AI driven acute renal replacement therapy - (AID-ART)

摘要 在接受治疗的急性肾损伤的危重患者中，有三分之一会发生透析中低血压。 在重症监护病房(ICU)接受肾脏替代治疗。IDH的发生与以下因素有关 增加资源利用率，如液体和血管加压剂给药，停用肾脏 替代疗法，肾功能恢复减慢，对肾脏替代疗法的依赖和 死亡。IDH通常不被认识，直到它被很好地建立起来，那时患者对治疗是困难的。 或者已经出现器官损伤。因此，如果一个人能够准确地预测谁和患者何时发生 IDH时，可给予有效的预防性治疗，以降低IDH的风险，改善预后。 我们的初步工作表明，高级高频数据建模和波形分析识别出 有低血压风险的患者在ICU监测2分钟内，如果监测5分钟， 区分在接下来的48小时内会出现低血压或保持稳定的患者。在这 题为《人工智能驱动的急性肾脏替代疗法(AID-ART)》，我们建议 使用链接的电子健康记录和高频监测数据对危重患者进行预测性分析 接受间歇性和持续性肾脏替代治疗的急性肾损伤患者 匹兹堡大学医学中心和梅奥诊所ICU。我们将检查各种方法的准确性 用于预测IDH风险的机器学习模型--评估模型性能、可用性、警报频率、交货期 需要警惕的人数、住院死亡率和对肾脏替代疗法的依赖(目标1a)； 预测IDH对一系列临床干预措施的反应和随后的临床结果(目标1b)；以及 在两个医疗保健系统之间执行交叉验证(目标1c)。我们将构造强化学习 系统为IDH警报和测量驱动的响应开发规则驱动的干预措施，以避免和 根据功能性血流动力学监测原则(目标2a)对IDH作出反应。我们还将开发一种 基于事件概率学习最优干预策略的强化学习算法 而不是对IDH事件作出反应(目标2b)。我们会默默地部署和评估这个人工的能力 智能(AI)算法，可预测IDH风险并实时建议两者的干预措施 医疗保健系统。然后，我们将使用专家临床医生评估建议的干预措施的有效性 裁决小组(目标3a)；并将人工智能建议的干预措施与实际干预措施进行比较 床边临床医生实施的干预措施(目标3b)。这项提议将是未来的先兆。 多中心随机临床试验，以检查个性化风险预测和人工智能增强的管理 急性肾损伤危重患者行肾脏替代治疗后弥漫性尿失禁 重症监护室。

Abstract Intradialytic hypotension (IDH) occurs in one-third of critically ill patients with acute kidney injury and treated with kidney replacement therapy in the intensive care unit (ICU). Occurrence of IDH is associated with increased resource utilization such as fluid and vasopressor administration, discontinuation of kidney replacement therapy, decreased recovery of kidney function, dependence on kidney replacement therapy and death. IDH is often unrecognized until it is well established, by which time patients are refractory to treatment or have already developed organ injury. Thus, if one could accurately predict who and when patients develop IDH, then effective preemptive treatments could be administered to reduce risk of IDH and improve outcomes. Our preliminary work showed that advanced high-frequency data modeling and waveform analysis identified patients at risk for hypotension within 2 minutes of monitoring in the ICU, and if monitored for 5 minutes, differentiated between patients who would develop hypotension or remain stable over the next 48 hours. In this proposal entitled “Artificial Intelligence Driven Acute Renal Replacement Therapy (AID-ART)”, we propose to apply predictive analytics using linked electronic health record and high-frequency monitor data to critically ill patients with acute kidney injury and undergoing intermittent and continuous kidney replacement therapies at the University of Pittsburgh Medical Center and the Mayo Clinic ICUs. We will examine the accuracy of various machine learning models to predict IDH risk-evaluating model performance, usability, alert frequency, lead time and number needed to alert, and hospital mortality and dependence on kidney replacement therapy (Aim 1a); predict response to a range of clinical interventions for IDH and subsequent clinical outcomes (Aim 1b); and perform cross validation across the two healthcare systems (Aim 1c). We will construct reinforcement learning systems to develop a rule-driven intervention for IDH alerts and measurement-driven responses to avoid and respond to IDH based on principles of functional hemodynamic monitoring (Aim 2a). We will also develop a reinforcement learning algorithm to learn an optimal intervention strategy based on the probability of events rather than in reaction to IDH events (Aim 2b). We will silently deploy and evaluate the ability of this artificial intelligence (AI) algorithm to forecast IDH risk and recommend interventions in real-time across the two healthcare systems. We will then assess the validity of recommended interventions using an expert clinician adjudication panel (Aim 3a); and will compare the AI recommended interventions with that of actual interventions performed by bedside clinicians (Aim 3b). This proposal will be the harbinger of a future multicenter randomized clinical trial to examine personalized risk prediction and AI-augmented management of IDH among critically ill patients with acute kidney injury and undergoing kidney replacement therapy in the intensive care unit.