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Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach

BACKGROUND: Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or c...

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Autores principales: van Wijk, Raymond J., Quinten, Vincent M., van Rossum, Mathilde C., Bouma, Hjalmar R., ter Maaten, Jan C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067229/
https://www.ncbi.nlm.nih.gov/pubmed/37005664
http://dx.doi.org/10.1186/s13049-023-01078-w
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author van Wijk, Raymond J.
Quinten, Vincent M.
van Rossum, Mathilde C.
Bouma, Hjalmar R.
ter Maaten, Jan C.
author_facet van Wijk, Raymond J.
Quinten, Vincent M.
van Rossum, Mathilde C.
Bouma, Hjalmar R.
ter Maaten, Jan C.
author_sort van Wijk, Raymond J.
collection PubMed
description BACKGROUND: Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. METHODS: Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. RESULTS: In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. CONCLUSIONS: We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-023-01078-w.
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spelling pubmed-100672292023-04-03 Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach van Wijk, Raymond J. Quinten, Vincent M. van Rossum, Mathilde C. Bouma, Hjalmar R. ter Maaten, Jan C. Scand J Trauma Resusc Emerg Med Original Research BACKGROUND: Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. METHODS: Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. RESULTS: In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. CONCLUSIONS: We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13049-023-01078-w. BioMed Central 2023-04-01 /pmc/articles/PMC10067229/ /pubmed/37005664 http://dx.doi.org/10.1186/s13049-023-01078-w Text en © The Author(s) 2023 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 Original Research
van Wijk, Raymond J.
Quinten, Vincent M.
van Rossum, Mathilde C.
Bouma, Hjalmar R.
ter Maaten, Jan C.
Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title_full Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title_fullStr Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title_full_unstemmed Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title_short Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
title_sort predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067229/
https://www.ncbi.nlm.nih.gov/pubmed/37005664
http://dx.doi.org/10.1186/s13049-023-01078-w
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