Legionnaires’ disease in the time of COVID-19

Due to similarities in initial disease presentation, clinicians may be inclined to repeatedly test community-acquired pneumonia cases for COVID-19 before recognizing the need to test for Legionnaires’ disease. Legionnaires’ disease is an illness characterized by pneumonia that has a summer/early fall seasonality due to favorable conditions for Legionella growth and exposure. Legionella proliferate in warm water environments and stagnant sections of indoor plumbing and cooling systems. During the ongoing pandemic crisis, exposures to aerosolized water from recently reopened office or retail buildings should be considered as an epidemiologic risk factor for Legionella exposure and an indication to test. The majority of Legionnaires’ disease cases occurring each year are not diagnosed, and some experts recommend that all patients hospitalized with community-acquired pneumonia without a known etiology be tested for Legionella infection. Proper diagnosis can increase the likelihood of appropriate and timely antibiotic treatment, identify potential clusters of disease, and facilitate source attribution.

Comparing Patient Risk Factor-, Sequence Type-, and Resistance Locus Identification-Based Approaches for Predicting Antibiotic Resistance in Escherichia coli Bloodstream Infections

ABSTRACT Rapid diagnostic tests for antibiotic resistance that identify the presence or absence of antibiotic resistance genes/loci are increasingly being developed. However, these approaches usually neglect other sources of predictive information which could be identified over shorter time periods, including patient epidemiologic risk factors for antibiotic resistance and markers of lineage. Using a data set of 414 Escherichia coli isolates recovered from separate episodes of bacteremia at a single academic institution in Toronto, Ontario, Canada, between 2010 and 2015, we compared the potential predictive ability of three approaches (epidemiologic risk factor-, pathogen sequence type [ST]-, and resistance gene identification-based approaches) for classifying phenotypic resistance to three antibiotics representing classes of broad-spectrum antimicrobial therapy (ceftriaxone [a 3rd-generation cephalosporin], ciprofloxacin [a fluoroquinolone], and gentamicin [an aminoglycoside]). We used logistic regression models to generate model receiver operating characteristic (ROC) curves. Predictive discrimination was measured using apparent and corrected (bootstrapped) areas under the curves (AUCs). Epidemiologic risk factor-based models based on two simple risk factors (prior antibiotic exposure and recent prior susceptibility of Gram-negative bacteria) provided a modest predictive discrimination, with AUCs ranging from 0.65 to 0.74. Sequence type-based models demonstrated strong discrimination (AUCs, 0.83 to 0.94) across all three antibiotic classes. The addition of epidemiologic risk factors to sequence type significantly improved the ability to predict resistance for all antibiotics (P < 0.05). Resistance gene identification-based approaches provided the highest degree of discrimination (AUCs, 0.88 to 0.99), with no statistically significant benefit being achieved by adding the patient epidemiologic predictors. In summary, sequence type or other lineage-based approaches could produce an excellent discrimination of antibiotic resistance and may be improved by incorporating readily available patient epidemiologic predictors but are less discriminatory than identification of the presence of known resistance loci.