scholarly journals Modern Aspects of Etiological Diagnostics, Clinical Picture and Treatment of Severe Community-Acquired Pneumonia in Soldiers

2020 ◽  
Vol 22 (1) ◽  
pp. 45-52
Author(s):  
V V Saluhov ◽  
M A Haritonov ◽  
V V Ivanov ◽  
M A Zhurkin ◽  
B A Chumak ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Antiviral Agents ◽  
Military Medicine ◽  
Community Acquired Pneumonia ◽  
Laboratory Research ◽  
Enzyme Linked Immunosorbent Assay ◽  
Atypical Pathogens ◽  
Severe Community Acquired Pneumonia ◽  
Express Methods ◽  
The Military

The problem of community-acquired pneumonia is one of the most relevant for military medicine. The relevance of community-acquired pneumonia is determined by the high incidence of conscription by military personnel, the severity of the clinical course, the presence of severe complications, the duration of labor losses, the tendency to epidemic spread, and the risk of deaths. It is necessary to improve laboratory research methods with the introduction of express methods for verifying bacterial and viral agents, determining the clinical features of the viral-bacterial pneumonia, and justifying the inclusion of antiviral agents in the etiotropic treatment regimen. An expanded complex of microbiological diagnosis of pneumonia has been developed, combining classical bacteriological methods with express methods (polymerase chain reaction, enzyme- linked immunosorbent assay, immunochromatography), which made it possible to determine atypical pathogens and viruses in addition to agents of a bacterial nature. Using these techniques, the modern etiological structure of community-acquired pneumonia in the military has been established, the prevalence of viral-bacterial pneumonia has been revealed. Among viruses, the leadership of adenovirus infection has been established, clinical and laboratory features of the disease are shown depending on the pathogens identified, the feasibility of additional prescribing of antiviral agents in addition to antibiotics in the treatment of viral-bacterial pneumonia is justified.

Diagnosis and management of community-acquired pneumonia

2016 ◽  
Author(s):  
Antoni Torres ◽  
Adamantia Liapikou
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Community Acquired Pneumonia ◽  
Atypical Pathogens ◽  
High Prevalence ◽  
Refractory Septic Shock ◽  
Pneumococcal Bacteraemia ◽  
Severe Community Acquired Pneumonia ◽  
Reason For Admission ◽  
Selection Of

Severe community-acquired pneumonia (SCAP) remains the most common infectious reason for admission to the intensive care unit (ICU), reaching a mortality rate of 30–40%. The microbial pattern of the SCAP has changed with S. pneumoniae still the leading pathogen, but a decrease of atypical pathogens, especially Legionella and an increase of viral and polymicrobial pneumonias. IDSA/ATS issued guidelines on the management of CAP including specific criteria to identify patients for ICU admission with good predictive value. The first selection of antimicrobial therapy should be started early covering all likely pathogens, depending on the presence of the risk factors for Pseudomonas aeruginosa infection. Combination therapy may be useful in patients with non-refractory septic shock and severe sepsis pneumococcal bacteraemia as well. The challenges include the emergence of new pathogens as community-acquired methicillin-resistant Staphylococcus aureus, new influenza virus subtypes and the high prevalence of multidrug resistance, mainly from institutionalizing patients.

Severe Community-Acquired Pneumonia (CAP) due to Atypical Pathogens

2005 ◽  
Vol 1 (3) ◽  
pp. 265-271
Author(s):  
Argyris Michalopoulos ◽  
Michael Rizos ◽  
Matthew Falagas
Keyword(s):  
Community Acquired Pneumonia ◽  
Atypical Pathogens ◽  
Severe Community Acquired Pneumonia

scholarly journals Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

2019 ◽  
Author(s):  
Claire LHOMMET ◽  
Denis GAROT ◽  
Leslie GRAMMATICO-GUILLON ◽  
Cassandra JOURDANNAUD ◽  
Pierre ASFAR ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Positive Likelihood Ratio ◽  
Community Acquired Pneumonia ◽  
Machine Learning Model ◽  
Causal Pathogen ◽  
Microbial Etiology ◽  
Validation Set ◽  
Complex Relationships ◽  
Severe Community Acquired Pneumonia ◽  
Diverse Data

Abstract Background Severe community-acquired pneumonia (sCAP) 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 sCAP and recorded clinical/paraclinical data available in the first 3-hour period of care. 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 sCAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]). 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 sCAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

scholarly journals Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

2020 ◽  
Author(s):  
Claire LHOMMET ◽  
Denis GAROT ◽  
Leslie GRAMMATICO-GUILLON ◽  
Cassandra JOURDANNAUD ◽  
Pierre ASFAR ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Positive Likelihood Ratio ◽  
Community Acquired Pneumonia ◽  
Respiratory Pathogen ◽  
Machine Learning Model ◽  
Causal Pathogen ◽  
Validation Set ◽  
Complex Relationships ◽  
Severe Community Acquired Pneumonia ◽  
Diverse Data

Abstract Background. Severe community-acquired pneumonia (sCAP) 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 sCAP and recorded all data available in the first 3-hour 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 sCAP (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 sCAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

scholarly journals Predicting the microbial cause of community-acquired pneumonia: can physicians or a data-driven method differentiate viral from bacterial pneumonia at patient presentation?

2020 ◽  
Author(s):  
Claire LHOMMET ◽  
Denis GAROT ◽  
Leslie GRAMMATICO-GUILLON ◽  
Cassandra JOURDANNAUD ◽  
Pierre ASFAR ◽  
...  
Keyword(s):  
Bacterial Pneumonia ◽  
Positive Likelihood Ratio ◽  
Community Acquired Pneumonia ◽  
Machine Learning Model ◽  
Causal Pathogen ◽  
Microbial Etiology ◽  
Validation Set ◽  
Complex Relationships ◽  
Severe Community Acquired Pneumonia ◽  
Diverse Data

Abstract Background Severe community-acquired pneumonia (sCAP) 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 sCAP and recorded clinical/paraclinical data available in the first 3-hour period of care. 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 sCAP (70.6% men; 62 [51-73] years old; mean SAPSII, 37 [27-47]). 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 sCAP within the first hours of hospitalization when there is an urgent need to define the anti-infective therapeutic strategy.

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