COST-EFFECTIVENESS OF CEFTRIAXONE-SULBACTAM IN TREATMENT OF NOSOCOMIAL INFECTIONS USING PROBABILISTIC SENSITIVITY ANALYSIS

Vishnu Dutt Sharma, Manu Chaudhary and Manish Taneja*

ABSTRACT

Increasing prevalence of carbapenem resistance in Gram negative bacteria due to excessive and indiscriminate use of carbapenems has forced the medical fraternity to look for better alternatives. One promising solution is to use pharmacodynamically synergistic drugs combination against the resistant pathogen. Recently, a fixed dose combination of ceftriaxone/sulbactam (2/1) (marketed as Elores®) has shown promising response against antibacterial resistance. However, the economic evaluation of Elores in comparison with carbapenem class of drug was not done so far. Therefore, the objective of the present study was to evaluate the cost-effectiveness of Elores and meropenem against hospital acquired infections (HAIs). A retrospective study on patients receiving either Elores or meropenem with/without colistin in management of HAIs was utilized for the cost effective analysis (CEA) study using ‘decision table” as an analytical model. Cost of therapy evaluation included both direct and indirect costs. Effectiveness measures were estimated from drug’s efficacy, adherence tendencies and tolerability in the model. The cost effectiveness ratio (CER) were computed, where Elores treatment was more cost-effective than meropenem treated approach with CER of INR 1132 (USD $ 17.4) per unit of effectiveness measures. The probabilistic sensitivity analysis was then performed to improve the model predictions, and to reduce the uncertainty in the model parameters. In base-case analysis, Elores was superior with an incremental-CER (ICER) of INR 27 at willingness-to-pay (WTP) of INR 200. The model was robust to variations in model input parameters. The study advocated Elores as a cost-effective use of resources and as a carbapenem sparer drug in the management of HAIs.

Keywords: Cost Effectiveness Analysis, Antibacterial Therapy, decision tree analysis, probabilistic sensitivity analysis.