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

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.

Keywords: Arizona; Coccidioides; United States; Valley fever; coccidioidomycosis; diagnosis; fungi; prediction model; respiratory infections; risk factors.

Figure

Stratification diagram for suspected coccidioidomycosis among inpatients and outpatients in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA. Outpatient participants were recruited from emergency departments and affiliated clinics. Inpatient participants were recruited from among hospitalized patients. MRN, medical record number; +, positive; –, negative.