Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms

Amal A H Gadalla,Clive Gregory,Mandy Wootton,Nick A Francis,Christopher Butler,Jingjing Zhang,Paul Davis,Kathryn Hughes,Matthias Eberl,Nicholas Topley,Ian Weeks,Ann Kift-Morgan,Micaela Gal,Gita Parekh,Simone Cuff,Ida M Friberg,Kerenza Hood

doi:10.1038/s41598-019-55523-x

Abstract

Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance. Available diagnostic tools are either not cost-effective or diagnostically sub-optimal. Here, we identified clinical and urinary immunological predictors for UTI diagnosis. We explored 17 clinical and 42 immunological potential predictors for bacterial culture among women with uncomplicated UTI symptoms using random forest or support vector machine coupled with recursive feature elimination. Urine cloudiness was the best performing clinical predictor to rule out (negative likelihood ratio [LR−] = 0.4) and rule in (LR+ = 2.6) UTI. Using a more discriminatory scale to assess cloudiness (turbidity) increased the accuracy of UTI prediction further (LR+ = 4.4). Urinary levels of MMP9, NGAL, CXCL8 and IL-1β together had a higher LR+ (6.1) and similar LR− (0.4), compared to cloudiness. Varying the bacterial count thresholds for urine culture positivity did not alter best clinical predictor selection, but did affect the number of immunological predictors required for reaching an optimal prediction. We conclude that urine cloudiness is particularly helpful in ruling out negative UTI cases. The identified urinary biomarkers could be used to develop a point of care test for UTI but require further validation.

Highlights

Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance
They ranged in age from 18 to 85 years, and the key UTI symptoms of urgency, frequency and dysuria were present in 84.2%, 91.8% and 77.0% of patients, respectively
We found that cloudiness of urine samples was the best clinical predictor of microbiologically confirmed UTI among symptomatic women, and that assessing cloudiness using a categorical turbidity scale improved the predictive properties further, in identifying positive UTI

Summary

Introduction

Women with uncomplicated urinary tract infection (UTI) symptoms are commonly treated with empirical antibiotics, resulting in overuse of antibiotics, which promotes antimicrobial resistance. In this study we aimed to use a machine learning-based approach, in which random forest (RF) and support vector machines (SVM) were implemented to allow fewer assumptions and more complex relationships between predictors. We combined these algorithms with recursive feature elimination (RFE) to extract the best predictor(s) for uncomplicated UTI using clinical information and potential biomarkers present in urine. These analytical approaches have been widely used in medical applications, such as drug discovery, biomarker selection and early diagnosis[9,10,11,12,13,14,15,16]. For SVMs, the separate hyperplane relies on the support vectors not all data, giving it independent advantages in dealing with high-dimensional data

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Conclusion