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Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA

Authors
  • Ramadan, Ferris A.
  • Ellingson, Katherine D.
  • Canales, Robert A.
  • Bedrick, Edward J.
  • Galgiani, John N.
  • Donovan, Fariba M.
Type
Published Article
Journal
Emerging Infectious Diseases
Publisher
Centers For Disease Control and Prevention
Publication Date
Jun 01, 2022
Volume
28
Issue
6
Pages
1091–1100
Identifiers
DOI: 10.3201/eid2806.212311
PMID: 35608552
PMCID: PMC9155888
Source
PubMed Central
Keywords
Disciplines
  • Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA
License
Unknown

Abstract

Demographic and clinical indicators have been described to support identification of coccidioidomycosis; however, the interplay of these conditions has not been explored in a clinical setting. In 2019, we enrolled 392 participants in a cross-sectional study for suspected coccidioidomycosis in emergency departments and inpatient units in Coccidioides -endemic regions. We aimed to develop a predictive model among participants with suspected coccidioidomycosis. We applied a least absolute shrinkage and selection operator to specific coccidioidomycosis predictors and developed univariable and multivariable logistic regression models. Univariable models identified elevated eosinophil count as a statistically significant predictive feature of coccidioidomycosis in both inpatient and outpatient settings. Our multivariable outpatient model also identified rash (adjusted odds ratio 9.74 [95% CI 1.03–92.24]; p = 0.047) as a predictor. Our results suggest preliminary support for developing a coccidioidomycosis prediction model for use in clinical settings.

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