Use of Automated Urine Microscopy Analysis in Clinical Diagnosis of Urinary Tract Infection: Defining an Optimal Diagnostic Score in an Academic Medical Center Population

Abstract

ABSTRACT A retrospective case record study was conducted that established a scoring tool based on clinical and iQ200 parameters, able to predict or rule out the clinical diagnosis of UTI in the majority of adult patients in an academic hospital. Automated standardized quantitative urine analysis, such as iQ200 analysis, is on the rise because of its high accuracy and efficiency compared to those of traditional urine analysis. Previous research on automated urinalysis focused mainly on predicting culture results but not on the clinical diagnosis of urinary tract infection (UTI). A retrospective analysis was conducted of consecutive urine samples sent in for culture because of suspected UTI. UTI was defined by expert opinion, based on reported symptoms, conventional urine sediment analysis, and urine cultures. Parameters of iQ200 analysis and clinical symptoms and signs were compared between cases and controls. Optimal cutoff values were determined for iQ200 parameters, and multivariate logistic regression analysis was used to identify the set of variables that best predicts the clinical diagnosis of UTI for development of a scoring tool. A total of 382 patients were included. Optimal cutoff values of iQ200 analysis were 74 white blood cells (WBC)/μl, 6,250 “all small particles” (ASP)/μl, and a bacterial score of 2 on an ordinal scale of 0 to 5. The scoring tool attributed 1 point for frequent micturition or increased urge, 2 points for dysuria, 1 point for a bacterial score of ≥2, 2 points for WBC/μl of ≥50, and an additional point for WBC/μl of ≥150. This score had a sensitivity of 86% and a specificity of 92% when using a threshold of <4 points. The combination of iQ200 analysis and a simple survey could predict or rule out UTIs in a majority of patients in an academic medical center.