Establishment of the Risk Prediction Model for Significant Bacteriuria in Adult Patients with Automated Urine Analysis.

INTRODUCTION

Urinary tract infections (UTIs) have been proven to be the most encountered bacterial infection in humans. We hope to establish a prediction model for significant bacteriuria by comprehensively analyzing the relevant parameters of age, gender, and urine automatic analysis data.

METHODS

A retrospective study was performed at Tai'an Central Hospital. All samples included in the study were tested for urine culture and urine automatic analysis. Data analysis was conducted with the SPSS.

RESULTS

The binary logistic regression module is used to establish the forecast formula, which gender, age, leukocyte count, bacterial count, leukocyte esterase, and nitrite were included. Receiver operating characteristic (ROC) curves showed that the area under ROC curve (AUC) of the prediction model was 0.878, bigger than the AUCs of the other 6 independent variables. The sensitivity and specificity of prediction model were 61.68 and 95.98%, respectively. The positive and the negative predictive values of the predictive model are 87.13 and 85.02%, respectively.

CONCLUSIONS

The prediction formula obtained in our study can achieve good prediction effect for significant bacteriuria, which can effectively avoid the treatment delay or antibiotic abuse caused by the subjective judgment of doctors.