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

摘要 急性肾损伤（阿基）影响了多达一半的重症监护室（ICU）重症患者。在 阿基和血流动力学不稳定患者，连续性肾脏替代治疗（CRRT）是 首选透析方式。ICU死亡率在这个脆弱的人群是高的，但肾脏恢复发生在 三分之二的幸存者。普遍接受和准确的方法来预测生存或肾脏 目前，这些患者中不存在恢复。这是临床相关的关键结果的预测 可以指导CRRT输送、急性护理目标和个性化ICU后护理的决策 根据肾功能恢复情况判断预后。由于没有经证实的干预措施来改善 对于这些患者，识别可改变的风险因素和亚表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，可以构建准确， 可靠的风险预测模型。此外，使用新的算法，如可行解算法， (FSA)可以帮助识别患者的亚表型和模型应用。我们建议发展和 验证创新和可重复的DL方法，以预测可操作时间点的无RRT生存期 并使用FSA根据多因素确定具有不同无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，与优化的模型相比，DL模型具有优越性。 无RRT生存预测的逻辑回归。24小时死亡率的预测得到改善， 在CRRT期间合并时间序列数据。我们假设时间序列多模态数据 （包括EHR和CRRT机器数据）将生成准确和可推广的风险预测， 指导临床干预并识别用于模型解释和临床实用性的亚表型 试验.我们将利用来自9个机构的数据集，这些机构包括多模式EHR临床数据， 来自CRRT机器的程序和治疗数据，用于模型和亚表型开发、测试， 独立验证。这项创新的研究将1）协助临床决策支持的发展 指导知情CRRT输送并改善临床结局的平台，以及2）识别 患者可以从更加个性化和可测试的新型CRRT干预中获益。

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.