Creation and assessment of a clinical predictive model for candidaemia in patients with candiduria.


Journal

Mycoses
ISSN: 1439-0507
Titre abrégé: Mycoses
Pays: Germany
ID NLM: 8805008

Informations de publication

Date de publication:
Jul 2019
Historique:
received: 27 02 2019
revised: 19 04 2019
accepted: 20 04 2019
pubmed: 27 4 2019
medline: 17 8 2019
entrez: 27 4 2019
Statut: ppublish

Résumé

Candidaemia is the most common clinical presentation of invasive candidiasis and is a major cause of morbidity and mortality. Candiduria is a predictor for candidaemia; however, patient characteristics that are associated with concurrent candidaemia in the setting of candiduria are unclear. Identifying these characteristics could aid in the early detection of systemic disease. We performed a retrospective cohort analysis of hospitalised patients with candiduria at our institution over a 13-year period. Our evaluation of patient characteristics included demographics, comorbidities, medications, procedures, devices, vital signs and laboratory values. We developed a multivariable logistic model to identify factors associated with candidaemia in patients with candiduria. We identified 4240 patients with candiduria, 263 (6.2%) of whom had candidaemia. Predictors for increased risk of candidaemia with candiduria included hospitalisations > 12 days, central venous catheter, parenteral nutrition, haematological and gynaecological malignancy, and receipt of β-lactam/β-lactamase inhibitors. Vital signs and laboratory values associated with candidaemia included elevated heart rate, temperature and creatinine, along with neutropenia and neutrophilia. Factors that demonstrated a decreased risk of candidaemia included diabetes mellitus, gastrostomy and urinary catheter with antibiotic use. The c-statistic was 0.741 (95% CI, 0.710-0.772). We identified a set of clinical characteristics that can predict the presence of candidaemia with candiduria.

Identifiants

pubmed: 31025417
doi: 10.1111/myc.12917
pmc: PMC6565491
mid: NIHMS1025895
doi:

Types de publication

Journal Article

Langues

eng

Pagination

554-561

Subventions

Organisme : NCI NIH HHS
ID : T32 CA190194
Pays : United States
Organisme : National Center for Advancing Translational Sciences (NCATS)
ID : UL1TR002345
Organisme : Astellas Pharma Global Development
ID : MYCA-15L03
Organisme : National Cancer Institute
Organisme : National Institute of Health (NIH)
ID : T32CA190194
Organisme : Washington University Institute of Clinical and Translational Sciences
Organisme : NCATS NIH HHS
ID : UL1 TR002345
Pays : United States

Informations de copyright

© 2019 Blackwell Verlag GmbH.

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Auteurs

Katie Wang (K)

Saint Louis University School of Medicine, Saint Louis, Missouri.

Kevin Hsueh (K)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.

Ryan Kronen (R)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.
Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts.

Charlotte Lin (C)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.

Ana S Salazar (AS)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.

William G Powderly (WG)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.

Andrej Spec (A)

Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, Saint Louis, Missouri.

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Classifications MeSH