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Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs

Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously m...

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Autores principales: Chen, Yang, Hong, Chengcheng, Pinsky, Michael R., Ma, Ting, Clermont, Gilles
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698368/
https://www.ncbi.nlm.nih.gov/pubmed/33212858
http://dx.doi.org/10.3390/s20226558
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author Chen, Yang
Hong, Chengcheng
Pinsky, Michael R.
Ma, Ting
Clermont, Gilles
author_facet Chen, Yang
Hong, Chengcheng
Pinsky, Michael R.
Ma, Ting
Clermont, Gilles
author_sort Chen, Yang
collection PubMed
description Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV.
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spelling pubmed-76983682020-11-29 Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs Chen, Yang Hong, Chengcheng Pinsky, Michael R. Ma, Ting Clermont, Gilles Sensors (Basel) Article Background: There are currently no effective and accurate blood loss volume (BLV) estimation methods that can be implemented in operating rooms. To improve the accuracy and reliability of BLV estimation and facilitate clinical implementation, we propose a novel estimation method using continuously monitored photoplethysmography (PPG) and invasive arterial blood pressure (ABP). Methods: Forty anesthetized York Pigs (31.82 ± 3.52 kg) underwent a controlled hemorrhage at 20 mL/min until shock development was included. Machine-learning-based BLV estimation models were proposed and tested on normalized features derived by vital signs. Results: The results showed that the mean ± standard deviation (SD) for estimating BLV against the reference BLV of our proposed random-forest-derived BLV estimation models using PPG and ABP features, as well as the combination of ABP and PPG features, were 11.9 ± 156.2, 6.5 ± 161.5, and 7.0 ± 139.4 mL, respectively. Compared with traditional hematocrit computation formulas (estimation error: 102.1 ± 313.5 mL), our proposed models outperformed by nearly 200 mL in SD. Conclusion: This is the first attempt at predicting quantitative BLV from noninvasive measurements. Normalized PPG features are superior to ABP in accurately estimating early-stage BLV, and normalized invasive ABP features could enhance model performance in the event of a massive BLV. MDPI 2020-11-17 /pmc/articles/PMC7698368/ /pubmed/33212858 http://dx.doi.org/10.3390/s20226558 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Yang
Hong, Chengcheng
Pinsky, Michael R.
Ma, Ting
Clermont, Gilles
Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title_full Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title_fullStr Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title_full_unstemmed Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title_short Estimating Surgical Blood Loss Volume Using Continuously Monitored Vital Signs
title_sort estimating surgical blood loss volume using continuously monitored vital signs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7698368/
https://www.ncbi.nlm.nih.gov/pubmed/33212858
http://dx.doi.org/10.3390/s20226558
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