An Early-Stage Decision-Analytic Health Economic Model of Above Cuff Vocalization: What Do We Know and What Do We Need to Resolve?

Objectives:

Above cuff vocalization (ACV) is used in patients with a tracheostomy in the ICU despite limited evidence. This early-stage decision-analytic model (DAM) for ACV evaluates the expected cost-effectiveness exploring the impact of uncertainty to identify key drivers of cost and effect and critical further research priorities.

Perspective:

U.K. National Health Service.

Setting:

Hypothetical cohort of general ICU patients with a tracheostomy, 63 years old, 64% male.

Methods:

A de novo decision-analytic health economic model comparing ACV to usual care (UC). Model parameters were acquired from the literature review and expert opinion. One-way sensitivity analyses were conducted to identify key drivers of cost-effectiveness.

Results:

The daily cost of ACV in the ICU ranged from £75 to 89 (USD 101–120), with most of this cost attributable to staff resources for delivery. The base-case scenario revealed ACV is potentially cost-effective, dominating UC with cost savings of £9,488 (USD 12,808) and 0.395 Quality-Adjusted Life Years gained. Most sensitivity analyses revealed that ACV dominated UC, costing less and being more effective. When ACV had a negative impact on ICU and ward length of stay (LoS), or had no effect on the speed of weaning, it was not cost-effective. The primary driver of cost was whether ACV affected the speed of weaning and ICU LoS. The two primary drivers of effect were: i) whether ACV impacted which end state a patient transitioned to and ii) whether ACV had a sustained positive impact on quality of life.

Conclusions:

Despite the substantial input required from speech-language pathologists—a typically scarce resource in ICU settings—ACV demonstrates strong potential for cost-effectiveness. There is no reason for decision-makers to de-adopt ACV, and delaying adoption may result in loss of opportunity costs. Improved reporting of mortality and utility data in critical care research would increase the reliability of early-stage DAMs.