VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19

摘要 2019冠状病毒病大流行给医疗保健系统和全球 经济学突然出现的病例激增使现有资源不堪重负， 病人数量增加，导致许多药品、人员和设备供应短缺。 机械通气成为一个特殊的问题，因为一项新发表的研究报告说，18 研究中的24名COVID-19患者（75%）需要机械通气。早期 在大流行的几个月里，许多提供者决定尽早插管，因为他们认为病人 最终需要机械通气，以避免“紧急插管”和潜在的 污染.一项关于重症监护病房COVID-19患者的最新观察性研究 急性低氧血症性呼吸衰竭的早期有创机械通气 与60天死亡风险增加相关。这方面的一个核心问题是 护理人员无法预测哪些患者可能需要机械通气， 使用临床参数通常是主观的，并且在不同的机构之间是不一致的。我们有 将机器学习算法应用于电子健康记录中常见的数据， (EHR)开发并验证一个预测模型，用于提前24小时预测呼吸衰竭。 这种新的预测模型在我们的内部和外部研究中已经证明AUC在0.90-0.94的范围内。 外部COVID-19数据集。也就是说，我们现在有强大的能力来预测哪些患者可能需要 机械通气和哪些不会。我们现在正计划在临床上部署， 通过添加其他数据流（如成像）来迭代我们的模型， 预测能力，而且使预测更'可操作'，使临床医生可以及时追求 而不仅仅是被告知一个预后。我们正在进一步解决许多障碍， 通过解决“临床医生买入”来实现，这涉及使我们的基本推理 算法更加透明，使预测无缝集成到临床工作流程中， 找到可行的参数，这将允许预测和治疗干预。这样的 算法将提高临床医生估计呼吸衰竭风险的能力，理想情况下， 及时预测并响应患者的需求。此外，如果预测时间足够长， 这样的系统可以促进相关资源的分类和优化 （医务人员和工作人员）在给定医院内和跨医疗保健系统。最后，虽然 2019冠状病毒病大流行凸显了优化机械通气时机的必要性， 根据该建议开发的技术广泛适用于呼吸衰竭的其他原因 以及其他类型的器官支持技术。

Abstract The COVID-19 pandemic has led to massive challenges for health care systems and for global economics. The surge in cases which occurred abruptly strained the existing resources to care for the volume of patients, leading to a shortage of supply in many medications, personnel and equipment. The mechanical ventilator became a particular problem as one newly published study reported that 18 out of 24 patients with COVID-19 in the study (75%) required mechanical ventilation. During the early months of the pandemic many providers decided to intubate early on the assumption that patients would eventually need mechanical ventilation so as to avoid ‘crash intubation’ and potential contamination. A recent observational study of intensive care unit patients with COVID-19 suffering from acute hypoxemic respiratory failure revealed that early invasive mechanical ventilation was associated with an increased risk of day-60 mortality. One central problem in this context was caregivers’ inability to predict which patients may need mechanical ventilation since existing methods using clinical parameters are often subjective and inconsistent across different institutions. We have thus applied machine learning algorithms to commonly available data in electronic health records (EHR) to develop and validate a predictive model for 24-hours ahead prediction of respiratory failure. This novel predictive model has demonstrated AUCs in the range of 0.90-0.94 in our internal and external COVID-19 datasets. That is, we have a robust ability now to predict which patients may need mechanical ventilation and which will not. We are now planning to deploy clinically and to improve iteratively on our model by adding other data streams such as imaging to not only improve our predictive ability but also to make the predictions more ‘actionable’, so that clinicians can pursue timely interventions rather than just being told a prognosis. We are further addressing the many barriers to implementation by addressing ‘clinician buy-in’ which involves making the underlying reasoning of our algorithms more transparent, making the predictions seamlessly integrated into clinical workflow, and finding actionable parameters that will allow both predictions and therapeutic interventions. Such an algorithm will enhance the ability of clinicians to estimate the risk for respiratory failure, and ideally, to anticipate and respond to patient needs in a timely fashion. Moreover, given a long enough prediction horizon (48-72 hours) such systems can facilitate triage and optimization of related resources (ventilators and personnel) within a given hospital and across healthcare systems. Finally, while the COVID-19 pandemic highlighted the need for optimizing the timing of mechanical ventilation, the techniques developed under this proposal are broadly applicable to other causes of respiratory failure and to other types of organ support technologies.