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. 2025 Jan;9(1):15-25.
doi: 10.1007/s41669-024-00538-y. Epub 2024 Nov 19.

Health Economic Evaluation of Antimicrobial Stewardship, Procalcitonin Testing, and Rapid Blood Culture Identification in Sepsis Care: A 90-Day Model-Based, Cost-Utility Analysis

Wendy I Sligl  1   2 , Charles Yan  3 , Jeff Round  3 , Xiaoming Wang  4   5 , Justin Z Chen  6 , Cheyanne Boehm  7 , Karen Fong  8 , Katelynn Crick  9 , Míriam Garrido Clua  9 , Cassidy Codan  10 , Tanis C Dingle  11   12 , Connie Prosser  13 , Guanmin Chen  14 , Alena Tse-Chang  15 , Daniel Garros  16 , David Zygun  9 , Dawn Opgenorth  9 , John M Conly  17   18 , Christopher J Doig  8 , Vincent I Lau  9 , Sean M Bagshaw  9   19
Affiliations

Health Economic Evaluation of Antimicrobial Stewardship, Procalcitonin Testing, and Rapid Blood Culture Identification in Sepsis Care: A 90-Day Model-Based, Cost-Utility Analysis

Wendy I Sligl et al. Pharmacoecon Open. 2025 Jan.

Abstract

Objective: We evaluated the cost-effectiveness of a bundled intervention including an antimicrobial stewardship program (ASP), procalcitonin (PCT) testing, and rapid blood culture identification (BCID), compared with pre-implementation standard care in critically ill adult patients with sepsis.

Methods: We conducted a decision tree model-based cost-effectiveness analysis alongside a previously published pre- and post-implementation quality improvement study. We adopted a public Canadian healthcare payer's perspective. Two intensive care units in Alberta with 727 adult critically ill patients were included. Our bundled intervention was compared with pre-implementation standard care. We collected healthcare resource use and estimated unit costs in 2022 Canadian dollars (CAD) over a time horizon from study entry to hospital discharge or death. We calculated the incremental net monetary benefit (iNMB) of the intervention group compared with the pre-intervention group. The primary outcome was cost per sepsis case. Secondary outcomes included readmission rates, Clostridioides difficile infections, mortality, and lengths of stay. Uncertainty was investigated using cost-effectiveness acceptability curves, cost-effectiveness plane scatterplots, and sensitivity analyses.

Results: Mean (standard deviation [SD]) cost per index hospital admission was CAD $83,251 ($107,926) for patients in the intervention group and CAD $87,044 ($104,406) for the pre-intervention group, though the difference ($3,793 [$7,897]) was not statistically significant. Costs were higher in the pre-intervention group for antibiotics, readmissions, and C. difficile infections. The intervention group had a lower mean expected cost; $110,580 ($108,917) compared with pre-intervention ($125,745 [$113,210]), with a difference of $15,165 ($8278). There were no statistically significant differences in quality adjusted life years (QALYs) between groups. The iNMB of the intervention group compared with pre-intervention was greater than $15,000 for willingness-to-pay (WTP) per QALY values of between $0 and $100,000. In our sensitivity analysis, the intervention was most likely to be cost-effective in roughly 56% of simulations at all WTP thresholds.

Conclusions: Our bundled intervention of ASP, PCT, and BCID among adult critically ill patients with sepsis was potentially cost-effective, but with substantial decision uncertainty.

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Conflict of interest statement

Declarations. Funding: Funding was provided through a partnership grant between Alberta Innovates, Alberta Health Services, and bioMérieux. The investigators designed the study, wrote the manuscript, and vouch for the accuracy and completeness of the data and analyses. The funding organizations and partners were not involved in implementation or management of the study, in the analysis of data, or in the decision to submit the manuscript for publication. Competing Interests: Jeff Round is an editorial board member of PharmacoEconomics Open. He was not involved in the selection of peer reviewers for the manuscript nor any of the subsequent editorial decisions. John Conly was an invited speaker at a symposium on antimicrobial resistance co-hosted by the University of Toronto and bioMérieux Canada in 2022. All other authors have no relevant conflicts to declare. Availability of Data and Material: The data that support the findings of this study are available upon reasonable request if permitted by Alberta Health Services; restrictions may apply. The anonymized model is publicly available on request. Ethics Approval and Consent to Participate: The study was approved by the Institutional Review Boards (IRBs) at the University of Alberta (Pro00101420) and the University of Calgary (REB17-2244). Individual patient consent was waived by both IRBs given the study was classified as a quality improvement initiative. Consent for Publication: Not applicable. Code Availability: The decision tree model was conducted using TreeAge software and the statistical analysis was conducted in R. Both files are included as supplementary material. Author Contributions: Conceptualization: C.D., D.Z., D.O., J.M.C., J.C., S.B., J.R., V.L., W.S. Data curation: K.C., C.B., M.G., C.C., T.D., C.P., A.T., D.G., D.O., J.M.C., J.C., K.F., W.S. Formal Analysis: X.W., C.Y., C.D., D.O., J.M.C., J.C., S.B., J.R., V.L., W.S. Funding acquisition: D.Z., D.O., S.B., W.S. Investigation: K.C., C.B., M.G., C.C., T.D., C.P., C.Y., G.C., A.T., D.G., C.D., D.Z., D.O., J.M.C., J.C., K.F., J.R., V.L., S.B., W.S. Methodology: X.W., C.Y., G.C., C.D., J.M.C., J.C., S.B., J.R., V.L., W.S. Project administration: D.Z., D.O., S.B., W.S. Resources: D.Z., D.O., S.B., W.S. Software: D.O., J.C., X.W. Supervision: S.B., W.S. Validation: S.B., W.S. Visualization: S.B., W.S. Writing—original draft: C.Y., C.D., J.M.C., S.B., J.R., V.L., S.B., W.S. Writing—reviewing and editing: K.C., C.B., M.G., C.C., T.D., C.P., C.Y., G.C., A.T., D.G., C.D., D.Z., D.O., J.M.C., J.C., K.F., J.R., V.L., S.B., W.S.

Figures

Fig. 1
Fig. 1
Patient flowchart. The flowchart outlines the process for patients admitted to the ICU with confirmed or suspected sepsis. The intervention group includes ASP, PCT, and BCID testing. Surviving patients are investigated for adverse events (i.e., CDI) and associated costs. Decision points assess first and second hospital readmissions. The flowchart ends with two possible states: patient death in the hospital or no hospital readmission. The control group follows a similar flow, but lacks ASP, PCT, and BCID implementation. The probabilities at each decision point within the decision tree pathways are determined through statistical analysis of the study data. Detailed results are provided in Tables 1 and S.2. Additionally, point estimates of these probabilities are available in Table S.3. AE adverse event, ASP antimicrobial stewardship program, BCID blood culture identification, CDI Clostridioides difficile infection, died died in hospital, PCT procalcitonin, re-ad re-admission
Fig. 2
Fig. 2
Cost-effectiveness plane scatterplot of intervention compared with pre-intervention. The CE scatterplot shows the distribution of simulation results for the pre-intervention group compared with the intervention group. The intervention group has a higher proportion of cost-effective points. The x-axis illustrates incremental QALYs, with positive values indicating interventions more effective. The y-axis represents incremental cost (in CAD), with positive values indicating interventions more costly. The red square represents the mean values, indicating that the intervention was $15,165 less costly and had 0.001 fewer QALYs compared with the pre-intervention.
Fig. 3
Fig. 3
Cost-effectiveness acceptability curve
See this image and copyright information in PMC

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