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External validation of a machine learning model to predict hemodynamic instability in intensive care unit

BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a multi-parameter machine learning model, in predicting...

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Autores principales: Dung-Hung, Chiang, Cong, Tian, Zeyu, Jiang, Yu-Shan, Ou-Yang, Yung-Yan, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281065/
https://www.ncbi.nlm.nih.gov/pubmed/35836294
http://dx.doi.org/10.1186/s13054-022-04088-9
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author Dung-Hung, Chiang
Cong, Tian
Zeyu, Jiang
Yu-Shan, Ou-Yang
Yung-Yan, Lin
author_facet Dung-Hung, Chiang
Cong, Tian
Zeyu, Jiang
Yu-Shan, Ou-Yang
Yung-Yan, Lin
author_sort Dung-Hung, Chiang
collection PubMed
description BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a multi-parameter machine learning model, in predicting hemodynamic instability in Asian patients. METHOD: Hemodynamic instability was marked by using inotropic, vasopressor, significant fluid therapy, and/or blood transfusions. This retrospective study included among 15,967 ICU patients who aged 20 years or older (not included 20 years) and stayed in ICU for more than 6 h admitted to Taipei Veteran General Hospital (TPEVGH) between January 1, 2010, and March 31, 2020, of whom hemodynamic instability occurred in 3053 patients (prevalence = 19%). These patients in unstable group received at least one intervention during their ICU stays, and the HSI score of both stable and unstable group was calculated in every hour before intervention. The model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and was compared to single indicators like systolic blood pressure (SBP) and shock index. The hemodynamic instability alarm was set by selecting optimal threshold with high sensitivity, acceptable specificity, and lead time before intervention was calculated to indicate when patients were firstly identified as high risk of hemodynamic instability. RESULTS: The AUROC of HSI was 0.76 (95% CI, 0.75–0.77), which performed significantly better than shock Index (0.7; 95% CI, 0.69–0.71) and SBP (0.69; 95% CI, 0.68–0.70). By selecting 0.7 as a threshold, HSI predicted 72% of all 3053 patients who received hemodynamic interventions with 67% in specificity. Time-varying results also showed that HSI score significantly outperformed single indicators even up to 24 h before intervention. And 95% unstable patients can be identified more than 5 h in advance. CONCLUSIONS: The HSI has acceptable discrimination but underestimates the risk of stable patients in predicting the onset of hemodynamic instability in an external cohort. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04088-9.
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spelling pubmed-92810652022-07-15 External validation of a machine learning model to predict hemodynamic instability in intensive care unit Dung-Hung, Chiang Cong, Tian Zeyu, Jiang Yu-Shan, Ou-Yang Yung-Yan, Lin Crit Care Research BACKGROUND: Early prediction model of hemodynamic instability has the potential to improve the critical care, whereas limited external validation on the generalizability. We aimed to independently validate the Hemodynamic Stability Index (HSI), a multi-parameter machine learning model, in predicting hemodynamic instability in Asian patients. METHOD: Hemodynamic instability was marked by using inotropic, vasopressor, significant fluid therapy, and/or blood transfusions. This retrospective study included among 15,967 ICU patients who aged 20 years or older (not included 20 years) and stayed in ICU for more than 6 h admitted to Taipei Veteran General Hospital (TPEVGH) between January 1, 2010, and March 31, 2020, of whom hemodynamic instability occurred in 3053 patients (prevalence = 19%). These patients in unstable group received at least one intervention during their ICU stays, and the HSI score of both stable and unstable group was calculated in every hour before intervention. The model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and was compared to single indicators like systolic blood pressure (SBP) and shock index. The hemodynamic instability alarm was set by selecting optimal threshold with high sensitivity, acceptable specificity, and lead time before intervention was calculated to indicate when patients were firstly identified as high risk of hemodynamic instability. RESULTS: The AUROC of HSI was 0.76 (95% CI, 0.75–0.77), which performed significantly better than shock Index (0.7; 95% CI, 0.69–0.71) and SBP (0.69; 95% CI, 0.68–0.70). By selecting 0.7 as a threshold, HSI predicted 72% of all 3053 patients who received hemodynamic interventions with 67% in specificity. Time-varying results also showed that HSI score significantly outperformed single indicators even up to 24 h before intervention. And 95% unstable patients can be identified more than 5 h in advance. CONCLUSIONS: The HSI has acceptable discrimination but underestimates the risk of stable patients in predicting the onset of hemodynamic instability in an external cohort. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04088-9. BioMed Central 2022-07-14 /pmc/articles/PMC9281065/ /pubmed/35836294 http://dx.doi.org/10.1186/s13054-022-04088-9 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
Dung-Hung, Chiang
Cong, Tian
Zeyu, Jiang
Yu-Shan, Ou-Yang
Yung-Yan, Lin
External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title_full External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title_fullStr External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title_full_unstemmed External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title_short External validation of a machine learning model to predict hemodynamic instability in intensive care unit
title_sort external validation of a machine learning model to predict hemodynamic instability in intensive care unit
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281065/
https://www.ncbi.nlm.nih.gov/pubmed/35836294
http://dx.doi.org/10.1186/s13054-022-04088-9
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