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.
Highlights
Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy
Neither experts nor an Artificial intelligence (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
A growing body of research has recently suggested that difficulties in accessing, organizing, and using a substantial amount of data could be significantly ameliorated by use of emerging artificial intelligence (AI)-derived methods, which are nowadays applied in diverse fields including biology, computer science and sociology [11]
Summary
Community-acquired pneumonia (CAP) requires urgent and specific antimicrobial therapy. The first time point of this organized approach requires the physician to ask: “Does this patient have an infection that requires antibiotics?”. This question aims to remind the clinician to synthesize all relevant patient information to determine the likelihood of an infection that requires antibiotic therapy. In the context of CAP in intensive care units (ICUs), where information are diverse, we wondered if an AI datadriven approach to reducing the medical complexity of a patient could allow us to make a better hypothesis regarding the microbial etiology at the patient’s presentation
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