Department of Electrical and Computer Engineering, New York University (NYU), New York, USA.
Department of Biomedical Engineering, New York University (NYU), New York, USA.
Sci Rep. 2023 Jun 20;13(1):9968. doi: 10.1038/s41598-023-37013-3.
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images or comprehensive laboratory tests. This is infeasible for telehealth solutions and highlights a gap in deterioration prediction models based on minimal data, which can be recorded at a large scale in any clinic, nursing home, or even at the patient's home. In this study, we develop and compare two prognostic models that predict if a patient will experience deterioration in the forthcoming 3 to 24 h. The models sequentially process routine triadic vital signs: (a) oxygen saturation, (b) heart rate, and (c) temperature. These models are also provided with basic patient information, including sex, age, vaccination status, vaccination date, and status of obesity, hypertension, or diabetes. The difference between the two models is the way that the temporal dynamics of the vital signs are processed. Model #1 utilizes a temporally-dilated version of the Long-Short Term Memory model (LSTM) for temporal processes, and Model #2 utilizes a residual temporal convolutional network (TCN) for this purpose. We train and evaluate the models using data collected from 37,006 COVID-19 patients at NYU Langone Health in New York, USA. The convolution-based model outperforms the LSTM based model, achieving a high AUROC of 0.8844-0.9336 for 3 to 24 h deterioration prediction on a held-out test set. We also conduct occlusion experiments to evaluate the importance of each input feature, which reveals the significance of continuously monitoring the variation of the vital signs. Our results show the prospect for accurate deterioration forecast using a minimum feature set that can be relatively easily obtained using wearable devices and self-reported patient information.
未被识别的 COVID-19 患者病情恶化可导致高发病率和死亡率。大多数现有的恶化预测模型需要大量的临床信息，这些信息通常是在医院环境中收集的，例如医疗图像或综合实验室测试。这对于远程医疗解决方案来说是不可行的，这凸显了基于最小数据的恶化预测模型的差距，这些模型可以在任何诊所、疗养院甚至在患者家中大规模记录。在这项研究中，我们开发并比较了两种预测模型，用于预测患者在未来 3 至 24 小时内是否会恶化。这些模型依次处理常规三联生命体征：（a）血氧饱和度、（b）心率和（c）体温。这些模型还提供了基本的患者信息，包括性别、年龄、疫苗接种状态、接种日期以及肥胖、高血压或糖尿病的状况。这两种模型的区别在于处理生命体征时间动态的方式。模型 #1 使用长短期记忆模型（LSTM）的时间扩展版本来进行时间处理，而模型 #2 则使用残差时间卷积网络（TCN）来进行时间处理。我们使用从美国纽约 NYU Langone Health 收集的 37006 名 COVID-19 患者的数据来训练和评估模型。基于卷积的模型优于基于 LSTM 的模型，在独立测试集上，3 至 24 小时的恶化预测的高 AUROC 为 0.8844-0.9336。我们还进行了遮挡实验来评估每个输入特征的重要性，这揭示了连续监测生命体征变化的重要性。我们的结果表明，使用最小特征集可以实现准确的恶化预测，这些特征集可以使用可穿戴设备和患者自我报告的信息相对容易地获得。
