Abstract
Patients with stroke have a high risk of infection which may be predicted by age, procalcitonin, interleukin-6, C-reactive protein, National Institute of Health stroke scale (NHSS) score, diabetes, etc. These prediction methods can reduce unfavourable outcome by preventing the occurrence of infection.We aim to identify early predictors for urinary tract infection in patients after stroke.In 186 collected acute stroke patients, we divided them into urinary tract infection group, other infection type groups, and non-infected group. Data were recorded at admission. Independent risk factors and infection prediction model were determined using Logistic regression analyses. Likelihood ratio test was used to detect the prediction effect of the model. Receiver operating characteristic curve and the corresponding area under the curve were used to measure the predictive accuracy of indicators for urinary tract infection.Of the 186 subjects, there were 35 cases of urinary tract infection. Elevated interleukin-6, higher NIHSS, and decreased hemoglobin may be used to predict urinary tract infection. And the predictive model for urinary tract infection (including sex, NIHSS, interleukin-6, and hemoglobin) have the best predictive effect.This study is the first to discover that decreased hemoglobin at admission may predict urinary tract infection. The prediction model shows the best accuracy.