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 provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, 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.

中文翻译：

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SWIFT：使用 SpO2 波形预测预测危重患者低氧血症的深度学习方法

在包括 COVID-19 患者在内的危重患者的脑损伤和心脏骤停等情况下，低氧血症是死亡率和临床结果不佳的重要驱动因素。鉴于低氧血症导致的大量负面临床结果，识别可能出现低氧血症的患者将为早期和更有效的干预提供宝贵的机会。我们提出了 SWIFT（SpO2 波形 ICU 预测技术），这是一种深度学习模型，仅使用先前的 SpO2 值作为输入来预测未来 5 分钟和 30 分钟的血氧饱和度 (SpO2) 波形。在对新数据进行测试时，SWIFT 分别预测了重症患者和 COVID-19 患者 80% 和 60% 以上的低氧血症事件。SWIFT 还预测平均 MSE 低于 0.0007 的 SpO2 波形。SWIFT 可提前 30 分钟提供有关潜在低氧血症事件的发生和严重程度的信息，使其可用于为临床干预、患者分类和最佳资源分配提供信息。SWIFT 可用于临床决策支持系统，以在 COVID-19 大流行期间及之后为重症患者的管理提供信息。