Back

Multicentre validation and update of a Legionella prediction score to guide testing and treatment in community-acquired pneumonia

Bigler, M.; Draeger, S.; Zacher, F.; Hattendorf, J.; Maeusezahl, D.; Albrich, W. C.; SwissLEGIO Hospital Network,

2026-02-27 infectious diseases
10.64898/2026.02.25.26347092 medRxiv
Show abstract

ObjectivesDiagnosis of community-acquired Legionnaires disease (CALD) relies on microbiological testing. Routine testing in hospitalised CAP patients has low positivity rates. We externally validated a Legionella prediction score, assessed its applicability in routine care, and explored potential updates. MethodsWe analysed data from 196 CALD patients from 20 Swiss hospitals and 196 Legionella-negative CAP controls matched by date of diagnosis ({+/-}14 days; August 2022-March 2024). We assessed the availability of the original score predictors (fever, no/dry cough, hyponatremia, elevated CRP, elevated LDH, low platelet count) in routine care and the original scores discriminative performance. The dataset was split into development and validation cohorts to evaluate whether simplifying modifications improved predictive performance. ResultsThe original score showed 91% (95% CI: 86-96%) sensitivity and 35% (95% CI: 28-42%) specificity at a cut-off [&ge;]2; LDH was infrequently measured, and platelet count was a poor predictor. The simplified SwissLEGIO score (fever >38{degrees}C, sodium <133 mmol/L, CRP >180 mg/L, no/dry cough, prior {beta}-lactam therapy) maintained high sensitivity (88-92%) and showed improved specificity (46-58%) at cut-off [&ge;]2. ConclusionThe SwissLEGIO score is an easy-to-apply screening tool to rule out CALD in hospitalised CAP patients with scores <2 and may reduce testing by 36-52% at a CALD prevalence of 4%.

Published in International Journal of Infectious Diseases (predicted rank #13) · training set

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.