Dataset Information

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations.

ABSTRACT:

Background

Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use.

Methods

Species identification and colony-forming unit (CFU/ml) quantification from bacterial cultures were matched to corresponding cellular (leucocytes/epithelial cells) and bacteria counts per ?l. Results comprise samples analysed between 2013 and 2015 for which urine culture (reference standard) and UFC and urine test strip data (index tests, Sysmex UX-2000) were available.

Results

47,572 urine samples of 26,256 patients were analysed. Bacteria counts used to predict bacterial growth of ?10⁵?CFU/ml showed an accuracy with an area under the receiver operating characteristic curve of >?93% compared to 82% using leukocyte counts. The relevant bacteriuria rule-out cut-off of 50 bacteria/?l reached a negative predictive value of 98, 91 and 89% and the rule-in cut-off of 250 bacteria/?l identified relevant bacteriuria with an overall positive predictive value of 67, 72 and 73% for microbiologically defined bacteriuria thresholds of 10⁵, 10⁴ or 10³?CFU/ml, respectively. Measured epithelial cell counts by UFC could not identify contaminated urine.

Conclusions

Prediction of a relevant bacterial growth by bacteria counts was most accurate and was a better predictor than leucocyte counts independently of the source of the urine and the medical specialty ordering the test (medical, surgical or others).

SUBMITTER: Gehringer C

PROVIDER: S-EPMC7908726 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations.

Gehringer Christian C Regeniter Axel A Rentsch Katharina K Tschudin-Sutter Sarah S Bassetti Stefano S Egli Adrian A

BMC infectious diseases 20210225 1

<h4>Background</h4>Urinary tract infection (UTI) is diagnosed combining urinary symptoms with demonstration of urine culture growth above a given threshold. Our aim was to compare the diagnostic accuracy of Urine Flow Cytometry (UFC) with urine test strip in predicting bacterial growth and in identifying contaminated urine samples, and to derive an algorithm to identify relevant bacterial growth for clinical use.<h4>Methods</h4>Species identification and colony-forming unit (CFU/ml) quantificati ...[more]

PMID: 33632129

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Project description:Mass cytometry by time-of-flight (CyTOF) is a valuable technology for high-dimensional analysis at the single cell level. Identification of different cell populations is an important task during the data analysis. Many clustering tools can perform this task, which is essential to identify "new" cell populations in explorative experiments. However, relying on clustering is laborious since it often involves manual annotation, which significantly limits the reproducibility of identifying cell-populations across different samples. The latter is particularly important in studies comparing different conditions, for example in cohort studies. Learning cell populations from an annotated set of cells solves these problems. However, currently available methods for automatic cell population identification are either complex, dependent on prior biological knowledge about the populations during the learning process, or can only identify canonical cell populations. We propose to use a linear discriminant analysis (LDA) classifier to automatically identify cell populations in CyTOF data. LDA outperforms two state-of-the-art algorithms on four benchmark datasets. Compared to more complex classifiers, LDA has substantial advantages with respect to the interpretable performance, reproducibility, and scalability to larger datasets with deeper annotations. We apply LDA to a dataset of ~3.5 million cells representing 57 cell populations in the Human Mucosal Immune System. LDA has high performance on abundant cell populations as well as the majority of rare cell populations, and provides accurate estimates of cell population frequencies. Further incorporating a rejection option, based on the estimated posterior probabilities, allows LDA to identify previously unknown (new) cell populations that were not encountered during training. Altogether, reproducible prediction of cell population compositions using LDA opens up possibilities to analyze large cohort studies based on CyTOF data. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Dataset Information

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations.

Background

Methods

Results

Conclusions

Publications

Accuracy of urine flow cytometry and urine test strip in predicting relevant bacteriuria in different patient populations.

Similar Datasets

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