Cargando…

The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden

Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. How...

Descripción completa

Detalles Bibliográficos
Autores principales: Ericson, Oskar, Hjelmgren, Jonas, Sjövall, Fredrik, Söderberg, Joakim, Persson, Inger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Columbia Data Analytics, LLC 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042649/
https://www.ncbi.nlm.nih.gov/pubmed/35620451
http://dx.doi.org/10.36469/jheor.2022.33951
_version_ 1784694705866407936
author Ericson, Oskar
Hjelmgren, Jonas
Sjövall, Fredrik
Söderberg, Joakim
Persson, Inger
author_facet Ericson, Oskar
Hjelmgren, Jonas
Sjövall, Fredrik
Söderberg, Joakim
Persson, Inger
author_sort Ericson, Oskar
collection PubMed
description Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown. Objectives: The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting. Methods: A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice. Results: In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year. Conclusions: A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system.
format Online
Article
Text
id pubmed-9042649
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Columbia Data Analytics, LLC
record_format MEDLINE/PubMed
spelling pubmed-90426492022-05-25 The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden Ericson, Oskar Hjelmgren, Jonas Sjövall, Fredrik Söderberg, Joakim Persson, Inger J Health Econ Outcomes Res Methodology and Healthcare Policy Background: Early diagnosis of sepsis has been shown to reduce treatment delays, increase appropriate care, and reduce mortality. The sepsis machine learning algorithm NAVOY® Sepsis, based on variables routinely collected at intensive care units (ICUs), has shown excellent predictive properties. However, the economic consequences of forecasting the onset of sepsis are unknown. Objectives: The potential cost and cost-effectiveness impact of a machine learning algorithm forecasting the onset of sepsis was estimated in an ICU setting. Methods: A health economic model has been developed to capture short-term and long-term consequences of sepsis. The model is based on findings from a randomized, prospective clinical evaluation of NAVOY® Sepsis and from literature sources. Modeling the relationship between time from sepsis onset to treatment and prevalence of septic shock and in-hospital mortality were of particular interest. The model base case assumes that the time to treatment coincides with the time to detection and that the algorithm predicts sepsis 3 hours prior to onset. Total costs include the costs of the prediction algorithm, days spent at the ICU and hospital ward, and long-term consequences. Costs are estimated for an average patient admitted to the ICU and for the healthcare system. The reference method is sepsis diagnosis in accordance with clinical practice. Results: In Sweden, the total cost per patient amounts to €16 436 and €16 512 for the algorithm and current practice arms, respectively, implying a potential cost saving per patient of €76. The largest cost saving is for the ICU stay, which is reduced by 0.16 days per patient (5860 ICU days for the healthcare sector) resulting in a cost saving of €1009 per ICU patient. Stochastic scenario analysis showed that NAVOY® Sepsis was a dominant treatment option in most scenarios and well below an established threshold of €20 000 per quality-adjusted life-year. A 3-hour faster detection implies a reduction in in-hospital mortality, resulting in 356 lives saved per year. Conclusions: A sepsis prediction algorithm such as NAVOY® Sepsis reduces the cost per ICU patient and will potentially have a substantial cost-saving and life-saving impact for ICU departments and the healthcare system. Columbia Data Analytics, LLC 2022-04-26 /pmc/articles/PMC9042649/ /pubmed/35620451 http://dx.doi.org/10.36469/jheor.2022.33951 Text en https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (4.0) (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Methodology and Healthcare Policy
Ericson, Oskar
Hjelmgren, Jonas
Sjövall, Fredrik
Söderberg, Joakim
Persson, Inger
The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title_full The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title_fullStr The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title_full_unstemmed The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title_short The Potential Cost and Cost-Effectiveness Impact of Using a Machine Learning Algorithm for Early Detection of Sepsis in Intensive Care Units in Sweden
title_sort potential cost and cost-effectiveness impact of using a machine learning algorithm for early detection of sepsis in intensive care units in sweden
topic Methodology and Healthcare Policy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9042649/
https://www.ncbi.nlm.nih.gov/pubmed/35620451
http://dx.doi.org/10.36469/jheor.2022.33951
work_keys_str_mv AT ericsonoskar thepotentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT hjelmgrenjonas thepotentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT sjovallfredrik thepotentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT soderbergjoakim thepotentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT perssoninger thepotentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT ericsonoskar potentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT hjelmgrenjonas potentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT sjovallfredrik potentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT soderbergjoakim potentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden
AT perssoninger potentialcostandcosteffectivenessimpactofusingamachinelearningalgorithmforearlydetectionofsepsisinintensivecareunitsinsweden