Department of Pathology and Laboratory Medicine, 4400 V Street, Sacramento, CA, 95817, USA.
Division of Burn Surgery, Department of Surgery, 2221 Stockton Blvd., Sacramento, CA, 95817, USA.
Sci Rep. 2020 Jan 14;10(1):205. doi: 10.1038/s41598-019-57083-6.
Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients.
严重烧伤和非烧伤创伤患者有发生急性肾损伤 (AKI) 的风险。本研究旨在评估人工智能 (AI)/机器学习 (ML) 算法的理论性能，这些算法使用新型生物标志物中性粒细胞明胶酶相关脂质运载蛋白 (NGAL)，并结合传统生物标志物如 N 末端 pro B 型利钠肽 (NT-proBNP)、尿量 (UOP) 和血浆肌酐，来增强 AKI 的识别能力。本研究中使用了逻辑回归 (LR)、k-最近邻 (k-NN)、支持向量机 (SVM)、随机森林 (RF) 和深度神经网络 (DNN) 等机器学习方法。与烧伤和非烧伤创伤患者的肾脏病改善全球结局 (KDIGO) 标准相比，AI/ML 算法帮助预测 AKI 的速度提前了 61.8 (32.5) 小时。NGAL 在分析上优于肌酐和 UOP 等传统 AKI 生物标志物。通过 ML，当与 NT-proBNP 或肌酐结合使用时，NGAL 的 AKI 预测能力得到进一步增强。在有风险的烧伤和非烧伤创伤患者中，使用 AI/ML 结合 NGAL 可以加速 AKI 的检测。
