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

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Ramadan, Ferris A. ; Ellingson, Katherine D. ; Canales, Robert A. ; Bedrick, Edward J. ; Galgiani, John N. ; Donovan, Fariba M.
• Description:
  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.
• Subjects:
  Coccidioidomycosis Cross-Sectional Studies Humans
• Source:
  Emerg Infect Dis. 2022; 28(6):1091-1100
• Pubmed ID:
  35608552
• Pubmed Central ID:
  PMC9155888
• Document Type:
  Journal Article
• Place as Subject:
  Arizona
• Volume:
  28
• Issue:
  6
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:669e9180ff061a08fb88b8c612e1b9cb9de85bfc43f56416e1f81646371f4e1c
• Download URL:
  https://stacks.cdc.gov/view/cdc/118178/cdc_118178_DS1.pdf
• File Type:
  [PDF - 776.35 KB ]