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. DESIGN: Cost-minimization and cost-utility analysis. PATIENTS: 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. CONCLUSION: 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.
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. DESIGN: Cost-minimization and cost-utility analysis. PATIENTS: 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. CONCLUSION: 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: Marcus J Schultz; Martin W Dunser; Arjen M Dondorp; Neill K J Adhikari; Shivakumar Iyer; Arthur Kwizera; Yoel Lubell; Alfred Papali; Luigi Pisani; Beth D Riviello; Derek C Angus; Luciano C Azevedo; Tim Baker; Janet V Diaz; Emir Festic; Rashan Haniffa; Randeep Jawa; Shevin T Jacob; Niranjan Kissoon; Rakesh Lodha; Ignacio Martin-Loeches; Ganbold Lundeg; David Misango; Mervyn Mer; Sanjib Mohanty; Srinivas Murthy; Ndidiamaka Musa; Jane Nakibuuka; Ary Serpa Neto; Mai Nguyen Thi Hoang; Binh Nguyen Thien; Rajyabardhan Pattnaik; Jason Phua; Jacobus Preller; Pedro Povoa; Suchitra Ranjit; Daniel Talmor; Jonarthan Thevanayagam; C Louise Thwaites Journal: Intensive Care Med Date: 2017-03-27 Impact factor: 17.440
Authors: John E Schneider; Jonathan Romanowsky; Philipp Schuetz; Ivana Stojanovic; Henry K Cheng; Oliver Liesenfeld; Ljubomir Buturovic; Timothy E Sweeney Journal: J Health Econ Outcomes Res Date: 2020-04-29
Authors: Michelle M A Kip; Jos A van Oers; Arezoo Shajiei; Albertus Beishuizen; A M Sofie Berghuis; Armand R Girbes; Evelien de Jong; Dylan W de Lange; Maarten W N Nijsten; Maarten J IJzerman; Hendrik Koffijberg; Ron Kusters Journal: Crit Care Date: 2018-11-13 Impact factor: 9.097
Authors: Janne C Mewes; Michael S Pulia; Michael K Mansour; Michael R Broyles; H Bryant Nguyen; Lotte M Steuten Journal: PLoS One Date: 2019-04-23 Impact factor: 3.240