Role of rapid diagnostics for viral respiratory infections in antibiotic prescribing decision in the emergency department

Abstract

Objective:To describe the frequency of antibiotic prescriptions in patients with known viral respiratory infections (VRIs) diagnosed by polymerase chain reaction (PCR) in 3 emergency departments (EDs) and to identify patient characteristics that influence the prescribing of antibiotics by ED physicians despite PCR confirmation of viral cause.Design:Retrospective, observational analysis of patients with PCR-diagnosed VRI discharged from 3 acute-care hospital EDs within 1 health system.Results:In total, 323 patients were discharged from the ED with a VRI diagnosis, of whom 68 were prescribed antibiotics (21.1%). These patients were older (median, 59.5 vs 43 years; P = .04), experienced symptoms longer (median, 4 vs 2 days; P = .002), were more likely to have received antibiotics in the preceding 7 days (27.9% vs 9.8%; P < .001), and had higher proportions of abnormal chest X-rays (64.5% vs 28.4%; P < .001). Patients were more likely to receive antibiotics for a diagnosis of pneumonia (39.7% vs 1.6%; P < .001) or otitis media (7.4% vs 0.4%; P = .002), and were less likely with diagnosis of upper respiratory infection (2.9% vs 13.7%; P = .02) or influenza (20.6% vs 44.3%; P < .001).Conclusions:Despite a diagnosis of VRI, one-fifth of ED patients were prescribed antibiotics. Patient characteristics including age, duration of symptoms, abnormal chest X-rays, and specific diagnosis may increase provider concern for concurrent bacterial infections. Opportunities exist for antimicrobial stewardship strategies to incorporate rapid diagnostics in promoting judicious antibiotic usage in the ED.

RE: Role of rapid diagnostics for viral respiratory infections in antibiotic prescribing decision in the emergency department, by Li et al (2019)

“…To the Editor-Li et al 1 recently published a notable article evaluating the frequency of patients who had a viral respiratory infection (VRI) confirmed via rapid polymerase chain reaction (PCR) and were prescribed an antibiotic prior to discharge at 3 distinct emergency departments (EDs) within a single healthcare network. Antibiotic use is associated with adverse events and leads to antibiotic resistance.…”

“…2,3 In the United States, >2.8 million antibiotic-resistance infections occur annually, which highlights the importance of research in appropriate antibiotic prescribing. 3 In their study, Li et al 1 conducted an exploratory analysis using multivariable logistic regression to evaluate the predictors influencing antibiotic prescriptions in patients with PCR-confirmed VRI prior to discharge from the emergency department. Their study reveals 3 notable predictors that we believe are likely to bear out as meaningful predictors.…”

“…The initial covariates selected were age; immunocompromised status (ie, chronic kidney disease, diabetes mellitus, human immunodeficiency virus, or an active malignancy); receipt of antibiotics within 7 days prior to their ED visit; duration of symptoms; hemoglobin levels; abnormal chest x-ray results; and discharge diagnoses of influenza, upper respiratory tract infection, pneumonia, and otitis media. 1 In table 3 of this article, the final model reported effect estimates for the following covariates: receipt of antibiotics within 7 days prior to the ED visit, immunocompromised state, abnormal chest x-ray results, and discharge diagnosis of pneumonia. 1 Readers should use caution when interpreting results like these because they may be biased due to what is known as the " Table 2 Fallacy."…”

“…1 In table 3 of this article, the final model reported effect estimates for the following covariates: receipt of antibiotics within 7 days prior to the ED visit, immunocompromised state, abnormal chest x-ray results, and discharge diagnosis of pneumonia. 1 Readers should use caution when interpreting results like these because they may be biased due to what is known as the " Table 2 Fallacy." 5 The " Table 2 Fallacy" is when effect estimates for multiple exposures and their confounders are estimated from the same statistical model, results that are often presented in an article's " Table 2."…”

“…To illustrate, we have calculated crude odds ratios for the covariates included in the final model. Based on table 3 of Li et al, 1 VRI serves as the primary exposure and the prescription of antibiotics is the outcome. In this model, all of the covariates are adjusted for the exposure of viral respiratory infection.…”