Emerg Infect Dis. 2022 Jun;28(6):1091-1100. doi: 10.3201/eid2806.212311.
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.
人口统计学和临床指标已被描述用于支持荚膜组织胞浆菌病的识别；然而，这些情况的相互作用尚未在临床环境中得到探索。2019 年，我们在荚膜组织胞浆菌病流行地区的急诊科和住院病房招募了 392 名疑似荚膜组织胞浆菌病的参与者进行横断面研究。我们旨在为疑似荚膜组织胞浆菌病的参与者开发一个预测模型。我们应用最小绝对收缩和选择算子（LASSO）对特定的荚膜组织胞浆菌病预测因子进行了分析，并建立了单变量和多变量逻辑回归模型。单变量模型确定了嗜酸性粒细胞计数升高是住院和门诊环境中荚膜组织胞浆菌病的统计学显著预测特征。我们的多变量门诊模型还确定了皮疹（调整后的优势比 9.74 [95%置信区间 1.03-92.24]；p=0.047）是一个预测因子。我们的结果初步支持开发用于临床环境的荚膜组织胞浆菌病预测模型。
