Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning

Abstract In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remainin...

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Autores principales:	Brian L. Hill, Nadav Rakocz, Ákos Rudas, Jeffrey N. Chiang, Sidong Wang, Ira Hofer, Maxime Cannesson, Eran Halperin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/80a96a20abd34c43a3f0742ea23f08d7
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spelling	oai:doaj.org-article:80a96a20abd34c43a3f0742ea23f08d72021-12-02T18:49:16ZImputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning10.1038/s41598-021-94913-y2045-2322https://doaj.org/article/80a96a20abd34c43a3f0742ea23f08d72021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94913-yhttps://doaj.org/toc/2045-2322Abstract In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remaining (high-risk) patients ABP is measured continuously using invasive devices, and derived values are extracted from the recorded waveforms. However, since invasive monitoring is associated with major complications (infection, bleeding, thrombosis), the ideal ABP monitor should be both non-invasive and continuous. With large volumes of high-fidelity physiological waveforms, it may be possible today to impute a physiological waveform from other available signals. Currently, the state-of-the-art approaches for ABP imputation only aim at intermittent systolic and diastolic blood pressure imputation, and there is no method that imputes the continuous ABP waveform. Here, we developed a novel approach to impute the continuous ABP waveform non-invasively using two continuously-monitored waveforms that are currently part of the standard-of-care, the electrocardiogram (ECG) and photo-plethysmogram (PPG), by adapting a deep learning architecture designed for image segmentation. Using over 150,000 min of data collected at two separate health systems from 463 patients, we demonstrate that our model provides a highly accurate prediction of the continuous ABP waveform (root mean square error 5.823 (95% CI 5.806–5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference − 2.497 ± 3.785 mmHg) compared to arterial line measurements. Our approach can potentially be used to measure blood pressure continuously and non-invasively for all patients in the acute care setting, without the need for any additional instrumentation beyond the current standard-of-care.Brian L. HillNadav RakoczÁkos RudasJeffrey N. ChiangSidong WangIra HoferMaxime CannessonEran HalperinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brian L. Hill Nadav Rakocz Ákos Rudas Jeffrey N. Chiang Sidong Wang Ira Hofer Maxime Cannesson Eran Halperin Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
description	Abstract In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but intermittently using a blood pressure cuff. Since even a few minutes of hypotension increases the risk of mortality and morbidity, for the remaining (high-risk) patients ABP is measured continuously using invasive devices, and derived values are extracted from the recorded waveforms. However, since invasive monitoring is associated with major complications (infection, bleeding, thrombosis), the ideal ABP monitor should be both non-invasive and continuous. With large volumes of high-fidelity physiological waveforms, it may be possible today to impute a physiological waveform from other available signals. Currently, the state-of-the-art approaches for ABP imputation only aim at intermittent systolic and diastolic blood pressure imputation, and there is no method that imputes the continuous ABP waveform. Here, we developed a novel approach to impute the continuous ABP waveform non-invasively using two continuously-monitored waveforms that are currently part of the standard-of-care, the electrocardiogram (ECG) and photo-plethysmogram (PPG), by adapting a deep learning architecture designed for image segmentation. Using over 150,000 min of data collected at two separate health systems from 463 patients, we demonstrate that our model provides a highly accurate prediction of the continuous ABP waveform (root mean square error 5.823 (95% CI 5.806–5.840) mmHg), as well as the derived systolic (mean difference 2.398 ± 5.623 mmHg) and diastolic blood pressure (mean difference − 2.497 ± 3.785 mmHg) compared to arterial line measurements. Our approach can potentially be used to measure blood pressure continuously and non-invasively for all patients in the acute care setting, without the need for any additional instrumentation beyond the current standard-of-care.
format	article
author	Brian L. Hill Nadav Rakocz Ákos Rudas Jeffrey N. Chiang Sidong Wang Ira Hofer Maxime Cannesson Eran Halperin
author_facet	Brian L. Hill Nadav Rakocz Ákos Rudas Jeffrey N. Chiang Sidong Wang Ira Hofer Maxime Cannesson Eran Halperin
author_sort	Brian L. Hill
title	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
title_short	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
title_full	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
title_fullStr	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
title_full_unstemmed	Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
title_sort	imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/80a96a20abd34c43a3f0742ea23f08d7
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Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning

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