CRP under 130 mg/L rules out the diagnosis of Legionella pneumophila serogroup 1 (URINELLA Study).

INTRODUCTION: In case of pneumonia, some biological findings are suggestive for Legionnaire's disease (LD) including C-reactive protein (CRP). A low level of CRP is predictive for negative Legionella Urinary-Antigen-Test (L-UAT). METHOD: Observational retrospective study in Nord-Franche-Comté Hospital with external validation in Besançon University Hospital, France which included all adults with L-UAT performed during January 2018 to December 2022. The objective was to determine CRP optimal threshold to predict a L-UAT negative result. RESULTS: URINELLA included 5051 patients (83 with positive L-UAT). CRP optimal threshold was 131.9 mg/L, with a negative predictive value (NPV) at 100%, sensitivity at 100% and specificity at 58.0%. The AUC of the ROC-Curve was at 88.7% (95% CI, 86.3-91.1). External validation in Besançon Hospital patients showed an AUC at 89.8% (95% CI, 85.5-94.1) and NPV, sensitivity and specificity was respectively 99.9%, 97.6% and 59.1% for a CRP threshold at 131.9 mg/L; after exclusion of immunosuppressed patients, index sensitivity and NPV reached also 100%. CONCLUSION: In case of pneumonia suspicion with a CRP level under 130 mg/L (independently of the severity) L-UAT is useless in immunocompetent patients with a NPV at 100%. We must remain cautious in patients with symptoms onset less than 48 h before CRP dosage.

The respiratory microbiota alpha-diversity in chronic lung diseases: first systematic review and meta-analysis.

BACKGROUND: While there seems to be a consensus that a decrease in gut microbiome diversity is related to a decline in health status, the associations between respiratory microbiome diversity and chronic lung disease remain a matter of debate. We provide a systematic review and meta-analysis of studies examining lung microbiota alpha-diversity in patients with asthma, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF) or bronchiectasis (NCFB), in which a control group based on disease status or healthy subjects is provided for comparison. RESULTS: We reviewed 351 articles on title and abstract, of which 27 met our inclusion criteria for systematic review. Data from 24 of these studies were used in the meta-analysis. We observed a trend that CF patients have a less diverse respiratory microbiota than healthy individuals. However, substantial heterogeneity was present and detailed using random-effects models, which limits the comparison between studies. CONCLUSIONS: Knowledge on respiratory microbiota is under construction, and for the moment, it seems that alpha-diversity measurements are not enough documented to fully understand the link between microbiota and health, excepted in CF context which represents the most studied chronic respiratory disease with consistent published data to link alpha-diversity and lung function. Whether differences in respiratory microbiota profiles have an impact on chronic respiratory disease symptoms and/or evolution deserves further exploration.

Deep Learning Transformer Models for Building a Comprehensive and Real-time Trauma Observatory: Development and Validation Study.

BACKGROUND: Public health surveillance relies on the collection of data, often in near-real time. Recent advances in natural language processing make it possible to envisage an automated system for extracting information from electronic health records. OBJECTIVE: To study the feasibility of setting up a national trauma observatory in France, we compared the performance of several automatic language processing methods in a multiclass classification task of unstructured clinical notes. METHODS: A total of 69,110 free-text clinical notes related to visits to the emergency departments of the University Hospital of Bordeaux, France, between 2012 and 2019 were manually annotated. Among these clinical notes, 32.5% (22,481/69,110) were traumas. We trained 4 transformer models (deep learning models that encompass attention mechanism) and compared them with the term frequency-inverse document frequency associated with the support vector machine method. RESULTS: The transformer models consistently performed better than the term frequency-inverse document frequency and a support vector machine. Among the transformers, the GPTanam model pretrained with a French corpus with an additional autosupervised learning step on 306,368 unlabeled clinical notes showed the best performance with a micro F1-score of 0.969. CONCLUSIONS: The transformers proved efficient at the multiclass classification of narrative and medical data. Further steps for improvement should focus on the expansion of abbreviations and multioutput multiclass classification.

New Insights in Microbial Species Predicting Lung Function Decline in CF: Lessons from the MucoFong Project.

Several predictive models have been proposed to understand the microbial risk factors associated with cystic fibrosis (CF) progression. Very few have integrated fungal airways colonisation, which is increasingly recognized as a key player regarding CF progression. To assess the association between the percent predicted forced expiratory volume in 1 s (ppFEV1) change and the fungi or bacteria identified in the sputum, 299 CF patients from the "MucoFong" project were included and followed-up with over two years. The relationship between the microorganisms identified in the sputum and ppFEV1 course of patients was longitudinally analysed. An adjusted linear mixed model analysis was performed to evaluate the effect of a transient or chronic bacterial and/or fungal colonisation at inclusion on the ppFEV1 change over a two-year period. Pseudomonas aeruginosa, Achromobacter xylosoxidans, Stenotrophomonas maltophilia, and Candida albicans were associated with a significant ppFEV1 decrease. No significant association was found with other fungal colonisations. In addition, the ppFEV1 outcome in our model was 11.26% lower in patients presenting with a transient colonisation with non-pneumoniae Streptococcus species compared to other patients. These results confirm recently published data and provide new insights into bacterial and fungal colonisation as key factors for the assessment of lung function decline in CF patients.

Long-term chronic diseases and crash responsibility: a record linkage study.

OBJECTIVE: To assess the population impact of chronic conditions on the risk of road traffic crashes. METHODS: Data from three French national databases were extracted and matched: the national health care insurance database, police reports and the national police database of injurious crashes. Exposure to chronic conditions were compared between responsible and nonresponsible drivers. Analysis was performed using the Lasso (least absolute shrinkage and selection operator) method. RESULTS: 69,630 drivers involved in an injurious crash in France between 2005 and 2008, were included. 6210 (8.9%) were suffering from at least one long-term disease. When adjusted for prescription of medicines, blood alcohol, demographic driver characteristics and crash characteristics, increased risk of being responsible for a crash was found in drivers registered in the French healthcare database with the following long-term diseases: epilepsy (odds ratio [OR]=2.53 [1.53-4.20]), type 1 diabetes (OR=1.47) [1.12-1.92], alcoholic liver disease (OR=3.37 [1.40-8.13]), asthma (OR=1.72 [1.13-2.60]) and specific personality disorders (OR=1.35 [1.05-1.74]). No association was found for cardiovascular diseases or Alzheimer's disease. CONCLUSION: The results update the list of medical conditions that may impair driving skills. However, results should be considered cautiously with regards to potential regulatory driving judgments that could have a negative impact on patients' social life.