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...

Descripción completa

Guardado en:
Detalles Bibliográficos
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:
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