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Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database

BACKGROUND: Septic shock patients fundamentally require delicate vasoactive and inotropic agent administration, which could be quantitatively and objectively evaluated by the vasoactive–inotropic score (VIS); however, whether the dynamic trends of high-time-resolution VIS alter the clinical outcomes...

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Autores principales: Ning, Yi-Le, Sun, Ce, Xu, Xiang-Hui, Li, Li, Ke, Yan-Ji, Mai, Ye, Lin, Xin-Feng, Yang, Zhong-Qi, Xian, Shao-Xiang, Chen, Wei-Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112491/
https://www.ncbi.nlm.nih.gov/pubmed/37082452
http://dx.doi.org/10.3389/fcvm.2023.1126888
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author Ning, Yi-Le
Sun, Ce
Xu, Xiang-Hui
Li, Li
Ke, Yan-Ji
Mai, Ye
Lin, Xin-Feng
Yang, Zhong-Qi
Xian, Shao-Xiang
Chen, Wei-Tao
author_facet Ning, Yi-Le
Sun, Ce
Xu, Xiang-Hui
Li, Li
Ke, Yan-Ji
Mai, Ye
Lin, Xin-Feng
Yang, Zhong-Qi
Xian, Shao-Xiang
Chen, Wei-Tao
author_sort Ning, Yi-Le
collection PubMed
description BACKGROUND: Septic shock patients fundamentally require delicate vasoactive and inotropic agent administration, which could be quantitatively and objectively evaluated by the vasoactive–inotropic score (VIS); however, whether the dynamic trends of high-time-resolution VIS alter the clinical outcomes remains unclear. Thus, this study proposes the term VIS Reduction Rate (VRR) to generalise the tendency of dynamic VIS, to explore the association of VRR and mortality for patients with septic shock. METHODS: We applied dynamic and static VIS data to predict ICU mortality by two models: the long short-term memory (LSTM) deep learning model, and the extreme gradient boosting (XGBoost), respectively. The specific target cohort was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database by the sophisticated structured query language (SQL). Enrolled patients were divided into four groups by VRR value: ≥50%, 0 ~ 50%, −50% ~ 0, and < −50%. Statistical approaches included pairwise propensity score matching (PSM), Cox proportional hazards regression, and two doubly robust estimation models to ensure the robustness of the results. The primary and secondary outcomes were ICU mortality and in-hospital mortality, respectively. RESULTS: VRR simplifies the dosing trends of vasoactive and inotropic agents represented by dynamic VIS data while requiring fewer data. In total, 8,887 septic shock patients were included. Compared with the VRR ≥50% group, the 0 ~ 50%, −50% ~ 0, and < −50% groups had significantly higher ICU mortality [hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.17–1.50, p < 0.001; HR 1.79, 95% CI 1.44–2.22, p < 0.001; HR 2.07, 95% CI 1.61–2.66, p < 0.001, respectively] and in-hospital mortality [HR 1.43, 95% CI 1.28–1.60, p < 0.001; HR 1.75, 95% CI 1.45–2.11, p < 0.001; HR 2.00, 95% CI 1.61–2.49, p < 0.001, respectively]. Similar findings were observed in two doubly robust estimation models. CONCLUSION: The trends of dynamic VIS in ICU might help intensivists to stratify the prognosis of adult patients with septic shock. A lower decline of VIS was remarkably associated with higher ICU and in-hospital mortality among septic shock patients receiving vasoactive–inotropic therapy for more than 24 h.
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spelling pubmed-101124912023-04-19 Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database Ning, Yi-Le Sun, Ce Xu, Xiang-Hui Li, Li Ke, Yan-Ji Mai, Ye Lin, Xin-Feng Yang, Zhong-Qi Xian, Shao-Xiang Chen, Wei-Tao Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Septic shock patients fundamentally require delicate vasoactive and inotropic agent administration, which could be quantitatively and objectively evaluated by the vasoactive–inotropic score (VIS); however, whether the dynamic trends of high-time-resolution VIS alter the clinical outcomes remains unclear. Thus, this study proposes the term VIS Reduction Rate (VRR) to generalise the tendency of dynamic VIS, to explore the association of VRR and mortality for patients with septic shock. METHODS: We applied dynamic and static VIS data to predict ICU mortality by two models: the long short-term memory (LSTM) deep learning model, and the extreme gradient boosting (XGBoost), respectively. The specific target cohort was extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database by the sophisticated structured query language (SQL). Enrolled patients were divided into four groups by VRR value: ≥50%, 0 ~ 50%, −50% ~ 0, and < −50%. Statistical approaches included pairwise propensity score matching (PSM), Cox proportional hazards regression, and two doubly robust estimation models to ensure the robustness of the results. The primary and secondary outcomes were ICU mortality and in-hospital mortality, respectively. RESULTS: VRR simplifies the dosing trends of vasoactive and inotropic agents represented by dynamic VIS data while requiring fewer data. In total, 8,887 septic shock patients were included. Compared with the VRR ≥50% group, the 0 ~ 50%, −50% ~ 0, and < −50% groups had significantly higher ICU mortality [hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.17–1.50, p < 0.001; HR 1.79, 95% CI 1.44–2.22, p < 0.001; HR 2.07, 95% CI 1.61–2.66, p < 0.001, respectively] and in-hospital mortality [HR 1.43, 95% CI 1.28–1.60, p < 0.001; HR 1.75, 95% CI 1.45–2.11, p < 0.001; HR 2.00, 95% CI 1.61–2.49, p < 0.001, respectively]. Similar findings were observed in two doubly robust estimation models. CONCLUSION: The trends of dynamic VIS in ICU might help intensivists to stratify the prognosis of adult patients with septic shock. A lower decline of VIS was remarkably associated with higher ICU and in-hospital mortality among septic shock patients receiving vasoactive–inotropic therapy for more than 24 h. Frontiers Media S.A. 2023-03-07 /pmc/articles/PMC10112491/ /pubmed/37082452 http://dx.doi.org/10.3389/fcvm.2023.1126888 Text en Copyright © 2023 Ning, Sun, Xu, Li, Ke, Mai, Lin, Yang, Xian and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Ning, Yi-Le
Sun, Ce
Xu, Xiang-Hui
Li, Li
Ke, Yan-Ji
Mai, Ye
Lin, Xin-Feng
Yang, Zhong-Qi
Xian, Shao-Xiang
Chen, Wei-Tao
Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title_full Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title_fullStr Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title_full_unstemmed Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title_short Tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: An analysis of the MIMIC-IV database
title_sort tendency of dynamic vasoactive and inotropic medications data as a robust predictor of mortality in patients with septic shock: an analysis of the mimic-iv database
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10112491/
https://www.ncbi.nlm.nih.gov/pubmed/37082452
http://dx.doi.org/10.3389/fcvm.2023.1126888
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