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

Published online by Cambridge University Press:  23 March 2020

E. Zomer
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
D. Osborn
Affiliation:
University College London, Division of Psychiatry, London, United Kingdom National Health Service, Camden and Islington National Health Service Foundation Trust, London, United Kingdom
I. Nazareth
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
R. Blackburn
Affiliation:
University College London, Division of Psychiatry, London, United Kingdom
A. Burton
Affiliation:
University College London, Division of Psychiatry, London, United Kingdom
S. Hardoon
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
R.I.G. Holt
Affiliation:
University of Southampton, Human Development and Health Academic Unit, Southampton, United Kingdom
M. King
Affiliation:
University College London, Division of Psychiatry, London, United Kingdom
L. Marston
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
S. Morris
Affiliation:
University College London, Department of Applied Health Research, London, United Kingdom
R. Omar
Affiliation:
University College London, Department of Statistical Science, London, United Kingdom
I. Petersen
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
K. Walters
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
R.M. Hunter
Affiliation:
University College London, Department of Primary Care and Population Health, London, United Kingdom
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Abstract

Introduction

Cardiovascular risk prediction tools are important for cardiovascular disease (CVD) prevention, however, which algorithms are appropriate for people with severe mental illness (SMI) is unclear.

Objectives/aims

To determine the cost-effectiveness using the net monetary benefit (NMB) approach of two bespoke SMI-specific risk algorithms compared to standard risk algorithms for primary CVD prevention in those with SMI, from an NHS perspective.

Methods

A microsimulation model was populated with 1000 individuals with SMI from The Health Improvement Network Database, aged 30–74 years without CVD. 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 high-risk (> 10%) were assumed to be prescribed statin therapy, others received usual care. Individuals entered the model in a ‘healthy’ free of CVD health state and with each year could retain their current health state, have cardiovascular events (non-fatal/fatal) or die from other causes according to transition probabilities.

Results

The SMI-specific BMI and general population lipid algorithms had the highest NMB of the four algorithms resulting in 12 additional QALYs and a cost saving of approximately £37,000 (US$ 58,000) per 1000 patients with SMI over 10 years.

Conclusions

The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of a SMI-specific BMI algorithm (blood tests not required) makes it an attractive algorithm to implement in clinical settings.

Disclosure of interest

The authors have not supplied their declaration of competing interest.

Type
EW315
Copyright
Copyright © European Psychiatric Association 2016

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