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 se

SUBMITTER: Ramadan FA 

PROVIDER: S-EPMC9155888 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature