학술논문
SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO.sub.2 waveform prediction
Research Article
Research Article
Document Type
Report
Author
Source
PLoS Computational Biology. December 21, 2021, Vol. 17 Issue 12, e1009712
Subject
Language
English
ISSN
1553-734X
Abstract
Author(s): Akshaya V. Annapragada 1, Joseph L. Greenstein 2, Sanjukta N. Bose 2,3, Bradford D. Winters 4, Sridevi V. Sarma 2,5, Raimond L. Winslow 2,5,* Introduction Hypoxemia, or a decrease [...]
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 (SpO.sub.2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO.sub.2) waveforms 5 and 30 minutes in the future using only prior SpO.sub.2 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 SpO.sub.2 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.
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 (SpO.sub.2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO.sub.2) waveforms 5 and 30 minutes in the future using only prior SpO.sub.2 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 SpO.sub.2 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.