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: 10630230
• 负责人: Gilles Clermont
• 金额: $ 61.68万
• 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/10630230
• 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; Early Diagnosis; 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; Hypovolemics; 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; Reaction; Recommendation; Recovery; Refractory; Renal Replacement Therapy; Renal function; Resolution; Resources; Resuscitation; Risk; Risk Reduction; Structure; System; Testing; Time; Titrations; Training; Universities; Validation; Vasoconstrictor Agents; Work; adjudication; artificial intelligence algorithm; augmented intelligence; 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; randomized, clinical trials; 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.

摘要 三分之一的急性肾损伤重症患者发生透析中低血压（IDH）， 在重症监护室（ICU）接受肾脏替代治疗。IDH的发生与 资源利用增加，如液体和血管加压药给药，肾脏中断 替代治疗，肾功能恢复减少，对肾脏替代治疗的依赖， 死亡IDH通常在确诊之前无法被识别，此时患者对治疗难以耐受 或者已经出现器官损伤。因此，如果人们能够准确地预测谁和何时患者发展 IDH，那么有效的先发制人的治疗可以管理，以减少IDH的风险和改善结果。 我们的初步工作表明，先进的高频数据建模和波形分析确定了 在ICU监测2分钟内有低血压风险的患者，如果监测5分钟， 在接下来的48小时内，将发生低血压或保持稳定的患者区分开来。在这 在题为“人工智能驱动的急性肾脏替代疗法（AID-ART）"的提案中，我们建议 使用链接的电子健康记录和高频监测数据对重症患者进行预测分析 急性肾损伤并接受间歇性和连续性肾脏替代治疗的患者， 匹兹堡大学医学中心和马约诊所重症监护室。我们将检查各种 机器学习模型预测IDH风险评估模型性能，可用性，警报频率，交付时间 和需要警戒的人数，以及住院死亡率和对肾脏替代疗法的依赖（目标1a）; 预测对IDH的一系列临床干预措施的反应和随后的临床结局（目标1b）;以及 在两个医疗保健系统之间进行交叉验证（目标1c）。我们将构建强化学习 系统开发规则驱动的干预IDH警报和测量驱动的响应，以避免和 根据功能性血流动力学监测原则对IDH做出反应（目标2a）。我们还将开发一个 强化学习算法，根据事件概率学习最优干预策略 而不是对IDH事件的反应（目标2b）。我们会悄悄部署并评估这个人造卫星的能力 智能（AI）算法来预测IDH风险，并在两个系统之间实时推荐干预措施。 医疗保健系统。然后，我们将使用专家临床医生评估推荐干预措施的有效性 裁决小组（目标3a）;并将AI建议的干预措施与实际干预措施进行比较。 由床旁临床医生进行的干预（目标3b）。这项提议将预示着 多中心随机临床试验，以检查个性化的风险预测和AI增强的管理， 急性肾损伤和接受肾脏替代治疗的危重患者中的IDH 加护病房