Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.
J Med Econ
; 23(4): 386-393, 2020 Apr.
Article
en En
| MEDLINE
| ID: mdl-31855091
ABSTRACT
Aims:
As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.Methods:
Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective.Results:
Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.Limitations:
The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.Conclusions:
Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Fibrilación Atrial
/
Tamizaje Masivo
/
Medición de Riesgo
/
Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Humans
País/Región como asunto:
Europa
Idioma:
En
Revista:
J Med Econ
Asunto de la revista:
SERVICOS DE SAUDE
Año:
2020
Tipo del documento:
Article
País de afiliación:
Reino Unido