Analysis of the relevant factors and establishment of a predictive model for urinary system infection in pa-tients with acute cerebral infarction
Objective To explore the risk factors for urinary system infections in patients with a-cute cerebral infarction,establish an infection prediction model,and provide reference for formulating prevention and control measures.Methods A retrospective analysis was conducted on the clinical data of 223 patients with acute cerebral infarction in our hospital from January 2021 to December 2022.A-mong them,48 patients with urinary system infections were included in the infection group,and 175 patients without urinary system infections were included in the non infection group.Logistic regres-sion analysis was used to screen for risk factors for urinary system infections in patients with cerebral infarction and a predictive model was constructed.The discriminant validity and cutoff value of the model were evaluated using receiver operating curve(ROC).Results Logistic regression analysis showed that female,age≥60 years old,history of diabetes,urinary calculi and indwelling catheter were independent risk factors for urinary system infection in patients with cerebral infarction;Accord-ing to the results of multi factor analysis,the regression equation of the probability value of urinary system infection in patients with cerebral infarction was constructed as:P=1/[1+e-(-3.883+1.001*gender+0.880*age+1.136*diabetes+1.018*urinary calculus+1.957*indwelling catheter)].The modeling team conducted internal validation on the model,with ROC area under the curve(AUC)of 0.823,95％CI(0.762,0.884),sensitivity of 87.50％,specificity of 64.00％,and good discrimination.According to the principle of maximum Jordan index,a cut-off point of 0.120 was selected.Conclusion The estab-lished predictive model has good discriminant validity and can be used to identify high-risk patients with cerebral infarction and urinary system infections.
Cerebral infarctionUrinary system infectionRisk factorsPrediction model
NETL
NSTL
万方数据
维普
