Cargando…
Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study
BACKGROUND: The aim of this study was to validate whether regional ventilation and perfusion data measured by electrical impedance tomography (EIT) with saline bolus could discriminate three broad acute respiratory failure (ARF) etiologies. METHODS: Perfusion image was generated from EIT-based imped...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401348/ https://www.ncbi.nlm.nih.gov/pubmed/34453622 http://dx.doi.org/10.1186/s13613-021-00921-6 |
_version_ | 1783745529137594368 |
---|---|
author | He, Huaiwu Chi, Yi Long, Yun Yuan, Siyi Zhang, Rui Yang, Yingying Frerichs, Inéz Möller, Knut Fu, Feng Zhao, Zhanqi |
author_facet | He, Huaiwu Chi, Yi Long, Yun Yuan, Siyi Zhang, Rui Yang, Yingying Frerichs, Inéz Möller, Knut Fu, Feng Zhao, Zhanqi |
author_sort | He, Huaiwu |
collection | PubMed |
description | BACKGROUND: The aim of this study was to validate whether regional ventilation and perfusion data measured by electrical impedance tomography (EIT) with saline bolus could discriminate three broad acute respiratory failure (ARF) etiologies. METHODS: Perfusion image was generated from EIT-based impedance–time curves caused by 10 ml 10% NaCl injection during a respiratory hold. Ventilation image was captured before the breath holding period under regular mechanical ventilation. DeadSpace(%), Shunt(%) and VQMatch(%) were calculated based on lung perfusion and ventilation images. Ventilation and perfusion maps were divided into four cross-quadrants (lower left and right, upper left and right). Regional distribution defects of each quadrant were scored as 0 (distribution% ≥ 15%), 1 (15% > distribution% ≥ 10%) and 2 (distribution% < 10%). Data percentile distributions in the control group and clinical simplicity were taken into consideration when defining the scores. Overall defect scores (Defect(V), Defect(Q) and Defect(V+Q)) were the sum of four cross-quadrants of the corresponding images. RESULTS: A total of 108 ICU patients were prospectively included: 93 with ARF and 15 without as a control. PaO(2)/FiO(2) was significantly correlated with VQMatch(%) (r = 0.324, P = 0.001). Three broad etiologies of ARF were identified based on clinical judgment: pulmonary embolism-related disease (PED, n = 14); diffuse lung involvement disease (DLD, n = 21) and focal lung involvement disease (FLD, n = 58). The PED group had a significantly higher DeadSpace(%) [40(24)% vs. 14(15)%, PED group vs. the rest of the subjects; median(interquartile range); P < 0.0001] and Defect(Q) score than the other groups [1(1) vs. 0(1), PED vs. the rest; P < 0.0001]. The DLD group had a significantly lower Defect(V+Q) score than the PED and FLD groups [0(1) vs. 2.5(2) vs. 3(3), DLD vs. PED vs. FLD; P < 0.0001]. The FLD group had a significantly higher Defect(V) score than the other groups [2(2) vs. 0(1), FLD vs. the rest; P < 0.0001]. The area under the receiver operating characteristic (AUC) for using DeadSpace(%) to identify PED was 0.894 in all ARF patients. The AUC for using the Defect(V+Q) score to identify DLD was 0.893. The AUC for using the Defect(V) score to identify FLD was 0.832. CONCLUSIONS: Our study showed that it was feasible to characterize three broad etiologies of ARF with EIT-based regional ventilation and perfusion. Further study is required to validate clinical applicability of this method. Trial registration clinicaltrials, NCT04081142. Registered 9 September 2019—retrospectively registered, https://clinicaltrials.gov/show/NCT04081142. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13613-021-00921-6. |
format | Online Article Text |
id | pubmed-8401348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84013482021-08-30 Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study He, Huaiwu Chi, Yi Long, Yun Yuan, Siyi Zhang, Rui Yang, Yingying Frerichs, Inéz Möller, Knut Fu, Feng Zhao, Zhanqi Ann Intensive Care Research BACKGROUND: The aim of this study was to validate whether regional ventilation and perfusion data measured by electrical impedance tomography (EIT) with saline bolus could discriminate three broad acute respiratory failure (ARF) etiologies. METHODS: Perfusion image was generated from EIT-based impedance–time curves caused by 10 ml 10% NaCl injection during a respiratory hold. Ventilation image was captured before the breath holding period under regular mechanical ventilation. DeadSpace(%), Shunt(%) and VQMatch(%) were calculated based on lung perfusion and ventilation images. Ventilation and perfusion maps were divided into four cross-quadrants (lower left and right, upper left and right). Regional distribution defects of each quadrant were scored as 0 (distribution% ≥ 15%), 1 (15% > distribution% ≥ 10%) and 2 (distribution% < 10%). Data percentile distributions in the control group and clinical simplicity were taken into consideration when defining the scores. Overall defect scores (Defect(V), Defect(Q) and Defect(V+Q)) were the sum of four cross-quadrants of the corresponding images. RESULTS: A total of 108 ICU patients were prospectively included: 93 with ARF and 15 without as a control. PaO(2)/FiO(2) was significantly correlated with VQMatch(%) (r = 0.324, P = 0.001). Three broad etiologies of ARF were identified based on clinical judgment: pulmonary embolism-related disease (PED, n = 14); diffuse lung involvement disease (DLD, n = 21) and focal lung involvement disease (FLD, n = 58). The PED group had a significantly higher DeadSpace(%) [40(24)% vs. 14(15)%, PED group vs. the rest of the subjects; median(interquartile range); P < 0.0001] and Defect(Q) score than the other groups [1(1) vs. 0(1), PED vs. the rest; P < 0.0001]. The DLD group had a significantly lower Defect(V+Q) score than the PED and FLD groups [0(1) vs. 2.5(2) vs. 3(3), DLD vs. PED vs. FLD; P < 0.0001]. The FLD group had a significantly higher Defect(V) score than the other groups [2(2) vs. 0(1), FLD vs. the rest; P < 0.0001]. The area under the receiver operating characteristic (AUC) for using DeadSpace(%) to identify PED was 0.894 in all ARF patients. The AUC for using the Defect(V+Q) score to identify DLD was 0.893. The AUC for using the Defect(V) score to identify FLD was 0.832. CONCLUSIONS: Our study showed that it was feasible to characterize three broad etiologies of ARF with EIT-based regional ventilation and perfusion. Further study is required to validate clinical applicability of this method. Trial registration clinicaltrials, NCT04081142. Registered 9 September 2019—retrospectively registered, https://clinicaltrials.gov/show/NCT04081142. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13613-021-00921-6. Springer International Publishing 2021-08-28 /pmc/articles/PMC8401348/ /pubmed/34453622 http://dx.doi.org/10.1186/s13613-021-00921-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research He, Huaiwu Chi, Yi Long, Yun Yuan, Siyi Zhang, Rui Yang, Yingying Frerichs, Inéz Möller, Knut Fu, Feng Zhao, Zhanqi Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title | Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title_full | Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title_fullStr | Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title_full_unstemmed | Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title_short | Three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
title_sort | three broad classifications of acute respiratory failure etiologies based on regional ventilation and perfusion by electrical impedance tomography: a hypothesis-generating study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401348/ https://www.ncbi.nlm.nih.gov/pubmed/34453622 http://dx.doi.org/10.1186/s13613-021-00921-6 |
work_keys_str_mv | AT hehuaiwu threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT chiyi threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT longyun threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT yuansiyi threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT zhangrui threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT yangyingying threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT frerichsinez threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT mollerknut threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT fufeng threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy AT zhaozhanqi threebroadclassificationsofacuterespiratoryfailureetiologiesbasedonregionalventilationandperfusionbyelectricalimpedancetomographyahypothesisgeneratingstudy |