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

AI 驱动的急性肾脏替代疗法 - (AID-ART)

• 批准号: 10494259
• 负责人: Gilles Clermont
• 金额: $ 62.81万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10494259
• 关键词: Acute; Acute Renal Failure with Renal Papillary Necrosis; Algorithms; Animals; Artificial Intelligence; Cessation of life; Clinic; Clinical; Critical Illness; Data; Dependence; Dialysis procedure; Electronic Health Record; Event; Excision; Expert Opinion; Expert Systems; Frequencies; Future; Health system; Healthcare Systems; Hemorrhage; Hemorrhagic Shock; Hospital Mortality; Hospitals; Hour; Human; Hypotension; Hypovolemia; Intensive Care Units; Intervention; Lead; Learning; Link; Liquid substance; Measurement; Medical center; Modeling; Monitor; Morbidity - disease rate; Observational Study; Outcome; Patient Monitoring; Patients; Performance; Predictive Analytics; Probability; Psychological reinforcement; Randomized Clinical Trials; Reaction; Recovery; Refractory; Renal Replacement Therapy; Renal function; Resolution; Resources; Risk; Structure; System; Testing; Time; Titrations; Training; Universities; Validation; Vasoconstrictor Agents; Work; adjudicate; adjudication; artificial intelligence algorithm; augmented intelligence; base; clinically relevant; data modeling; design; effective therapy; effectiveness validation; hemodynamics; improved outcome; learning algorithm; learning strategy; machine learning model; mortality; mortality risk; organ injury; outcome prediction; personalized intervention; personalized risk prediction; porcine model; predicting response; prevent; prospective; response; risk prediction; usability

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.

抽象的 在三分之一的急性肾脏损伤患者中，酯内低血压（IDH）发生并接受治疗 在重症监护病房（ICU）中肾脏替代疗法。 IDH的出现与 增加资源利用（例如流体和加压器给药，肾中停用） 替代疗法，肾功能恢复的恢复，依赖肾脏替代疗法和 死亡。 IDH通常无法识别直到建立得很好，到此为止患者难治性治疗 或已经发生了器官损伤。如果有人能准确预测谁以及何时发展 IDH，可以进行有效的先发制人治疗，以降低IDH的风险并改善结果。 我们的初步工作表明，确定的高级高频数据建模和波形分析 在ICU监测后2分钟内有低血压的患者，如果监测5分钟， 区分会在接下来的48小时内会出现低血压或保持稳定的患者。在这个 提案为“人工智能驱动急性肾脏替代疗法（AID-ART）”，我们建议 使用链接的电子健康记录和高频监视器数据应用预测分析以重病 患有急性肾脏损伤的患者，并接受间歇性和连续的肾脏替代疗法 匹兹堡大学医学中心和梅奥诊所ICU。我们将检查各种的准确性 机器学习模型以预测IDH风险评估模型性能，可用性，警报频率，交货时间 需要警报，医院死亡率以及对肾脏替代疗法的依赖（AIM 1A）； 预测对IDH和随后临床结果的一系列临床干预措施的反应（AIM 1B）；和 在两个医疗保健系统（AIM 1C）上执行交叉验证。我们将构建强化学习 为IDH警报和测量驱动的响应开发规则驱动的干预措施的系统，以避免和 根据功能血流动力学监测原理（AIM 2A）对IDH做出反应。我们还将开发一个 增强学习算法以根据事件的可能性学习最佳干预策略 而不是对IDH事件的反应（AIM 2B）。我们将默默地部署和评估这种艺术的能力 智能（AI）算法以预测IDH的风险和建议干预措施，实时两者的实时干预措施 医疗保健系统。然后，我们将使用专家临床评估建议干预措施的有效性 调整面板（AIM 3A）；并将将AI建议的干预措施与实际的干预措施进行比较 床旁临床医生执行的干预措施（AIM 3B）。该提议将是未来的预兆 多中心随机临床试验，以检查个性化的风险预测和ai-aigment管理的管理 在患有急性肾脏损伤的重症患者和接受肾脏替代疗法中的IDH中 重症监护室。