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

Akshaya V Annapragada, Joseph L Greenstein, Sanjukta N Bose, Bradford D Winters, Sridevi V Sarma and Raimond L Winslow

PLOS Computational Biology, 2021, vol. 17, issue 12, 1-15

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.Author summary: Hypoxemia, or loss of blood oxygen saturation, is a dangerous condition that drives morbidity and mortality in critically ill patients, including COVID-19 patients and patients with brain injury or cardiac arrest. The ability to identify hypoxemia before it occurs would expand the possibilities for effective clinical interventions. To this end, we present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that can predict blood oxygen saturation 5 and 30 minutes in the future in critically ill patients. In testing, SWIFT identified more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT can predict both the occurrence and magnitude of hypoxemic events, which provides clinical information that can help prevent hypoxemia in critically ill patients. SWIFT can be used in clinical decision support systems to improve the management of patients at risk for hypoxemia during the COVID-19 pandemic and beyond.

Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009712 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 09712&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1009712

DOI: 10.1371/journal.pcbi.1009712

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().