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

Emerging Infectious Diseases

关键词: coccidioidomycosis;    Coccidioides;    fungi;    respiratory infections;    Valley fever;    risk factors;   

DOI  :  10.3201/eid2806.212311

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown