Cross-Sectional Study of Clinical Predictors of Coccidioidomycosis, Arizona, USA.
Emerg Infect Dis
; 28(6): 1091-1100, 2022 06.
Article
em En
| MEDLINE
| ID: mdl-35608552
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.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Coccidioidomicose
Tipo de estudo:
Diagnostic_studies
/
Observational_studies
/
Prevalence_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Emerg Infect Dis
Assunto da revista:
DOENCAS TRANSMISSIVEIS
Ano de publicação:
2022
Tipo de documento:
Article