The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.
Value Health
; 25(3): 359-367, 2022 03.
Article
em En
| MEDLINE
| ID: mdl-35227446
ABSTRACT
OBJECTIVES:
The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given the financial pressure on healthcare budgets, this study assessed whether PC has the potential to be cost-effective compared with standard care, without the use of PC, for Dutch patients in the ICU from a societal perspective.METHODS:
A 1-year, 7-state Markov model reflecting the ICU care pathway and incorporating the PC decision tool was developed. A hypothetical cohort of 1000 adult Dutch patients admitted in the ICU was entered in the model. We used the literature, expert opinion, and data from Amsterdam University Medical Center for model parameters. The uncertainty surrounding the incremental cost-effectiveness ratio was assessed using deterministic and probabilistic sensitivity analyses and scenario analyses.RESULTS:
PC was a cost-effective strategy with an incremental cost-effectiveness ratio of 18 507 per quality-adjusted life-year. PC remained cost-effective over standard care in multiple scenarios and sensitivity analyses. The likelihood that PC will be cost-effective was 71% at a willingness-to-pay threshold of 30 000 per quality-adjusted life-year. The key driver of the results was the parameter "reduction in ICU length of stay."CONCLUSIONS:
We showed that PC has the potential to be cost-effective for Dutch ICUs in a time horizon of 1 year. This study is one of the first cost-effectiveness analyses of a machine learning device. Further research is needed to validate the effectiveness of PC, thereby focusing on the key parameter "reduction in ICU length of stay" and potential spill-over effects.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Alta do Paciente
/
Aprendizado de Máquina
/
Unidades de Terapia Intensiva
Tipo de estudo:
Guideline
/
Health_economic_evaluation
/
Health_technology_assessment
/
Prognostic_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Revista:
Value Health
Assunto da revista:
FARMACOLOGIA
Ano de publicação:
2022
Tipo de documento:
Article