Cost-effectiveness analysis of whole-genome sequencing during an outbreak of carbapenem-resistant Acinetobacter baumannii

TM Elliott, PN Harris, LW Roberts, M Doidge… - Antimicrobial …, 2021 - cambridge.org
TM Elliott, PN Harris, LW Roberts, M Doidge, T Hurst, K Hajkowicz, B Forde, DL Paterson
Antimicrobial Stewardship & Healthcare Epidemiology, 2021cambridge.org
Background: Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics)
attempts to sequence the entire genetic content straight from the sample. Diagnostic
advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing
organisms. Objective: To evaluate the clinical and economic effects of using WGS and
metagenomics for outbreak management in a large metropolitan hospital. Design: Cost-
effectiveness study. Setting: Intensive care unit and burn unit of large metropolitan hospital …
Background
Whole-genome sequencing (WGS) shotgun metagenomics (metagenomics) attempts to sequence the entire genetic content straight from the sample. Diagnostic advantages lie in the ability to detect unsuspected, uncultivatable, or very slow-growing organisms.
Objective
To evaluate the clinical and economic effects of using WGS and metagenomics for outbreak management in a large metropolitan hospital.
Design
Cost-effectiveness study.
Setting
Intensive care unit and burn unit of large metropolitan hospital.
Patients
Simulated intensive care unit and burn unit patients.
Methods
We built a complex simulation model to estimate pathogen transmission, associated hospital costs, and quality-adjusted life years (QALYs) during a 32-month outbreak of carbapenem-resistant Acinetobacter baumannii (CRAB). Model parameters were determined using microbiology surveillance data, genome sequencing results, hospital admission databases, and local clinical knowledge. The model was calibrated to the actual pathogen spread within the intensive care unit and burn unit (scenario 1) and compared with early use of WGS (scenario 2) and early use of WGS and metagenomics (scenario 3) to determine their respective cost-effectiveness. Sensitivity analyses were performed to address model uncertainty.
Results
On average compared with scenario 1, scenario 2 resulted in 14 fewer patients with CRAB, 59 additional QALYs, and 93,822 in hospital cost savings. The likelihoods that scenario 2 and scenario 3 were cost-effective were 57% and 60%, respectively.
Conclusions
The use of WGS and metagenomics in infection control processes were predicted to produce favorable economic and clinical outcomes.
Cambridge University Press