The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

Autor: de Vos J; Pacmed B.V., Amsterdam, The Netherlands; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands. Electronic address: juliettedvos@gmail.com., Visser LA; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, Rotterdam, The Netherlands., de Beer AA; Pacmed B.V., Amsterdam, The Netherlands., Fornasa M; Pacmed B.V., Amsterdam, The Netherlands., Thoral PJ; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Elbers PWG; Department of Intensive Care Medicine, Laboratory for Critical Care Computational Intelligence, Amsterdam Medical Data Science, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands., Cinà G; Pacmed B.V., Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research [Value Health] 2022 Mar; Vol. 25 (3), pp. 359-367. Date of Electronic Publication: 2021 Oct 22.
DOI: 10.1016/j.jval.2021.06.018
Abstrakt: 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.
(Copyright © 2021 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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