ArticleBMC pulmonary medicine2020
Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?
Article in BMC pulmonary medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 43 citations in OpenAlex.
- Development and validation of an explainable model for Mycoplasma pneumoniae infection in children.Scientific reports · 2026Article
- Host, Pathogen, and Pulmonary Anatomy in Pneumonia: An Integrated Imaging and Clinical Perspective.Diagnostics (Basel, Switzerland) · 2026Review
- Challenges and opportunities in the management of severe pneumonia. Key concepts from the Seventh Annual Meeting of Spanish Experts 2025.Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia · 2026Review
- Cis-aconitate therapy protects against influenza mortality by dual targeting of viral polymerase and ERK/AKT/NF-κB signaling.EMBO molecular medicine · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- AI-driven diagnosis of pneumococcal infections in children-a call for primary-care focused and a coherent evidence base.Frontiers in artificial intelligence · 2026Review
- Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future.Journal of intensive medicine · 2024Review
- Ten Issues for Updating in Community-Acquired Pneumonia: An Expert Review.Journal of clinical medicine · 2023Review
- Serum-integrated omics reveal the host response landscape for severe pediatric community-acquired pneumonia.Critical care (London, England) · 2023Article
- Mechanically ventilated COVID-19 patients failed to meet the criteria for the Berlin definition of ARDS.Infection · 2022Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCommunity-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. However, the causal pathogen is typically unknown at the point when anti-infective therapeutics must be initiated. Physicians synthesize information from diverse data streams to make appropriate decisions. Artificial intelligence (AI) excels at finding complex relationships in large volumes of data. We aimed to evaluate the abilities of experienced physicians and AI to answer this question at patient admission: is it a viral or a bacterial pneumonia?
methodsWe included patients hospitalized for CAP and recorded all data available in the first 3-h period of care (clinical, biological and radiological information). For this proof-of-concept investigation, we decided to study only CAP caused by a singular and identified pathogen. We built a machine learning model prediction using all collected data. Finally, an independent validation set of samples was used to test the pathogen prediction performance of: (i) a panel of three experts and (ii) the AI algorithm. Both were blinded regarding the final microbial diagnosis. Positive likelihood ratio (LR) values > 10 and negative LR values < 0.1 were considered clinically relevant.
resultsWe included 153 patients with CAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]), 37% had viral pneumonia, 24% had bacterial pneumonia, 20% had a co-infection and 19% had no identified respiratory pathogen. We performed the analysis on 93 patients as co-pathogen and no-pathogen cases were excluded. The discriminant abilities of the AI approach were low to moderate (LR+ = 2.12 for viral and 6.29 for bacterial pneumonia), and the discriminant abilities of the experts were very low to low (LR+ = 3.81 for viral and 1.89 for bacterial pneumonia).
conclusionNeither experts nor an AI algorithm can predict the microbial etiology of CAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.
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