Abstract：Objective To establishment and validation of urinary tract infection(UTI)score prediction model caused by gram-positive(G+)or gram-negative bacteria(G-),and verify the prediction efficiency of the scoring model.Methods A total of 182 patients with UTI treated in Yangjiang Hospital of Traditional Chinese Medicine,Guangdong Province from January 2019 to May 2021 were retrospectively analyzed.According to random number table method,they were divided into modeling group(91 cases,59 cases of G- bacteria,G+ bacteria 32 cases)and verification group(91 cases,59 cases of G-bacteria,G+bacteria 32 cases).The risk prediction model of G+bacteria infection was constructed by binary logistic regression analysis to screen the relevant influencing factors,and the prediction ability of the model was judged.Results A total of 225 strains were detected,including 138 G- strains,75 G+ strains and 12 fungi.The AUC of G- bacterial infection predicted by bacterial count in the modeling group was 0.793,and the best cut-off value was 320/μl.The AUC of G-bacterial infection predicted by white blood cell(WBC)count was 0.699,and the best cut-off value was 64/μl.Binary logistic regression analysis showed that nitrite(NIT)negative,urinary sediment stained G+bacteria,bacterial count ＜320.00/μl and WBC count ＜64/μl were the influencing factors of G+ bacteria in UTI patients,and 11,4,5 and 4 points were assigned respectively.The AUC of model subjects in the modeling group was 0.874 and that in the validation group was 0.877.The risk of G+bacteria infection was stratified,0-11 points was divided into low G+bacteria risk,13-19 points was medium G+ bacteria risk,and 20-24 points was high G+ bacteria risk.Conclusion The score prediction model has good discrimination and calibration,which is convenient for the prediction of pathogens and clinical medication according to the model.
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