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.
Highlights
Hypoxemia, or a decrease in blood oxygen saturation, is a common symptom in critically ill patients, with a multinational, multicenter study finding that hypoxemia is a significant risk factor for mortality, with prevalence greater than 50% in ICU patients [1]
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
SWIFT identified more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively
Summary
A decrease in blood oxygen saturation, is a common symptom in critically ill patients, with a multinational, multicenter study finding that hypoxemia is a significant risk factor for mortality, with prevalence greater than 50% in ICU patients [1]. Severe cases of COVID-19 are characterized by hypoxemia and dyspnea (difficulty breathing) which can rapidly progress to respiratory failure [3]. These patients often require advanced life support measures including invasive mechanical ventilation, hospitalization in ICUs and even extra-corporeal membrane oxygenation (ECMO). As the COVID-19 pandemic continues to exact a heavy mortality toll with over half a million deaths directly attributed to the disease in the United States alone and herd immunity by vaccination remains elusive, it is important to find ways to manage these scarce resources and identify patients unable to maintain oxygen saturation without intervention. Triage systems using monitoring of blood oxygenation to inform life support measures are tremendously useful for directing resource allocation and have been demonstrated to reduce mortality [5]
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