SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction.

Journal: PLoS computational biology
Published Date: Dec 1, 2021

Abstract

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Authors

  • Akshaya V Annapragada
    Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
  • Joseph L Greenstein
    Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
  • Sanjukta N Bose
    Institute for Computational Medicine, The Johns Hopkins University, Baltimore, Maryland, United States of America.
  • Bradford D Winters
    Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
  • Sridevi V Sarma
    Biomedical Engineering, Institute for Computational Medicine, Neuromedical Control Systems Group, The Johns Hopkins University, Rm. 315 Hackerman Hall, 3400 N. Charles St., Baltimore, MD, 21218, USA. sridevi.sarma@gmail.com.
  • Raimond L Winslow
    Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.

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