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Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen
BACKGROUND: Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. METHODS: Patients wi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273991/ https://www.ncbi.nlm.nih.gov/pubmed/34253228 http://dx.doi.org/10.1186/s13054-021-03682-7 |
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author | Ma, Penglin Liu, Jingtao Shen, Feng Liao, Xuelian Xiu, Ming Zhao, Heling Zhao, Mingyan Xie, Jing Wang, Peng Huang, Man Li, Tong Duan, Meili Qian, Kejian Peng, Yue Zhou, Feihu Xin, Xin Wan, Xianyao Wang, ZongYu Li, Shusheng Han, Jianwei Li, Zhenliang Ding, Guolei Deng, Qun Zhang, Jicheng Zhu, Yue Ma, Wenjing Wang, Jingwen Kang, Yan Zhang, Zhongheng |
author_facet | Ma, Penglin Liu, Jingtao Shen, Feng Liao, Xuelian Xiu, Ming Zhao, Heling Zhao, Mingyan Xie, Jing Wang, Peng Huang, Man Li, Tong Duan, Meili Qian, Kejian Peng, Yue Zhou, Feihu Xin, Xin Wan, Xianyao Wang, ZongYu Li, Shusheng Han, Jianwei Li, Zhenliang Ding, Guolei Deng, Qun Zhang, Jicheng Zhu, Yue Ma, Wenjing Wang, Jingwen Kang, Yan Zhang, Zhongheng |
author_sort | Ma, Penglin |
collection | PubMed |
description | BACKGROUND: Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. METHODS: Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. RESULTS: A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. CONCLUSIONS: Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-021-03682-7. |
format | Online Article Text |
id | pubmed-8273991 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82739912021-07-13 Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen Ma, Penglin Liu, Jingtao Shen, Feng Liao, Xuelian Xiu, Ming Zhao, Heling Zhao, Mingyan Xie, Jing Wang, Peng Huang, Man Li, Tong Duan, Meili Qian, Kejian Peng, Yue Zhou, Feihu Xin, Xin Wan, Xianyao Wang, ZongYu Li, Shusheng Han, Jianwei Li, Zhenliang Ding, Guolei Deng, Qun Zhang, Jicheng Zhu, Yue Ma, Wenjing Wang, Jingwen Kang, Yan Zhang, Zhongheng Crit Care Research BACKGROUND: Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class. METHODS: Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset. RESULTS: A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion. CONCLUSIONS: Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-021-03682-7. BioMed Central 2021-07-12 /pmc/articles/PMC8273991/ /pubmed/34253228 http://dx.doi.org/10.1186/s13054-021-03682-7 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/) . 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 Ma, Penglin Liu, Jingtao Shen, Feng Liao, Xuelian Xiu, Ming Zhao, Heling Zhao, Mingyan Xie, Jing Wang, Peng Huang, Man Li, Tong Duan, Meili Qian, Kejian Peng, Yue Zhou, Feihu Xin, Xin Wan, Xianyao Wang, ZongYu Li, Shusheng Han, Jianwei Li, Zhenliang Ding, Guolei Deng, Qun Zhang, Jicheng Zhu, Yue Ma, Wenjing Wang, Jingwen Kang, Yan Zhang, Zhongheng Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title | Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title_full | Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title_fullStr | Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title_full_unstemmed | Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title_short | Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
title_sort | individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8273991/ https://www.ncbi.nlm.nih.gov/pubmed/34253228 http://dx.doi.org/10.1186/s13054-021-03682-7 |
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