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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的急性肾脏损伤（AKI）会影响多达一半的重症监护病房（ICU）的重症患者。 AKI和血流动力学不稳定的患者，连续肾脏替代疗法（CRRT）是首选的透析方式。该脆弱人群中的ICU死亡率很高，但肾脏恢复发生在最多三分之二的生存。普遍接受且准确的方法来预测生存或肾脏这些患者目前不存在这些患者的康复。这在临床上与关键结果的预测相关可以指导CRRT交付，急诊目标和个性化ICU护理的决策根据肾脏恢复提示。由于没有可靠的干预措施来改善结果这些患者，确定可修改的危险因素和亚表格型对于发展精度是必要的 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） - AI的子集 - 一种有价值的方法，可以构建准确的和可靠的风险预测模型。此外，使用新型算法（例如可行溶液算法）（FSA）可以帮助识别患者的子表格型和模型应用。我们建议开发和验证创新和可重现的DL方法，以预测可起诉时点的无RRT生存并使用FSA识别具有不同的无RRT生存风险的患者亚表型模态数据。我们发表的初步数据表明，与优化相比，DL模型的优势无RRT生存预测的逻辑回归。预测24小时死亡率已通过在CRRT期间合并时间序列数据。我们假设时间序列多模式数据（包括EHR和CRRT机器数据）将产生准确且可推广的风险预测指导临床干预措施并确定用于模型解释和临床实用程序的亚表格型测试。我们将利用来自多个多模式EHR临床数据的9个机构的数据集和来自CRRT机器的程序化和治疗数据，用于模型和子表型开发，测试，和独立验证。这项创新的研究将1）协助开发临床决策支持指导知情的CRRT交付并改善临床结果的平台，2）确定可能受益于更个性化和可检验的新型CRRT干预措施的患者。