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Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study

BACKGROUND: Although lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (B...

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Autores principales: Tseng, How-Yang, Chen, Chieh-Lung, Lin, Yu-Chao, Chuang, Ming-Che, Hsu, Wu-Huei, Hsiao, Wan-Yun, Chen, Tung-Mei, Wang, Min-Tzu, Huang, Wei-Chun, Chen, Chih-Yu, Wu, Biing-Ru, Tu, Chih-Yen, Liang, Shinn-Jye, Chen, Wei-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395891/
https://www.ncbi.nlm.nih.gov/pubmed/35996117
http://dx.doi.org/10.1186/s13054-022-04091-0
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author Tseng, How-Yang
Chen, Chieh-Lung
Lin, Yu-Chao
Chuang, Ming-Che
Hsu, Wu-Huei
Hsiao, Wan-Yun
Chen, Tung-Mei
Wang, Min-Tzu
Huang, Wei-Chun
Chen, Chih-Yu
Wu, Biing-Ru
Tu, Chih-Yen
Liang, Shinn-Jye
Chen, Wei-Cheng
author_facet Tseng, How-Yang
Chen, Chieh-Lung
Lin, Yu-Chao
Chuang, Ming-Che
Hsu, Wu-Huei
Hsiao, Wan-Yun
Chen, Tung-Mei
Wang, Min-Tzu
Huang, Wei-Chun
Chen, Chih-Yu
Wu, Biing-Ru
Tu, Chih-Yen
Liang, Shinn-Jye
Chen, Wei-Cheng
author_sort Tseng, How-Yang
collection PubMed
description BACKGROUND: Although lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (BI) for real-time data visualization is associated with an improvement in lung protective strategy and adjunctive therapy. METHODS: A retrospective observational cohort study was conducted on patients with ARDS admitted between September 2020 and June 2021 at two intensive care units (ICUs) of a tertiary referral hospital in Taiwan. BI was imported for data visualization and integration to assist in clinical decision in one of the ICUs. The primary outcomes were the implementation of low tidal volume ventilation (defined as tidal volume/predicted body weight ≤ 8 mL/kg) within 24 h from ARDS onset. The secondary outcomes included ICU and hospital mortality rates. RESULTS: Among the 1201 patients admitted to the ICUs during the study period, 148 (12.3%) fulfilled the ARDS criteria, with 86 patients in the BI-assisted group and 62 patients in the standard-of-care (SOC) group. Disease severity was similar between the two groups. The application of low tidal volume ventilation strategy was significantly improved in the BI-assisted group compared with that in the SOC group (79.1% vs. 61.3%, p = 0.018). Despite their ARDS and disease severity, the BI-assisted group tended to achieve low tidal volume ventilation. The ICU and hospital mortality were lower in the BI-assisted group. CONCLUSIONS: The use of real-time visualization system for data-driven decision support was associated with significantly improved compliance to low tidal volume ventilation strategy, which enhanced the outcomes of patients with ARDS in the ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04091-0.
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spelling pubmed-93958912022-08-23 Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study Tseng, How-Yang Chen, Chieh-Lung Lin, Yu-Chao Chuang, Ming-Che Hsu, Wu-Huei Hsiao, Wan-Yun Chen, Tung-Mei Wang, Min-Tzu Huang, Wei-Chun Chen, Chih-Yu Wu, Biing-Ru Tu, Chih-Yen Liang, Shinn-Jye Chen, Wei-Cheng Crit Care Research BACKGROUND: Although lung protective strategy and adjunctive intervention are associated with improved survival in patients with acute respiratory distress syndrome (ARDS), the implementation of effective therapies remains low. This study aimed to evaluate whether the use of business intelligence (BI) for real-time data visualization is associated with an improvement in lung protective strategy and adjunctive therapy. METHODS: A retrospective observational cohort study was conducted on patients with ARDS admitted between September 2020 and June 2021 at two intensive care units (ICUs) of a tertiary referral hospital in Taiwan. BI was imported for data visualization and integration to assist in clinical decision in one of the ICUs. The primary outcomes were the implementation of low tidal volume ventilation (defined as tidal volume/predicted body weight ≤ 8 mL/kg) within 24 h from ARDS onset. The secondary outcomes included ICU and hospital mortality rates. RESULTS: Among the 1201 patients admitted to the ICUs during the study period, 148 (12.3%) fulfilled the ARDS criteria, with 86 patients in the BI-assisted group and 62 patients in the standard-of-care (SOC) group. Disease severity was similar between the two groups. The application of low tidal volume ventilation strategy was significantly improved in the BI-assisted group compared with that in the SOC group (79.1% vs. 61.3%, p = 0.018). Despite their ARDS and disease severity, the BI-assisted group tended to achieve low tidal volume ventilation. The ICU and hospital mortality were lower in the BI-assisted group. CONCLUSIONS: The use of real-time visualization system for data-driven decision support was associated with significantly improved compliance to low tidal volume ventilation strategy, which enhanced the outcomes of patients with ARDS in the ICU. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04091-0. BioMed Central 2022-08-22 /pmc/articles/PMC9395891/ /pubmed/35996117 http://dx.doi.org/10.1186/s13054-022-04091-0 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Tseng, How-Yang
Chen, Chieh-Lung
Lin, Yu-Chao
Chuang, Ming-Che
Hsu, Wu-Huei
Hsiao, Wan-Yun
Chen, Tung-Mei
Wang, Min-Tzu
Huang, Wei-Chun
Chen, Chih-Yu
Wu, Biing-Ru
Tu, Chih-Yen
Liang, Shinn-Jye
Chen, Wei-Cheng
Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title_full Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title_fullStr Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title_full_unstemmed Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title_short Using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
title_sort using real-time visualization system for data-driven decision support to achieve lung protective strategy: a retrospective observational study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9395891/
https://www.ncbi.nlm.nih.gov/pubmed/35996117
http://dx.doi.org/10.1186/s13054-022-04091-0
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