Clinical Predictors of Coccidioidomycosis from a Cross-Sectional Study

Demographic and clinical indicators have been described to support identification of coccidioidomycosis (CM); 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 CM in emergency room and inpatient units within Coccidioides endemic regions. We aimed to develop a predictive CM model among participants presenting with suspected CM. We applied LASSO (least absolute shrinkage and selection operator) to select predictors of CM and report results from univariable and multivariable logistic regression models. Univariable models identified elevated eosinophil count as a significant predictive feature of CM in both inpatient and outpatient settings. Our multivariable outpatient model identified rash (OR=9.74, p= 0.047, 95%CI=1.03–92.24) as a predictor of CM. Our results suggest preliminary support for the development of a CM prediction model for use in the clinical setting.