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

Autor: Gadalla AAH; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom. GadallaA1@cardiff.ac.uk., Friberg IM; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Kift-Morgan A; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Zhang J; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Eberl M; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.; Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom., Topley N; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.; Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom., Weeks I; Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom.; Clinical Innovation Hub, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Cuff S; Division of Infection & Immunity, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.; Systems Immunity Research Institute, Cardiff University, Cardiff, United Kingdom.; Clinical Innovation Hub, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Wootton M; Specialist Antimicrobial Chemotherapy Unit, Public Health Wales Microbiology Cardiff, University Hospital of Wales, Cardiff, United Kingdom., Gal M; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Parekh G; Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, United Kingdom., Davis P; Mologic Ltd., Bedford Technology Park, Thurleigh, Bedford, United Kingdom., Gregory C; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Hood K; Centre for Trials Research, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Hughes K; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom., Butler C; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom., Francis NA; Division of Population Medicine, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.; Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, United Kingdom.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2019 Dec 23; Vol. 9 (1), pp. 19694. Date of Electronic Publication: 2019 Dec 23.
DOI: 10.1038/s41598-019-55523-x
Abstrakt: 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.
Databáze: MEDLINE
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