A Monte Carlo simulation estimating US hospital cost reductions associated with hypotension control in septic ICU patients

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Abstract

Objective: This economic analysis extends upon a recent epidemiological study to estimate the association between hypotension control and hospital costs for septic patients in US intensive care units (ICUs). Methods: A Monte Carlo simulation decision analytic model was developed that accounted for the probability of complications—acute kidney injury and mortality—in septic ICU patients and the cost of each health outcome from the hospital perspective. Probabilities of complications were calculated based on observational data from 110 US hospitals for septic ICU patients (n = 8,782) with various levels of hypotension exposure as measured by mean arterial pressure (MAP, units: mmHg). Costs for acute kidney injury (AKI) and mortality were derived from published literature. Each simulation calculated mean hospital cost reduction and 95% confidence intervals based on 10,000 trials. Results: In the base-case analysis hospital costs for a hypothetical “control” cohort (MAP of 65 mmHg) were $699 less per hospitalization (95% CI: $342–$1,116) relative to a “case” cohort (MAP of 60 mmHg). In the most extreme case considered (45 mmHg vs 65 mmHg), the associated cost reduction was $4,450 (95% CI: $2,020–$7,581). More than 99% of the simulated trials resulted in cost reductions. A conservative institution-level analysis for a hypothetical hospital (which assumes no benefit for increasing MAP above 65 mmHg) estimated a cost decline of $417 for a 5 mmHg increase in MAP per ICU septic patient. These results are applicable to the US only. Conclusions: Hypotension control (via MAP increases) for patients with sepsis in the ICU is associated with lower hospitalization cost.

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Keuffel, E. L., Stevens, M., Gunnarsson, C., Rizzo, J., Sessler, D. I., & Maheshwari, K. (2019). A Monte Carlo simulation estimating US hospital cost reductions associated with hypotension control in septic ICU patients. Journal of Medical Economics, 22(4), 383–389. https://doi.org/10.1080/13696998.2019.1576695

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