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...
Guardado en:
Autores principales: | , , , , , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
BMC
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f6daea3bc6d949eda64325550cba9c99 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:f6daea3bc6d949eda64325550cba9c99 |
---|---|
record_format |
dspace |
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 |
work_keys_str_mv |
AT asifrahman earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT yalechang earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT junzidong earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT bryanconroy earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT annamalainatarajan earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT takahirokinoshita earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT francescovicario earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT josephfrassica earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning AT minnanxuwilson earlypredictionofhemodynamicinterventionsintheintensivecareunitusingmachinelearning |
_version_ |
1718419340625182720 |