Tuesday, February 7, 2023
| 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.
| 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.
| 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.
| 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.
| 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.
Physicians - maximum of 1.00 AMA PRA Category 1 Credit(s)™
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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.
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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.
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.
