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Research ArticleOriginal Research

Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning

Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher and Vignesh Subbian
Respiratory Care February 2023, respcare.10382; DOI: https://doi.org/10.4187/respcare.10382
Patrick T Essay
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.
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Jarrod M Mosier
Department of Emergency Medicine, The University of Arizona College of Medicine, Tucson, Arizona; and Division of Pulmonary, Allergy, Critical Care, and Sleep, Department of Medicine, The University of Arizona College of Medicine, Tucson, Arizona.
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  • For correspondence: [email protected]
Amin Nayebi
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona.
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Julia M Fisher
Statistics Consulting Laboratory, BIO5 Institute, The University of Arizona, Tucson, Arizona.
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Vignesh Subbian
Department of Systems and Industrial Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; Department of Biomedical Engineering, College of Engineering, The University of Arizona, Tucson, Arizona; and BIO5 Institute, The University of Arizona, Tucson, Arizona.
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Abstract

BACKGROUND: Noninvasive respiratory support (NRS) is increasingly used to support patients with acute respiratory failure. However, noninvasive support failure may worsen outcomes compared to primary support with invasive mechanical ventilation. Therefore, there is a need to identify patients where NRS is failing so that treatment can be reassessed and adjusted. The objective of this study was to develop and evaluate 3 recurrent neural network (RNN) models to predict NRS failure.

METHODS: This was a cross-sectional observational study to evaluate the ability of deep RNN models (long short-term memory [LSTM], gated recurrent unit [GRU]), and GRU with trainable decay) to predict failure of NRS. Data were extracted from electronic health records from all adult (≥ 18 y) patient records requiring any type of oxygen therapy or mechanical ventilation between November 1, 2013–September 30, 2020, across 46 ICUs in the Southwest United States in a single health care network. Input variables for each model included serum chloride, creatinine, albumin, breathing frequency, heart rate, SpO2, FIO2, arterial oxygen saturation (SaO2), and 2 measurements each (point-of-care and laboratory measurement) of PaO2 and partial pressure of arterial oxygen from an arterial blood gas.

RESULTS: Time series data from electronic health records were available for 22,075 subjects. The highest accuracy and area under the receiver operating characteristic curve were for the LSTM model (94.04% and 0.9636, respectively). Accurate predictions were made 12 h after ICU admission, and performance remained high well in advance of NRS failure.

CONCLUSIONS: RNN models using routinely collected time series data can accurately predict NRS failure well before intubation. This lead time may provide an opportunity to intervene to optimize patient outcomes.

  • machine learning
  • deep neural network
  • ICU
  • respiratory failure
  • mechanical ventilation

Footnotes

  • Correspondence: Jarrod M Mosier MD, Department of Emergency Medicine, 1501 N. Campbell Avenue, AHSL 4171D, PO Box 245057, Tucson, AZ 85724–5057. E-mail: jmosier{at}aemrc.arizona.edu
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Respiratory Care: 68 (3)
Respiratory Care
Vol. 68, Issue 3
1 Mar 2023
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Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning
Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher, Vignesh Subbian
Respiratory Care Feb 2023, respcare.10382; DOI: 10.4187/respcare.10382

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Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning
Patrick T Essay, Jarrod M Mosier, Amin Nayebi, Julia M Fisher, Vignesh Subbian
Respiratory Care Feb 2023, respcare.10382; DOI: 10.4187/respcare.10382
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Keywords

  • machine learning
  • deep neural network
  • ICU
  • respiratory failure
  • mechanical ventilation

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