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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.

摘要