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Table 1.  

Characteristics Positive, n = 73 Negative, n = 319 Total, n = 392 p value
Median age, y (range) 55 (18–83) 57 (18–98) 57 (18–98) 0.038
Sex, no. (%)       0.514
   F 38 (52.8) 152 (47.9) 190 (48.8)  
   M 34 (47.2) 165 (52.1) 199 (51.2)  
Race, no. (%)       0.024
   African American 8 (11.4) 18 (5.8) 26 (6.8)  
   American Indian/Alaska Native 6 (8.6) 11 (3.5) 17 (4.5)  
   Asian 3 (4.3) 4 (1.3) 7 (1.8)  
   White 52 (74.3) 263 (84.8) 315 (82.9)  
   Unknown 1 (1.4) 14 (4.5) 15 (3.9)  
Ethnicity, no. (%)       0.882
   Hispanic 18 (26.5) 87 (28.1) 105 (27.8)  
   Non-Hispanic 50 (73.5) 223 (71.9) 273 (72.2)  
Median length of endemic residence, y (range) 13 (0–78) 21 (0–98) 20 (0–98) 0.017
Admission status, no. (%)        
   Outpatient 31 (42.5) 49 (15.4) 80 (20.5) <0.001
   Inpatient 42 (57.5) 269 (84.6) 311 (79.5)  
Immunocompromised, no. (%)       0.001
   Y 24 (33.3) 174 (55.1) 198 (51)  
   N 48 (66.7) 142 (44.9) 190 (49)  
 

Table 1. Patient characteristics by confirmed Coccidioides diagnosis in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA*

*Bold text indicates statistical significance.

Table 2.  

Characteristics Outpatient   Inpatient Total, n = 392 p value
Positive, n = 35 Negative, n = 64 Positive, n = 38 Negative, n = 255 Outpatient Inpatient
Median age, y (range) 57 (24–77) 51 (19–93)   45 (18–83) 58 (18–98) 57 (18–98) 0.534 0.022
Sex, no. (%)             0.289 1.000
   F 20 (58.8) 29 (46)   18 (47.4) 123 (48.4) 190 (48.8)    
   M 14 (41.2) 34 (54)   20 (52.6) 131 (51.6) 199 (51.2)    
Race, no. (%)             0.574 0.018
   African American 2 (5.9) 4 (6.6)   6 (16.7) 14 (5.6) 26 (6.8)    
   AI/AN 4 (11.8) 2 (3.3)   2 (5.6) 9 (3.6) 17 (4.5)    
   Asian 1 (2.9) 2 (3.3)   2 (5.6) 2 (0.8) 7 (1.8)    
   White 27 (79.4) 52 (85.2)   25 (69.4) 211 (84.7) 315 (82.9)    
   Unknown 0 1 (1.6)   1 (2.8) 13 (5.2) 15 (3.9)    
Ethnicity, no. (%)             0.808 1.000
   Hispanic 8 (25.8) 18 (30)   10 (27) 69 (27.6) 105 (27.8)    
   Non-Hispanic 23 (74.2) 42 (70)   27 (73) 181 (72.4) 273 (72.2)    
Median length of endemic residence, y (range) 10 (0–59) 20 (0–88)   19 (0–78) 22 (0–98) 20 (0–98) 0.091 0.331
Immunocompromised, no. (%)†             0.344 0.076
   Y 7 (20.6) 20 (31.2)   17 (44.7) 154 (61.1) 198 (51)    
   N 27 (79.4) 44 (68.8)   21 (55.3) 98 (38.9) 190 (49)    
Median length of Illness, d (range) 14 (0–300) 14 (0–5,110)   14 (2–365) 14 (1–8,760) 14 (0–8,760) 0.370 0.972
Symptoms, no. (%)‡                
   Fatigue 19 (54.3) 39 (60.9)   27 (71.1) 203 (79.9) 288 (73.7) 0.531 0.209
   Cough 22 (62.9) 44 (68.8)   26 (68.4) 164 (64.6) 256 (65.5) 0.656 0.718
   Fever 12 (34.3) 24 (37.5)   15 (39.5) 128 (50.4) 179 (45.8) 0.829 0.227
   Chest pain 13 (37.1) 26 (40.6)   14 (36.8) 82 (32.3) 135 (34.5) 0.831 0.583
   Shortness of breath 13 (37.1) 42 (65.6)   25 (65.8) 172 (67.7) 256 (65.5) 0.011 0.853
   Headache 9 (25.7) 22 (34.4)   13 (34.2) 116 (45.7) 160 (40.9) 0.497 0.221
   Night sweats 15 (42.9) 21 (32.8)   13 (34.2) 104 (40.9) 153 (39.1) 0.384 0.481
   Muscle aches 13 (37.1) 28 (43.8)   10 (26.3) 122 (47.8) 163 (41.7) 0.670 0.014
   Joint pain 13 (37.1) 14 (21.9)   7 (18.4) 82 (32.1) 126 (32.2) 0.156 0.051
   Rash 16 (45.7) 3 (4.7)   11 (28.9) 38 (15) 68 (17.3) <0.001 0.037
   Other 11 (31.4) 27 (42.2)   11 (28.9) 74 (29.1) 123 (31.5) 0.388 1.000
Laboratory tests, median (range)              
   Procalcitonin, ng/mL 0.05 (0.012–0.27) 0.10 (0.05–11.59)   0.11 (0.05–92.34) 0.165 (0.02–198.5) 0.11 (0.02–198.5) <0.001 0.617
   C-reactive protein, mg/L 7.40 (0.7–260) 17.5 (0.6–266.3)   46.0 (1.4–170.2) 68.0 (0.6–557) 49.00 (0.6–557) 0.090 0.066
   ESR, mm/h 15.0 (5–76) 26.0 (1–145)   45.0 (6.0–122.0) 46.0 (4–145) 41.0 (1–145) 0.222 0.427
   Leukocytes, × 103 cells/mm3 9.8 (4.6–14) 8.9 (3.7–26.5)   9.0 (0.3–24.4) 10.0 (0.1–45.4) 9.90 (0.1–45.4) 0.560 0.481
   Hemoglobin, g/dL 13.8 (12.4–15.9) 13.3 (6.9–19.7)   12.0 (7.2–17.4) 12.0 (4.8–18) 12.6 (4.8–19.7) 0.163 0.438
   Platelet count, × 103/mm3 312.0 (226–457) 238.0 (94–446)   260 (10–520) 239.0 (5–940) 248.0 (5–940) <0.001 0.676
   Eosinophil count, × 103/µL 0.39 (0–1.4) 0.1 (0–0.8)   0.2 (0.0–3.0) 0.07 (0.0–4.55) 0.1 (0–4.55) <0.001 0.015
   Albumin, g/dL 4.05 (2.5–5) 3.9 (1.9–5)   3.0 (1.4–5.0) 3.1 (0.6–6.4) 3.5 (0.6–6.4) 0.483 0.333
   Total protein, g/dL 7.3 (6.2–8.7) 7.3 (5.9–9.3)   7.35 (5.4–9.3) 6.95 (2.5–12.0) 7.05 (2.5–12) 0.747 0.044

Table 2. Characteristics of inpatients and outpatients by confirmed Coccidioides diagnosis in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA*

*Inpatient participants were recruited from among hospitalized patients; outpatients were recruited from patients in emergency departments and affiliated clinics. Bold text indicates statistical significance. AI/AN, American Indian/Alaskan Native; ESR, erythrocyte sedimentation rate.
†Immunocompromised status was identified as a participant with a weakened immune system at the time of coccidioidomycosis diagnosis, which included participants with type 2 diabetes, HIV/AIDS, lupus, rheumatoid arthritis, or leukemia, and organ transplant recipients and those receiving chemotherapy agents, corticosteroids, and biologic response modifiers. ‡Symptom counts represent the total number of patients reporting the condition.

Table 3.  

Characteristics Univariable model Multivariable model
OR (95% CI) p value aOR (95% CI) p value
Symptoms        
   Rash 19.64 (2.34–164.67) 0.006 9.74 (1.03–92.24) 0.047
   Shortness of breath 0.43 (0.17–1.09) 0.075 0.36 (0.12–1.07) 0.066
Laboratory tests        
   Procalcitonin, ng/mL 0.45 (0.21–0.96) 0.039 0.59 (0.25–1.38) 0.222
   Platelet count, × 103/mm3 1.73 (0.98–3.07) 0.060 1.70 (0.90–3.22) 0.100
   Eosinophil count, × 103/µL 2.18 (1.19–4.01) 0.012 1.62 (0.79–3.32) 0.186

Table 3. Characteristics of outpatients in univariable and multivariable models in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA*

*Participants were recruited from among patients in emergency departments and affiliated clinics, including 35 coccidioidomycosis-positive and 64 coccidioidomycosis-negative participants. Bold text indicates statistical significance. aOR, adjusted OR; OR, odds ratio.

Table 4.  

Characteristics Univariable model Multivariable model
OR (95% CI) p value aOR (95% CI) p value
Demographics        
   Age, y 0.70 (0.50–0.98) 0.035 0.72 (0.51–1.03) 0.071
   Non-White race 2.42 (1.16–5.04) 0.018 2.14 (0.51–1.03) 0.061
Symptoms        
   Muscle aches 0.45 (0.22–0.94) 0.034 0.38 (0.17–0.84) 0.017
   Rash 2.29 (1.08–4.84) 0.030 2.20 (0.97–4.99) 0.060
Clinical feature        
   Immunocompromised 0.49 (0.25–0.94) 0.033 0.64 (0.31–1.31) 0.220
Laboratory tests        
   C-reactive protein, mg/L 0.66 (0.46–0.94) 0.023 0.72 (0.49–1.07) 0.100
   Eosinophil count, × 103/µL 1.65 (1.17–2.34) 0.005 1.50 (1.02–2.19) 0.037
   Total protein, g/dL 1.50 (1.08–2.08) 0.015 1.30 (0.91–1.87) 0.152

Table 4. Characteristics of inpatients in univariable and multivariable models in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA*

*Participants were recruited from among hospitalized patients, including 38 coccidioidomycosis-positive participants and 255 coccidioidomycosis-negative participants. Bold text indicates statistical significance. Bold text indicates statistical significance. aOR, adjusted odds ratio; OR, odds ratio.

Table 5  

Metric Outpatient Inpatient
ROC AUC 78.2 64.3
Sensitivity 72.7 34.4
Specificity 69.5 87.5
Positive predictive value 28.6 11.9
Negative predictive value 93.8 96.4
Prevalence 14.4 4.6
Detection rate 10.5 1.6
Detection prevalence 36.6 13.5
Balanced accuracy 71.1 61.0

Table 5. Performance metrics for outpatient and inpatient multivariable model in a cross-sectional study of clinical predictors of coccidioidomycosis, Arizona, USA*

*ROC AUC, receiver operating characteristic area under the curve.

CME / ABIM MOC

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

  • Authors: Ferris A. Ramadan, MS; Katherine D. Ellingson, PhD; Robert A. Canales, PhD; Edward J. Bedrick, PhD; John N. Galgiani, MD;Fariba M. Donovan, MD/PhD
  • CME / ABIM MOC Released: 5/20/2022
  • Valid for credit through: 5/20/2023
Start Activity

  • Credits Available

    Physicians - maximum of 1.00 AMA PRA Category 1 Credit(s)™

    ABIM Diplomates - maximum of 1.00 ABIM MOC points

    You Are Eligible For

    • Letter of Completion
    • ABIM MOC points

Target Audience and Goal Statement

This activity is intended for infectious disease clinicians, internists, dermatologists, pulmonologists, public health officials, and other clinicians caring for patients with coccidioidomycosis (CM).

The goal of this activity is for the learner to be better able to describe clinical predictors of CM and prediction models for CM for outpatient and inpatient settings using demographic, clinical, and laboratory factors, according to an analysis of ~ 400 participants with suspected CM prospectively enrolled in 2019 in emergency departments and inpatient units in endemic regions in Arizona.

Upon completion of this activity, participants will:

  • Describe clinical predictors of coccidioidomycosis (CM) in outpatient and inpatient settings, according to an analysis of ~ 400 participants with suspected CM prospectively enrolled in 2019
  • Determine prediction models for CM for outpatient and inpatient settings, according to an analysis of ~ 400 participants with suspected CM prospectively enrolled in 2019
  • Identify clinical and public health implications of clinical predictors of CM and prediction models for CM for outpatient and inpatient settings, according to an analysis of 402 participants with suspected CM prospectively enrolled in 2019


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Faculty

  • Ferris A. Ramadan, MS

    Department of Epidemiology and Biostatistics
    Mel and Enid Zuckerman College of Public Health
    University of Arizona
    Tucson, Arizona

  • Katherine D. Ellingson, PhD

    Department of Epidemiology and Biostatistics
    Mel and Enid Zuckerman College of Public Health
    University of Arizona
    Tucson, Arizona

  • Robert A. Canales, PhD

    Department of Environmental and Occupational Health
    Milken Institute School of Public Health
    George Washington University
    Washington, DC

  • Edward J. Bedrick, PhD

    Department of Epidemiology and Biostatistics
    Mel and Enid Zuckerman College of Public Health
    University of Arizona
    Tucson, Arizona

  • John N. Galgiani, MD

    The Valley Fever Center for Excellence
    University of Arizona College of Medicine–Tucson

  • Fariba M. Donovan, MD/PhD

    The Valley Fever Center for Excellence
    University of Arizona College of Medicine–Tucson

CME Author

  • Laurie Barclay, MD

    Freelance writer and reviewer
    Medscape, LLC

    Disclosures

    Disclosure: Laurie Barclay, MD, has disclosed the following relevant financial relationships:
    Stocks, stock options, or bonds: AbbVie Inc. (former)

Editor

  • Amy J. Guinn, BA, MA

    Copyeditor 
    Emerging Infectious Diseases

    Disclosures

    Disclosure: Amy J. Guinn, BA, MA, has disclosed no relevant financial relationships.

Compliance Reviewer

  • Leigh A. Schmidt, MSN, RN, CMSRN, CNE, CHCP

    Associate Director, Accreditation and Compliance
    Medscape, LLC

    Disclosures

    Disclosure: Leigh A. Schmidt, MSN, RN, CMSRN, CNE, CHCP, has disclosed no relevant financial relationships.

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This activity has been peer reviewed and the reviewer has disclosed no relevant financial relationships.


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    Successful completion of this CME activity, which includes participation in the evaluation component, enables the participant to earn up to 1.0 MOC points in the American Board of Internal Medicine's (ABIM) Maintenance of Certification (MOC) program. Participants will earn MOC points equivalent to the amount of CME credits claimed for the activity. It is the CME activity provider's responsibility to submit participant completion information to ACCME for the purpose of granting ABIM MOC credit.

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CME / ABIM MOC

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

Authors: Ferris A. Ramadan, MS; Katherine D. Ellingson, PhD; Robert A. Canales, PhD; Edward J. Bedrick, PhD; John N. Galgiani, MD;Fariba M. Donovan, MD/PhDFaculty and Disclosures

CME / ABIM MOC Released: 5/20/2022

Valid for credit through: 5/20/2023

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Abstract and Introduction

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.

Introduction

Coccidioidomycosis, colloquially known as cocci or Valley fever, is a fungal infection endemic to the southwestern United States and parts of Central and South America[1]. Infection occurs through inhalation of an arthroconidium from the dimorphic, soil-dwelling fungi Coccidioides immitis and C. posadasii. Incidence has increased since 1995, when coccidioidomycosis became a reportable infection[2]. During 2016–2018, the Centers for Disease Control and Prevention reported a 32% increase in coccidioidomycosis cases[3]. Epidemiologic studies suggest climate change, more frequent soilborne dust exposures, and a growing population of older adults in endemic regions as possible causes for increased coccidioidomycosis rates[4]. Despite enhanced surveillance efforts, coccidioidomycosis incidence is underreported[4,5], and estimates suggest ≥150,000 infections annually in the United States[6].

Because of limited ability to prevent Coccidioides exposure in the community and no existing vaccine, coccidioidomycosis poses a substantial burden to patients and healthcare systems in endemic areas[7,8]. Most (60%) Coccidioides infections are subclinical, but clinical cases produce protracted respiratory conditions[9,10]. Observational studies indicate that 15%–29% of community-acquired pneumonia in endemic areas is caused by coccidioidomycosis[11,12]. Diverse and nonspecific manifestations including fatigue, cough, fever, and rash make diagnosis challenging, and coccidioidomycosis can easily be mistaken for other respiratory illnesses, eczema, or bacterial pneumonia. Thus, misdiagnosis and inappropriate treatments are common, and ≤81% of patients are prescribed an antibacterial drug[5,12]. However, few studies have investigated factors associated with increased coccidioidomycosis incidence to support clinical decision-making[13].

Increased incidence and complex clinical manifestations of coccidioidomycosis emphasize the need to improve disease identification in clinical settings. In 2019, we prospectively enrolled participants with suspected coccidioidomycosis to evaluate a novel diagnostic test[14]. For this study, we used data from our prior study to develop a coccidioidomycosis prediction model based on demographic, clinical, and laboratory factors. We developed independent models for outpatient and inpatient settings.