TY - JOUR
T1 - SWIFT
T2 - A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction
AU - Annapragada, Akshaya V.
AU - Greenstein, Joseph L.
AU - Bose, Sanjukta N.
AU - Winters, Bradford D.
AU - Sarma, Sridevi V.
AU - Winslow, Raimond L.
N1 - Funding Information:
This work was supported by NSF RAPID2031195 (RLW and SVS). AVA acknowledges support from the NIH Medical Scientist Training Program 1T32GM136577 and the Joseph and Helen Pardoll Scholarship for MSTP Students (AVA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2021 Annapragada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pcbi.1009712
DO - 10.1371/journal.pcbi.1009712
M3 - Article
C2 - 34932550
AN - SCOPUS:85122341468
SN - 1553-734X
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 12
M1 - e1009712
ER -