期刊
EMERGING INFECTIOUS DISEASES
卷 28, 期 6, 页码 1091-1100出版社
CENTERS DISEASE CONTROL & PREVENTION
DOI: 10.3201/eid2806.212311
关键词
-
资金
- Centers for Disease Control and Prevention [75D30118C02899]
Demographic and clinical indicators have been described to support identification of coccidioidomycosis, but their interplay has not been explored in a clinical setting. This study aimed to develop a predictive model for coccidioidomycosis among participants with suspected infection.
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.
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