Cargando…

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

OBJECTIVE: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. MATERIALS AND METHODS: Using electronic m...

Descripción completa

Detalles Bibliográficos
Autores principales: Fitzgerald, Oisin, Perez-Concha, Oscar, Gallego, Blanca, Saxena, Manoj K, Rudd, Lachlan, Metke-Jimenez, Alejandro, Jorm, Louisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324237/
https://www.ncbi.nlm.nih.gov/pubmed/33871017
http://dx.doi.org/10.1093/jamia/ocab060
_version_ 1783731365956550656
author Fitzgerald, Oisin
Perez-Concha, Oscar
Gallego, Blanca
Saxena, Manoj K
Rudd, Lachlan
Metke-Jimenez, Alejandro
Jorm, Louisa
author_facet Fitzgerald, Oisin
Perez-Concha, Oscar
Gallego, Blanca
Saxena, Manoj K
Rudd, Lachlan
Metke-Jimenez, Alejandro
Jorm, Louisa
author_sort Fitzgerald, Oisin
collection PubMed
description OBJECTIVE: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. MATERIALS AND METHODS: Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. RESULTS: Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%. DISCUSSION: ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. CONCLUSION: We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems.
format Online
Article
Text
id pubmed-8324237
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-83242372021-08-02 Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU Fitzgerald, Oisin Perez-Concha, Oscar Gallego, Blanca Saxena, Manoj K Rudd, Lachlan Metke-Jimenez, Alejandro Jorm, Louisa J Am Med Inform Assoc Research and Applications OBJECTIVE: Glycemic control is an important component of critical care. We present a data-driven method for predicting intensive care unit (ICU) patient response to glycemic control protocols while accounting for patient heterogeneity and variations in care. MATERIALS AND METHODS: Using electronic medical records (EMRs) of 18 961 ICU admissions from the MIMIC-III dataset, including 318 574 blood glucose measurements, we train and validate a gradient boosted tree machine learning (ML) algorithm to forecast patient blood glucose and a 95% prediction interval at 2-hour intervals. The model uses as inputs irregular multivariate time series data relating to recent in-patient medical history and glycemic control, including previous blood glucose, nutrition, and insulin dosing. RESULTS: Our forecasting model using routinely collected EMRs achieves performance comparable to previous models developed in planned research studies using continuous blood glucose monitoring. Model error, expressed as mean absolute percentage error is 16.5%–16.8%, with Clarke error grid analysis demonstrating that 97% of predictions would be clinically acceptable. The 95% prediction intervals achieve near intended coverage at 93%–94%. DISCUSSION: ML algorithms built on observational data sources, such as EMRs, present a promising approach for personalization and automation of glycemic control in critical care. Future research may benefit from applying a combination of methodologies and data sources to develop robust methodologies that account for the variations seen in ICU patients and difficultly in detecting the extremes of observed blood glucose values. CONCLUSION: We demonstrate that EMRs can be used to train ML algorithms that may be suitable for incorporation into ICU decision support systems. Oxford University Press 2021-04-19 /pmc/articles/PMC8324237/ /pubmed/33871017 http://dx.doi.org/10.1093/jamia/ocab060 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Fitzgerald, Oisin
Perez-Concha, Oscar
Gallego, Blanca
Saxena, Manoj K
Rudd, Lachlan
Metke-Jimenez, Alejandro
Jorm, Louisa
Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title_full Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title_fullStr Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title_full_unstemmed Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title_short Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU
title_sort incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the icu
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324237/
https://www.ncbi.nlm.nih.gov/pubmed/33871017
http://dx.doi.org/10.1093/jamia/ocab060
work_keys_str_mv AT fitzgeraldoisin incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT perezconchaoscar incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT gallegoblanca incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT saxenamanojk incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT ruddlachlan incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT metkejimenezalejandro incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu
AT jormlouisa incorporatingrealworldevidenceintothedevelopmentofpatientbloodglucosepredictionalgorithmsfortheicu