Early prediction of hemodynamic interventions in the intensive care unit using machine learning

Abstract Background Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the be...

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Autores principales:	Asif Rahman, Yale Chang, Junzi Dong, Bryan Conroy, Annamalai Natarajan, Takahiro Kinoshita, Francesco Vicario, Joseph Frassica, Minnan Xu-Wilson
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Hemodynamics Vasoactive therapy Machine learning Clinical decision support Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
Acceso en línea:	https://doaj.org/article/f6daea3bc6d949eda64325550cba9c99
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spelling	oai:doaj.org-article:f6daea3bc6d949eda64325550cba9c992021-11-21T12:02:51ZEarly prediction of hemodynamic interventions in the intensive care unit using machine learning10.1186/s13054-021-03808-x1364-8535https://doaj.org/article/f6daea3bc6d949eda64325550cba9c992021-11-01T00:00:00Zhttps://doi.org/10.1186/s13054-021-03808-xhttps://doaj.org/toc/1364-8535Abstract Background Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. Methods We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. Results HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. Conclusions The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.Asif RahmanYale ChangJunzi DongBryan ConroyAnnamalai NatarajanTakahiro KinoshitaFrancesco VicarioJoseph FrassicaMinnan Xu-WilsonBMCarticleHemodynamicsVasoactive therapyMachine learningClinical decision supportMedical emergencies. Critical care. Intensive care. First aidRC86-88.9ENCritical Care, Vol 25, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hemodynamics Vasoactive therapy Machine learning Clinical decision support Medical emergencies. Critical care. Intensive care. First aid RC86-88.9
spellingShingle	Hemodynamics Vasoactive therapy Machine learning Clinical decision support Medical emergencies. Critical care. Intensive care. First aid RC86-88.9 Asif Rahman Yale Chang Junzi Dong Bryan Conroy Annamalai Natarajan Takahiro Kinoshita Francesco Vicario Joseph Frassica Minnan Xu-Wilson Early prediction of hemodynamic interventions in the intensive care unit using machine learning
description	Abstract Background Timely recognition of hemodynamic instability in critically ill patients enables increased vigilance and early treatment opportunities. We develop the Hemodynamic Stability Index (HSI), which highlights situational awareness of possible hemodynamic instability occurring at the bedside and to prompt assessment for potential hemodynamic interventions. Methods We used an ensemble of decision trees to obtain a real-time risk score that predicts the initiation of hemodynamic interventions an hour into the future. We developed the model using the eICU Research Institute (eRI) database, based on adult ICU admissions from 2012 to 2016. A total of 208,375 ICU stays met the inclusion criteria, with 32,896 patients (prevalence = 18%) experiencing at least one instability event where they received one of the interventions during their stay. Predictors included vital signs, laboratory measurements, and ventilation settings. Results HSI showed significantly better performance compared to single parameters like systolic blood pressure and shock index (heart rate/systolic blood pressure) and showed good generalization across patient subgroups. HSI AUC was 0.82 and predicted 52% of all hemodynamic interventions with a lead time of 1-h with a specificity of 92%. In addition to predicting future hemodynamic interventions, our model provides confidence intervals and a ranked list of clinical features that contribute to each prediction. Importantly, HSI can use a sparse set of physiologic variables and abstains from making a prediction when the confidence is below an acceptable threshold. Conclusions The HSI algorithm provides a single score that summarizes hemodynamic status in real time using multiple physiologic parameters in patient monitors and electronic medical records (EMR). Importantly, HSI is designed for real-world deployment, demonstrating generalizability, strong performance under different data availability conditions, and providing model explanation in the form of feature importance and prediction confidence.
format	article
author	Asif Rahman Yale Chang Junzi Dong Bryan Conroy Annamalai Natarajan Takahiro Kinoshita Francesco Vicario Joseph Frassica Minnan Xu-Wilson
author_facet	Asif Rahman Yale Chang Junzi Dong Bryan Conroy Annamalai Natarajan Takahiro Kinoshita Francesco Vicario Joseph Frassica Minnan Xu-Wilson
author_sort	Asif Rahman
title	Early prediction of hemodynamic interventions in the intensive care unit using machine learning
title_short	Early prediction of hemodynamic interventions in the intensive care unit using machine learning
title_full	Early prediction of hemodynamic interventions in the intensive care unit using machine learning
title_fullStr	Early prediction of hemodynamic interventions in the intensive care unit using machine learning
title_full_unstemmed	Early prediction of hemodynamic interventions in the intensive care unit using machine learning
title_sort	early prediction of hemodynamic interventions in the intensive care unit using machine learning
publisher	BMC
publishDate	2021
url	https://doaj.org/article/f6daea3bc6d949eda64325550cba9c99
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Early prediction of hemodynamic interventions in the intensive care unit using machine learning

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