From the Department of Pathology and Laboratory Medicine (Rashidi, Albahra, Loegering, Tran), University of California, Davis, Sacramento.
The Department of Surgery, University of Cincinnati, Cincinnati, Ohio (Makley).
Arch Pathol Lab Med. 2021 Mar 1;145(3):320-326. doi: 10.5858/arpa.2020-0110-OA.
CONTEXT.—: Delayed recognition of acute kidney injury (AKI) results in poor outcomes in military and civilian burn-trauma care. Poor predictive ability of urine output (UOP) and creatinine contribute to the delayed recognition of AKI.
OBJECTIVE.—: To determine the impact of point-of-care (POC) AKI biomarker enhanced by machine learning (ML) algorithms in burn-injured and trauma patients.
DESIGN.—: We conducted a 2-phased study to develop and validate a novel POC device for measuring neutrophil gelatinase-associated lipocalin (NGAL) and creatinine from blood samples. In phase I, 40 remnant plasma samples were used to evaluate the analytic performance of the POC device. Next, phase II enrolled 125 adults with either burns that were 20% or greater of total body surface area or nonburn trauma with suspicion of AKI for clinical validation. We applied an automated ML approach to develop models predicting AKI, using a combination of NGAL, creatinine, and/or UOP as features.
RESULTS.—: Point-of-care NGAL (mean [SD] bias: 9.8 [38.5] ng/mL, P = .10) and creatinine results (mean [SD] bias: 0.28 [0.30] mg/dL, P = .18) were comparable to the reference method. NGAL was an independent predictor of AKI (odds ratio, 1.6; 95% CI, 0.08-5.20; P = .01). The optimal ML model achieved an accuracy, sensitivity, and specificity of 96%, 92.3%, and 97.7%, respectively, with NGAL, creatinine, and UOP as features. Area under the receiver operator curve was 0.96.
CONCLUSIONS.—: Point-of-care NGAL testing is feasible and produces results comparable to reference methods. Machine learning enhanced the predictive performance of AKI biomarkers including NGAL and was superior to the current techniques.
急性肾损伤（AKI）的延迟识别导致军事和平民烧伤创伤护理的预后不良。尿量（UOP）和肌酐的预测能力差导致 AKI 的延迟识别。
确定通过机器学习（ML）算法增强的即时 AKI 生物标志物在烧伤和创伤患者中的影响。
我们进行了一项两阶段研究，以开发和验证一种用于测量血液样本中中性粒细胞明胶酶相关脂质运载蛋白（NGAL）和肌酐的新型即时检测设备。在第一阶段，使用 40 个剩余血浆样本评估即时检测设备的分析性能。接下来，第二阶段招募了 125 名成年人，他们要么烧伤面积占全身表面积的 20%或以上，要么有非烧伤创伤且怀疑 AKI 需要临床验证。我们应用自动化 ML 方法，使用 NGAL、肌酐和/或 UOP 作为特征来开发预测 AKI 的模型。
即时检测 NGAL（平均[SD]偏差：9.8[38.5]ng/mL，P =.10）和肌酐结果（平均[SD]偏差：0.28[0.30]mg/dL，P =.18）与参考方法相当。NGAL 是 AKI 的独立预测因子（优势比，1.6；95%CI，0.08-5.20；P =.01）。最佳 ML 模型使用 NGAL、肌酐和 UOP 作为特征，分别达到 96%、92.3%和 97.7%的准确性、敏感性和特异性，接受者操作特征曲线下面积为 0.96。
即时检测 NGAL 测试是可行的，其结果与参考方法相当。机器学习增强了 AKI 生物标志物（包括 NGAL）的预测性能，优于当前技术。
