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A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit

BACKGROUND: Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care...

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Autores principales: Potes, Cristhian, Conroy, Bryan, Xu-Wilson, Minnan, Newth, Christopher, Inwald, David, Frassica, Joseph
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5694915/
https://www.ncbi.nlm.nih.gov/pubmed/29151364
http://dx.doi.org/10.1186/s13054-017-1874-z
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author Potes, Cristhian
Conroy, Bryan
Xu-Wilson, Minnan
Newth, Christopher
Inwald, David
Frassica, Joseph
author_facet Potes, Cristhian
Conroy, Bryan
Xu-Wilson, Minnan
Newth, Christopher
Inwald, David
Frassica, Joseph
author_sort Potes, Cristhian
collection PubMed
description BACKGROUND: Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration. METHODS: The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted. RESULTS: A total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier. CONCLUSIONS: The proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-017-1874-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-56949152017-11-27 A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit Potes, Cristhian Conroy, Bryan Xu-Wilson, Minnan Newth, Christopher Inwald, David Frassica, Joseph Crit Care Research BACKGROUND: Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration. METHODS: The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted. RESULTS: A total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier. CONCLUSIONS: The proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13054-017-1874-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-20 /pmc/articles/PMC5694915/ /pubmed/29151364 http://dx.doi.org/10.1186/s13054-017-1874-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Potes, Cristhian
Conroy, Bryan
Xu-Wilson, Minnan
Newth, Christopher
Inwald, David
Frassica, Joseph
A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title_full A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title_fullStr A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title_full_unstemmed A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title_short A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
title_sort clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5694915/
https://www.ncbi.nlm.nih.gov/pubmed/29151364
http://dx.doi.org/10.1186/s13054-017-1874-z
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