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Published on: August 30, 2018
When to include polymyxins in the empirical antibiotic regimen in critically ill patients with fever? A decision
Matthew E Falagas1, Petros I Rafailidis
1Alfa Institute of Biomedical Sciences, Athens, Greece. m.falagas@aibs.gr
Abstract:
We sought to approach a practical question: Should polymyxins be used in the initial empirical antibiotic regimen in the intensive care unit (ICU) patient with fever that is thought to be due to infection? By retrieving data from the literature and the WHONET Greece, we formulated a mathematical model to estimate the probability (Ptotal) that a gram-negative bacterium susceptible only to polymyxins is isolated from an ICU patient. Ptotal = P1 * P2 * P3 * P4, where Ptotal = total probability; P1 = probability that fever is due to infection; P2 = probability that infection is due to gram-negative bacteria; P3 = probability that the gram-negative bacterium is Acinetobacter baumannii, Pseudomonas aeruginosa, or Klebsiella pneumoniae; and P4 = probability that A. baumannii, P. aeruginosa, or K. pneumoniae is susceptible only to polymyxins. Using the information from our data sources, we estimated that P1 (before physician input in differential diagnosis) = 0.5, P2 = 0.523, P3 = 0.79, and P4 = 0.567, thus Ptotal = P1 * P2 * P3 * P4 = 0.5 * 0.523 * 0.79 * 0.567 = 0.117 = 11.7%. Based on this information and combining it with data regarding the attributable mortality of inappropriate empirical antimicrobial treatment, 4 to 5 patients in every 100 ICU patients will die if physicians do not include polymyxins in the initial empirical regimen in the ICU setting for an episode of fever due to infection. Polymyxins should probably be included in the empirical antibiotic regimen in the ICU setting in hospitals, where the observed probability that a gram-negative bacterium (A. baumannii, P. aeruginosa, or K. pneumoniae) is polymyxin-only-susceptible is close to that (50%) used in our model (based on the individual hospital data).
Insights
Polymyxins are crucial in empirical antibiotic regimens for intensive care unit (ICU) patients with fever. Omitting them risks preventable deaths from gram-negative infections, particularly those resistant to other drugs.
Area of Science:
- Infectious Diseases
- Critical Care Medicine
- Pharmacology
Background:
- Fever in intensive care unit (ICU) patients often indicates infection, necessitating prompt empirical antibiotic treatment.
- The rise of antimicrobial resistance poses challenges for selecting effective empirical therapies.
- Polymyxins are reserved for multidrug-resistant gram-negative infections.
Purpose of the Study:
- To determine the probability of isolating polymyxin-susceptible gram-negative bacteria in ICU patients with fever.
- To evaluate the impact of including polymyxins in initial empirical antibiotic regimens in the ICU.
- To inform clinical decision-making regarding empirical antibiotic strategies in critical care.
Main Methods:
- A mathematical model was developed to estimate the probability of isolating polymyxin-only-susceptible gram-negative bacteria.
- Data were retrieved from literature reviews and the WHONET Greece database.
- Probabilities of infection, gram-negative etiology, specific bacterial species, and polymyxin susceptibility were calculated.
Main Results:
- The overall probability (Ptotal) of isolating a polymyxin-only-susceptible gram-negative bacterium was estimated at 11.7%.
- Failure to include polymyxins in empirical regimens could lead to 4-5 deaths per 100 ICU patients due to inappropriate treatment.
- Key contributors to Ptotal included the probability of gram-negative infection (52.3%) and polymyxin-only susceptibility (56.7%).
Conclusions:
- Polymyxins should be considered for inclusion in empirical antibiotic regimens for ICU patients with fever, especially in settings with high rates of polymyxin-only-susceptible gram-negative bacteria.
- This strategy can mitigate mortality associated with inappropriate empirical antimicrobial treatment.
- Hospital-specific data on bacterial susceptibility patterns are crucial for optimizing empirical therapy choices.
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