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Early Prediction of Hemodynamic Interventions in the Intensive Care Unit Using Machine Learning by Asif Rahman; Yale Chang; Junzi Dong; Bryan Conroy; Annamalai Natarajan; Takahiro Kinoshita; Francesco Vicario; Joseph Frassica; Minnan Xu-Wilson is a Medicine article available to read on EtoBox.

What is Early Prediction of Hemodynamic Interventions in the Intensive Care Unit Using Machine Learning about?

## 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 p

Who reads Early Prediction of Hemodynamic Interventions in the Intensive Care Unit Using Machine Learning?

Author

Asif Rahman; Yale Chang; Junzi Dong; Bryan Conroy; Annamalai Natarajan; Takahiro Kinoshita; Francesco Vicario; Joseph Frassica; Minnan Xu-Wilson

Publisher

Springer Science and Business Media LLC

Published

2021

Language

EN

Field

Medicine (Health Sciences)