Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms
| العنوان: | Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms |
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| المؤلفون: | Jingjing Zhang, Mandy Wootton, Nicholas Topley, Clive James Gregory, Simone Cuff, Micaela Gal, Paul A. Davis, Kathryn Hughes, Ian Weeks, Nick A Francis, Amal A. H. Gadalla, Ann Kift-Morgan, Christopher C Butler, Kerenza Hood, Matthias Eberl, Ida M. Friberg, Gita Parekh |
| المصدر: | Scientific Reports, Vol 9, Iss 1, Pp 1-11 (2019) Scientific Reports |
| بيانات النشر: | Nature Publishing Group, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Adult, 0301 basic medicine, medicine.medical_specialty, Support Vector Machine, Microbiological culture, Adolescent, medicine.drug_class, Point-of-care testing, Urinary system, Interleukin-1beta, Antibiotics, lcsh:Medicine, Urine, Article, Machine Learning, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Antibiotic resistance, Urinary levels, Lipocalin-2, Nephelometry and Turbidimetry, Internal medicine, Humans, Immunologic Factors, Medicine, Diagnosis, Computer-Assisted, 030212 general & internal medicine, lcsh:Science, Aged, Aged, 80 and over, Likelihood Functions, Multidisciplinary, business.industry, Interleukin-8, lcsh:R, Diagnostic markers, Middle Aged, 030104 developmental biology, Matrix Metalloproteinase 9, Urine biomarkers, Point-of-Care Testing, Urinary Tract Infections, Female, lcsh:Q, Bacterial infection, business, Algorithms, Biomarkers |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2045-2322 |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd58ed255bcd4200eaf33b2141fa884cTest http://link.springer.com/article/10.1038/s41598-019-55523-xTest |
| حقوق: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....bd58ed255bcd4200eaf33b2141fa884c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 20452322 |
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