A diagnostic algorithm for detection of urinary tract infections in hospitalized patients with bacteriuria: The "Triple F" approach supported by Procalcitonin and paired blood and urine cultures.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 01 05 2020
accepted: 06 10 2020
entrez: 22 10 2020
pubmed: 23 10 2020
medline: 15 12 2020
Statut: epublish

Résumé

For acute medicine physicians, distinguishing between asymptomatic bacteriuria (ABU) and clinically relevant urinary tract infections (UTI) is challenging, resulting in overtreatment of ABU and under-recognition of urinary-source bacteraemia without genitourinary symptoms (USB). We conducted a retrospective analysis of ED encounters in a university hospital between October 2013 and September 2018 who met the following inclusion criteria: Suspected UTI with simultaneous collection of paired urinary cultures and blood cultures (PUB) and determination of Procalcitonin (PCT). We sought to develop a simple algorithm based on clinical signs and PCT for the management of suspected UTI. Individual patient presentations were retrospectively evaluated by a clinical "triple F" algorithm (F1 ="fever", F2 ="failure", F3 ="focus") supported by PCT and PUB. We identified 183 ED patients meeting the inclusion criteria. We introduced the term UTI with systemic involvement (SUTI) with three degrees of diagnostic certainty: bacteremic UTI (24.0%; 44/183), probable SUTI (14.2%; 26/183) and possible SUTI (27.9%; 51/183). In bacteremic UTI, half of patients (54.5%; 24/44) presented without genitourinary symptoms. Discordant bacteraemia was diagnosed in 16 patients (24.6% of all bacteremic patients). An alternative focus was identified in 67 patients, five patients presented with S. aureus bacteremia. 62 patients were diagnosed with possible UTI (n = 20) or ABU (n = 42). Using the proposed "triple F" algorithm, dichotomised PCT of < 0.25 pg/ml had a negative predictive value of 88.7% and 96.2% for bacteraemia und accordant bacteraemia respectively. The application of the algorithm to our cohort could have resulted in 33.3% reduction of BCs. Using the diagnostic categories "possible" or "probable" SUTI as a trigger for initiation of antimicrobial treatment would have reduced or streamlined antimicrobial use in 30.6% and 58.5% of cases, respectively. In conclusion, the "3F" algorithm supported by PCT and PUB is a promising diagnostic and antimicrobial stewardship tool.

Identifiants

pubmed: 33091046
doi: 10.1371/journal.pone.0240981
pii: PONE-D-20-12764
pmc: PMC7580978
doi:

Substances chimiques

Procalcitonin 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0240981

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

Kathrin Rothe (K)

Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany.

Christoph D Spinner (CD)

Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.

Birgit Waschulzik (B)

Institute of Medical Informatics, Statistics and Epidemiology, School of Medicine, Technical University of Munich, Munich, Germany.

Christian Janke (C)

Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany.

Jochen Schneider (J)

Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.

Heike Schneider (H)

Department of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany.

Krischan Braitsch (K)

Department of Internal Medicine III, School of Medicine, Technical University of Munich, Munich, Germany.

Christopher Smith (C)

School of Tropical Medicine and Global Health (TMGH), Nagasaki University, Nagasaki, Japan.
Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Roland M Schmid (RM)

Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.

Dirk H Busch (DH)

Institute for Medical Microbiology, Immunology and Hygiene, School of Medicine, Technical University of Munich, Munich, Germany.
German Centre for Infection Research (DZIF), partner site Munich, Munich, Germany.

Juri Katchanov (J)

Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany.

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Classifications MeSH