Effectiveness and cost-effectiveness of a cardiovascular risk prediction algorithm for people with severe mental illness (PRIMROSE)

Abstract

Objectives: To determine the cost-effectiveness of two bespoke severe mental illness (SMI)-specific risk algorithms compared to standard risk algorithms for primary cardiovascular disease (CVD) prevention in those with SMI. Setting: Primary care setting in the United Kingdom (UK). The analysis was from the National Health Service (NHS) perspective. Participants: 1000 individuals with SMI from The Health Improvement Network Database, aged 30-74 years and without existing CVD populated the model. Interventions: Four cardiovascular risk algorithms were assessed; (1) general population lipid, (2) general population BMI, (3) SMI-specific lipid and (4) SMI-specific BMI, compared against no algorithm. At baseline, each cardiovascular risk algorithm was applied and those considered high-risk (>10%) were assumed to be prescribed statin therapy whilst others received usual care. Primary and secondary outcome measures: Quality adjusted life years (QALYs) and costs were accrued for each algorithm including no algorithm, and cost-effectiveness was calculated using the net monetary benefit (NMB) approach. Deterministic and probabilistic sensitivity analyses were performed to test assumptions made and uncertainty around parameter estimates. Results: The SMI-specific BMI algorithm had the highest NMB resulting in 15 additional QALYs and a cost saving of approximately £53,000 per 1,000 patients with SMI over 10 years, followed by the general population lipid algorithm (13 additional QALYs and a cost saving of £46,000). Conclusions: The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of an SMI-specific BMI algorithm (blood tests not required), makes it an attractive algorithm to implement in clinical settings.

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ORCID for Richard Holt: orcid.org/0000-0001-8911-6744

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