.05). Mediation model showed that neutrophil count and triglyceride were the mediating variables of sepsis in patients with PLA. Conclusion:Comorbidity of diabetes is an independent risk factor of developing sepsis in patients with pyogenic liver abscess. Diabetes may induce sepsis by affecting the neutrophils and triglyceride.
RESUMO
Objective:To study the logistic regression model and Chi-square automatic interaction detection decision tree model in the prediction of the recurrence of acute pancreatitis (AP).Methods:Clinical data of 364 patients with AP admitted to the Affiliated Hospital of Jiangnan University from June 2021 to June 2022 were retrospectively analyzed, including 219 males and 145 females, aged 53 (19-91) years. The patients were divided into the recurrence group ( n=63), those who experienced a second or more episodes of AP, and the initial group ( n=301), those who were diagnosed of AP for the first time. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with recurrence of AP, and the decision tree model was used to analyze those factors. Receiver operating characteristic (ROC) curve were plotted to analyze the predictive performance of the two models. Results:Multivariate logistic regression analysis showed that age ( OR=0.969, 95% CI: 0.949-0.990, P=0.004), body mass index ( OR=1.142, 95% CI: 1.059-1.232, P=0.001), and hyperlipidemia ( OR=3.034, 95% CI: 1.543-5.964, P=0.001) were independent factors influencing the recurrence of AP. The accuracy of the model in predicting recurrence was 83.2% (303/364). The decision tree model showed that hyperlipidemia and body mass index were factors influencing the recurrence of AP, with an accuracy of 82.7% (301/364) in predicting recurrence. The area under the ROC curve was larger in the logistic regression model compared to that in the decision tree model (0.776 vs 0.730, Z=2.02, P=0.043). Conclusion:The logistic regression model and the Chi-square automatic interaction detection decision tree model can help predict the recurrence of AP. It is recommended to combine the two models to better guide clinical practice.