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: 10371943
• 负责人: Gilles Clermont
• 金额: $ 63.97万
• 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/10371943
• 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; Machine Learning; 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; augmented intelligence; base; clinically relevant; data modeling; design; effective therapy; effectiveness validation; hemodynamics; improved outcome; intelligent algorithm; learning algorithm; learning strategy; 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.

摘要