title: Is procalcitonin-guided antimicrobial use cost-effective in adult patients with suspected bacterial infection and sepsis?
abstract: OBJECTIVE
  Procalcitonin has emerged as a promising biomarker of bacterial infection. Published literature demonstrates that use of procalcitonin testing and an associated treatment pathway reduces duration of antibiotic therapy without impacting mortality. The objective of this study was to determine the financial impact of utilizing a procalcitonin-guided treatment algorithm in hospitalized patients with sepsis.
  
  
  METHODS
  Cost-minimization and cost-utility analysis.
  
  
  METHODS
  Hypothetical cohort of adult ICU patients with suspected bacterial infection and sepsis.
  
  
  METHODS
  Utilizing published clinical and economic data, a decision analytic model was developed from the U.S. hospital perspective. Effectiveness and utility measures were defined using cost-per-clinical episode and cost per quality-adjusted life years (QALYs). Upper and lower sensitivity ranges were determined for all inputs. Univariate and probabilistic sensitivity analyses assessed the robustness of our model and variables. Incremental cost-effectiveness ratios (ICERs) were calculated and compared to predetermined willingness-to-pay thresholds.
  
  
  RESULTS
  Base-case results predicted the use of a procalcitonin-guided treatment algorithm dominated standard care with improved quality (0.0002 QALYs) and decreased overall treatment costs ($65). The model was sensitive to a number of key variables that had the potential to impact results, including algorithm adherence (<42.3%), number and cost of procalcitonin tests ordered (≥9 and >$46), days of antimicrobial reduction (<1.6 d), incidence of nephrotoxicity and rate of nephrotoxicity reduction.
  
  
  CONCLUSIONS
  The combination of procalcitonin testing with an evidence-based treatment algorithm may improve patients' quality of life while decreasing costs in ICU patients with suspected bacterial infection and sepsis; however, results were highly dependent on a number of variables and assumptions.
authors.affiliation: 1Department of Pharmacy Services,St. Joseph Mercy Health System,Ann Arbor,Michigan.
authors.forename: Michelle
authors.initials: M
authors.lastname: Harrison
authors.affiliation: 1Department of Pharmacy Services,St. Joseph Mercy Health System,Ann Arbor,Michigan.
authors.forename: Curtis D
authors.initials: CD
authors.lastname: Collins
mesh_terms: D000328:Adult; D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000191:economics / Q000627:therapeutic use*; D001424:Bacterial Infections / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics; D015415:Biomarkers / Q000097:blood; D002116:Calcitonin / Q000097:blood*; D015740:Calcitonin Gene-Related Peptide; D000066491:Clinical Decision-Making; D003362:Cost-Benefit Analysis; D017721:Hospital Costs / Q000706:statistics & numerical data*; D006760:Hospitalization; D006801:Humans; D018803:Models, Economic; D011498:Protein Precursors / Q000097:blood*; D019057:Quality-Adjusted Life Years; D018805:Sepsis / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy* / Q000191:economics; D014481:United States
chemical_list: D000900:Anti-Bacterial Agents; D015415:Biomarkers; C000606589:CALCA protein, human; D011498:Protein Precursors; D002116:Calcitonin; D015740:Calcitonin Gene-Related Peptide