Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, 100020 Beijing, China.
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, 100876 Beijing, China.
Discov Med. 2023 Aug;35(177):623-631. doi: 10.24976/Discov.Med.202335177.61.
Prompt recognition of patients predisposed to acute kidney injury (AKI) within 72 hours of intensive care unit (ICU) admission holds significant clinical importance as it can considerably lower mortality rates. However, existing AKI prediction models often require complex data collection yet yield only moderate performance. This study aims to develop a straightforward and efficient AKI prediction model, providing ICU physicians with a powerful tool to expedite the detection of AKI patients.
This study proposed a novel generative adversarial imputation networks-least absolute shrinkage and selection operator-extreme gradient boosting (Gain-Lasso-XGBoost) framework and developed an AKI prediction model on the basis of the medical information mart for intensive care (MIMIC-III) database. All the steps, including data preprocessing, feature selection, development, and optimization of prediction models, are organically integrated into the framework which has strong scalability. To compare the performance of our model with current models, we conducted a systematic review to collect all studies on the basis of the MIMIC-III database with similar objectives.
From 15 demographic and clinical variables, 8 features and 5 features were identified as the optimal group of features and processed into the model development. The model optimization further improved the performance of our proposed framework, and the area under curve (AUC) results with 8 and 5 feature vectors achieved 0.849 and 0.830, respectively. Compared with other studies, our method extracted only 8 or 5 feature vectors and obtained superior performance, with an average AUC 1.9% higher than the state-of-the-art approaches in the same type.
Our study suggested that the onset of AKI be effectively and quickly predicted using simplified features, and not just for more specific patient groups. It may help clinicians accurately identify patients at risk of AKI after ICU admission and provide timely monitoring and treatment.
在重症监护病房（ICU）入院后 72 小时内及时识别易发生急性肾损伤（AKI）的患者具有重要的临床意义，因为这可以大大降低死亡率。然而，现有的 AKI 预测模型通常需要复杂的数据收集，且性能仅为中等。本研究旨在开发一种简单而高效的 AKI 预测模型，为 ICU 医生提供一种快速检测 AKI 患者的有力工具。
本研究提出了一种新颖的生成对抗补全网络-最小绝对值收缩和选择算子-极端梯度提升（Gain-Lasso-XGBoost）框架，并基于医疗信息集市重症监护（MIMIC-III）数据库开发了 AKI 预测模型。所有步骤，包括数据预处理、特征选择、模型开发和优化，都有机地集成到该框架中，该框架具有很强的可扩展性。为了将我们的模型与现有模型的性能进行比较，我们进行了系统评价，基于 MIMIC-III 数据库收集了所有具有类似目标的研究。
从 15 个人口统计学和临床变量中，确定了 8 个特征和 5 个特征作为最佳特征组，并将其输入模型开发。模型优化进一步提高了我们提出的框架的性能，使用 8 个和 5 个特征向量的曲线下面积（AUC）结果分别为 0.849 和 0.830。与其他研究相比，我们的方法仅提取了 8 个或 5 个特征向量，却获得了更好的性能，在同一类型的方法中，平均 AUC 比最先进的方法高 1.9%。
本研究表明，使用简化的特征可以有效地快速预测 AKI 的发生，而不仅仅是针对更特定的患者群体。它可以帮助临床医生在 ICU 入院后准确识别发生 AKI 的风险患者，并提供及时的监测和治疗。
