A risk model to identify Legionella among patients admitted with community‐acquired pneumonia: A retrospective cohort study. Issue 8 (26th July 2022) - Rothberg, Michael B.; Imrey, Peter B.; Guo, Ning; Deshpande, Abhishek; Higgins, Thomas L.; Lindenauer, Peter K.
Abstract: Background: Guidelines recommend testing hospitalized patients with community‐acquired pneumonia (CAP) for Legionella pneumophila only if the infection is severe or risk factors are present. There are no validated models for predicting Legionella . Objective: To derive and externally validate a model to predict a positive Legionella test. Design, Setting and Participants: Diagnostic study of adult inpatients with pneumonia using data from 177 US hospitals in the Premier Healthcare Database (training and hold‐out validation sets) and 12 Cleveland Clinic Health System (CCHS) hospitals (external validation set). We used multiple logistic regression to predict positive Legionella tests in the training set, and evaluated performance in both validation sets. Main Outcome and Measures: The outcome was a positive Legionella test. Potential predictors included demographics and co‐morbidities, disease severity indicators, season, region, and presence of a local outbreak. Results: Of 166, 689 patients hospitalized for pneumonia, 43, 070 were tested for Legionella and 642 (1.5%) tested positive. The strongest predictors of a positive test were a local outbreak (odds ratio [OR], 3.4), June–October occurrence (OR, 3.4), hyponatremia (OR, 3.3), smoking (OR, 2.4), and diarrhea (OR, 2.0); prior admission within 6 months (OR, 0.27) and chronic pulmonary disease (OR, 0.49) were associated with a negative test. Model c ‐statistics were 0.79 in the Premier and 0.77 in the CCHS validation samples. High‐risk patients were only slightly more likely to have been tested than lower‐risk patients. Compared to actual practice, the model‐based testing strategy detected twice as many cases. Conclusions: Although Legionella is an uncommon cause of pneumonia, patient characteristics can identify individuals at high risk, allowing for more efficient testing.