Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate
Claire Lhommet1, Denis Garot1, Leslie Grammatico-Guillon2
1CHRU Tours, Service de Médecine Intensive Réanimation, 2 Bd Tonnellé, F-37044, Tours Cedex 9, France.
Background:
Community-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?
Methods:
We 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.
Results:
We 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).
Conclusion:
Neither 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.
Insights
Neither experienced physicians nor artificial intelligence can accurately predict the cause of community-acquired pneumonia (CAP) in the crucial early hours of hospitalization. This study highlights limitations in diagnosing viral versus bacterial pneumonia at admission.
Area of Science:
- Medical Informatics
- Infectious Diseases
- Artificial Intelligence in Medicine
Background:
- Community-acquired pneumonia (CAP) necessitates prompt antimicrobial therapy, yet the causative pathogen is often unidentified upon initial treatment.
- Physicians rely on synthesizing diverse clinical, biological, and radiological data for treatment decisions.
- Artificial intelligence (AI) offers potential for complex data analysis in medical diagnostics.
Purpose of the Study:
- To evaluate the diagnostic capabilities of experienced physicians and an AI algorithm in differentiating viral from bacterial pneumonia at patient admission.
- To assess the predictive performance of AI and expert panels using early clinical data for CAP etiology.
Main Methods:
- A cohort of 153 patients hospitalized with CAP was analyzed, focusing on data available within the first 3 hours of care.
- A machine learning model was developed using clinical, biological, and radiological information.
- The predictive performance was tested on an independent validation set, comparing an AI algorithm against a panel of three expert physicians, both blinded to the final diagnosis.
Main Results:
- The study analyzed 93 cases of CAP with a single identified pathogen.
- AI demonstrated low to moderate discriminant abilities (LR+ 2.12 viral, 6.29 bacterial).
- Expert physicians showed very low to low discriminant abilities (LR+ 3.81 viral, 1.89 bacterial).
Conclusions:
- Neither the AI algorithm nor the expert physicians could reliably predict the microbial etiology of CAP within the initial hours of hospitalization.
- Current AI and expert diagnostic approaches are insufficient for urgent therapeutic strategy decisions in CAP.
- Further research is needed to improve early etiological diagnosis of CAP.
Related Concept Videos
01:30Pneumonia III: Complications and Assessment
01:28Pneumonia IV: Management
Bacterial Pneumonia Treatment
For bacterial pneumonia, antibiotics serve as the cornerstone of therapy. Initial treatment often begins with empirical antibiotics, tailored to the anticipated causative organism and adjusted based on culture results. Key antibiotic choices include:
01:30Pneumonia I: Introduction
Risk Factors
Various factors influence the likelihood of developing pneumonia. Age plays a crucial role, with infants, children under two, and individuals over 65 at increased risk due to their...
01:30Pneumonia V: Nursing management and Prevention
The nurse must practice strict medical asepsis and adhere to infection control guidelines to minimize healthcare-associated infections.
Enhance airway patency
Position the patient correctly to facilitate drainage of the affected lung segments. Manual or mechanical percussion and vibration can also be employed....
01:29Pneumonia II: Pathophysiology
01:28Acute Pyelonephritis II: Diagnostic Studies and Management