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

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.

Management of Coronavirus Disease 2019 Intubation Teams.

Some patients infected with the Coronavirus Disease 2019 (COVID-19) require endotracheal intubation, an aerosol-generating procedure that is believed to result in viral transmission to personnel performing the procedure. Additionally, donning and doffing personal protective equipment can be time consuming. In particular, doffing requires strict protocol adherence to avoid exposure. We describe the Emory Healthcare intubation team approach during the COVID-19 pandemic. This structure resulted in only 1 team member testing positive for COVID-19 despite 253 patient intubations over a 6-week period with 153 anesthesia providers on service.