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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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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. |
format | Online Article Text |
id | pubmed-7698368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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