Artificial Intelligence to Predict Outcomes in Patients with Acute Kidney Injury on Continuous Renal Replacement Therapy

人工智能预测急性肾损伤患者连续肾脏替代治疗的结果

• 批准号: 10658576
• 负责人: Girish Nitin Nadkarni
• 金额: $ 70.5万
• 依托单位: UNIVERSITY OF ALABAMA AT BIRMINGHAM
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658576
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Affect; Algorithms; Artificial Intelligence; Awareness; Biometry; Blood Vessels; Caring; Catheters; Classification; Clinic; Clinical; Clinical Data; Clinical Research; Clinical Trials; Critical Illness; Data; Data Set; Decision Making; Development; Dialysis procedure; Dose; Electronic Health Record; Excision; Exclusion; Fluid Therapy; Future; Goals; Hour; Institution; Intensive Care Units; Intervention; Kidney; Liquid substance; Logistic Regressions; Measurement; Measures; Modality; Modeling; Modification; Monitor; Outcome; Patient Admission; Patients; Phenotype; Population; Prognosis; Publishing; Recovery; Registries; Renal Replacement Therapy; Reproducibility; Research; Resolution; Risk; Risk Factors; Series; Survivors; Testing; Therapeutic Intervention; Time; Validation; Vulnerable Populations; acute care; clinical decision support; clinical development; clinically relevant; computing resources; data resource; deep learning; deep learning model; density; experience; hemodynamics; improved; improved outcome; innovation; model development; modifiable risk; mortality; multimodal data; multimodality; novel; outcome prediction; personalized intervention; precision medicine; predict clinical outcome; predictive modeling; risk prediction; risk prediction model; solute; survival prediction; tool

ABSTRACT Acute kidney injury (AKI) affects up to half of critically ill patients admitted to intensive care units (ICU). In patients with AKI and hemodynamic instability, continuous renal replacement therapy (CRRT) is the preferred dialysis modality. ICU mortality in this vulnerable population is high but kidney recovery occurs in up to two-thirds of survivors. Universally accepted and accurate approaches for predicting survival or kidney recovery in these patients do not exist currently. This is clinically relevant as prediction of key outcomes could guide decision-making of CRRT delivery, goals of acute care, and personalized post-ICU care according to kidney recovery prognosis. Since there are no proven interventions to improve outcomes in these patients, identification of modifiable risk factors and sub-phenotypes is necessary to develop precision medicine approaches in CRRT. Due to advances in artificial intelligence (AI) and availability of multi-modal data, deep learning (DL) –a subset of AI– is a valuable approach that allows construction of accurate and reliable risk prediction models. Further, the use of novel algorithms such as the Feasible Solution Algorithm (FSA) could help identify patient sub-phenotypes and model applications. We propose to develop and validate innovative and reproducible DL approaches to predict RRT-free survival at actionable timepoints and use FSA to identify patient sub-phenotypes with differing RRT-free survival risk according to multi- modal data. Our published preliminary data demonstrated superiority of DL models compared to optimized logistic regression for RRT-free survival prediction. Prediction of 24-hour mortality was improved by incorporating time-series data during CRRT. We hypothesize that time-series multi-modal data (including EHR and CRRT machine data) will generate accurate and generalizable risk prediction to guide clinical interventions and identify sub-phenotypes for model interpretation and clinical utility testing. We will utilize datasets from 9 institutions that encompass multi-modal EHR clinical data and programmatic and therapy data from CRRT machines for model and sub-phenotyping development, testing, and independent validation. This innovative research will 1) assist development of clinical decision support platforms to guide informed CRRT delivery and improve clinical outcomes and 2) identify sub-phenotypes of patients that could benefit from more personalized and testable novel CRRT interventions.

摘要 急性肾损伤(AKI)影响多达一半住进重症监护病房(ICU)的危重患者。在……里面 AKI和血流动力学不稳定的患者，连续性肾脏替代治疗(CRRT)是 首选透析方式。这一脆弱人群的ICU死亡率很高，但肾脏恢复发生在 高达三分之二的幸存者。普遍接受的准确预测存活或肾脏的方法 这些患者目前还不存在康复的情况。这在临床上与关键结果的预测有关 可以指导CRRT交付的决策、急诊护理的目标以及ICU后的个性化护理 根据肾脏恢复情况判断预后。因为没有经过证实的干预措施可以改善 对于这些患者，识别可改变的危险因素和亚型是发展精确度所必需的 CRRT中的医学方法。由于人工智能(AI)的进步和多模式的可用性 数据，深度学习(DL)-人工智能的一个子集-是一种有价值的方法，它允许构建准确和 可靠的风险预测模型。此外，使用诸如可行解算法之类的新算法 (FSA)可以帮助确定患者的亚型和模型应用。我们建议开发和开发 验证创新和可重复的DL方法，以预测在可操作时间点的无RRT生存 并使用FSA根据多项指标确定具有不同RRT无生存风险的患者亚型 模式数据。我们发布的初步数据表明，与优化后的模型相比，DL模型具有更好的性能 Logistic回归用于无RRT生存预测。对24小时死亡率的预测得到了改进 纳入CRRT期间的时间序列数据。我们假设时间序列的多模式数据 (包括EHR和CRRT机器数据)将生成准确和可概括的风险预测，以 指导临床干预并确定模型解释和临床实用的亚型 测试。我们将利用来自9个机构的数据集，这些数据集包含多模式EHR临床数据和 来自CRRT机器的程序和治疗数据，用于模型和子表型开发、测试、 和独立验证。这项创新性的研究将1)有助于临床决策支持的发展 指导知情CRRT交付和改善临床结果的平台，以及2)确定 可以从更个性化和可测试的新型CRRT干预中受益的患者。