The costs and health outcomes associated with a personalised antibiotic combination testing service for carbapenem-resistant Pseudomonas aeruginosa: A cost-effectiveness analysis.

OBJECTIVE

In vitro personalised antibiotic combination testing service (iACT) is a prospective antibiotic combination testing service for the individualised selection of antibiotic combinations against difficult-to-treat infections in Singapore. We assessed the cost-effectiveness of a decision to adopt iACT for carbapenem-resistant P. aeruginosa (CRPA) infections.

METHODS

We fitted a decision tree to estimate changes in costs (2025 SGD) and health outcomes from the decision to adopt iACT from the hospital perspective, compared with current practice. Input parameters were derived from the published literature, hospital databases and expert opinion. A probabilistic sensitivity analysis was performed. To describe how uncertainties in mortality risks affect cost-effectiveness, we also conducted a one-way sensitivity analysis, for which we varied the odds ratio for mortality in those receiving iACT-recommended regimens, in a deterministic manner. A value-of-information analysis was performed.

RESULTS

At a willingness-to-pay threshold of $45 000 per quality-adjusted life-year, the incremental cost-effectiveness ratio was $3 493. There was an 82.5% probability that the adoption of iACT is cost-effective. In the sensitivity analysis, the probability that iACT was cost-effective decreased as the odds ratio for mortality for iACT-recommended regimens increased. When the odds ratio was 0.99, simulating almost no mortality benefit, the likelihood that iACT is cost-effective was 54%. The value-of-information analysis showed that the parameter groups contributing to decision uncertainties were odd ratios for mortality and utilities.

CONCLUSIONS

Adoption of iACT for CRPA infections in Singapore is likely to be cost-effective. Robust trials to reveal mortality benefits of iACT, and quality-of-life studies in CRPA-infected patients, will be useful in decreasing uncertainties surrounding the decision to adopt.