Urine Flow Cytometry Parameter Cannot Safely Predict Contamination of Urine-A Cohort Study of a Swiss Emergency Department Using Machine Learning Techniques.

Müller, Martin; Sägesser, Nadine; Keller, Peter M; Arampatzis, Spyridon; Steffens, Benedict; Ehrhard, Simone; Leichtle, Alexander B (2022). Urine Flow Cytometry Parameter Cannot Safely Predict Contamination of Urine-A Cohort Study of a Swiss Emergency Department Using Machine Learning Techniques. Diagnostics, 12(4) MDPI 10.3390/diagnostics12041008

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BACKGROUND

Urine flow cytometry (UFC) analyses urine samples and determines parameter counts. We aimed to predict different types of urine culture growth, including mixed growth indicating urine culture contamination.

METHODS

A retrospective cohort study (07/2017-09/2020) was performed on pairs of urine samples and urine cultures obtained from adult emergency department patients. The dataset was split into a training (75%) and validation set (25%). Statistical analysis was performed using a machine learning approach with extreme gradient boosting to predict urine culture growth types (i.e., negative, positive, and mixed) using UFC parameters obtained by UF-4000, sex, and age.

RESULTS

In total, 3835 urine samples were included. Detection of squamous epithelial cells, bacteria, and leukocytes by UFC were associated with the different types of culture growth. We achieved a prediction accuracy of 80% in the three-class approach. Of the n = 126 mixed cultures in the validation set, 11.1% were correctly predicted; positive and negative cultures were correctly predicted in 74.0% and 96.3%.

CONCLUSIONS

Significant bacterial growth can be safely ruled out using UFC parameters. However, positive urine culture growth (rule in) or even mixed culture growth (suggesting contamination) cannot be adequately predicted using UFC parameters alone. Squamous epithelial cells are associated with mixed culture growth.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry
04 Faculty of Medicine > Department of Intensive Care, Emergency Medicine and Anaesthesiology (DINA) > University Emergency Center
04 Faculty of Medicine > Service Sector > Institute for Infectious Diseases > Clinical Microbiology
04 Faculty of Medicine > Service Sector > Institute for Infectious Diseases > General Bacteriology

UniBE Contributor:

Müller, Martin (B), Keller, Peter Michael, Ehrhard, Simone, Leichtle, Alexander Benedikt (B)

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2075-4418

Publisher:

MDPI

Language:

English

Submitter:

Pubmed Import

Date Deposited:

26 Apr 2022 13:58

Last Modified:

29 Mar 2023 23:38

Publisher DOI:

10.3390/diagnostics12041008

PubMed ID:

35454055

Uncontrolled Keywords:

UF-4000 automated urine sediment analyser culture growth flow cytometry mixed urine culture prediction squamous epithelial cell urinary tract infection urine analysis

BORIS DOI:

10.48350/169492

URI:

https://boris.unibe.ch/id/eprint/169492

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