Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA.
Department of Nephrology, Akron Children's Hospital, Akron, OH, USA.
Pediatr Nephrol. 2024 Aug;39(8):2309-2324. doi: 10.1007/s00467-023-06191-7. Epub 2023 Oct 27.
Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.
急性肾损伤（AKI）对儿科和新生儿患者的短期和长期临床结局有重大影响，在这些人群中，减轻导致 AKI 的途径，并为 AKI 的早期诊断和治疗干预做好准备至关重要。最近，人工智能（AI）通过机器学习（ML）为 AKI 的早期检测/预测提供了更多的预测模型。通过提供风险评分和电子警报的详细和有力证据，本综述概述了 AI 在儿科/新生儿 AKI 中的当前状态的全面和整体的见解。在儿科人群中，包括 XGBoost、逻辑回归、支持向量机、决策树、朴素贝叶斯和风险分层评分（肾绞痛指数（RAI）、即时行动否定的肾毒性（NINJA））在内的 AI 模型已成功使用血清肌酐、尿量和电子健康记录（EHR）警报等变量预测 AKI。同样，在新生儿人群中，使用“Baby NINJA”模型可将肾毒性药物暴露减少 42%，AKI 发生率降低 78%，AKI 天数减少 68%。此外，“STARZ”风险分层 AI 模型在新生儿重症监护病房（NICU）入院后 7 天内对 AKI 的预测能力分别为 AUC 0.93 和验证队列中的 AUC 0.96。许多研究还报告了使用生物标志物预测儿科患者和新生儿 AKI 的优越性。未来的方向包括将 AI 与生物标志物（NGAL、CysC、OPN、IL-18、B2M 等）结合应用于 Labelbox 配置中，以创建更强大、更准确的预测和检测儿科/新生儿 AKI 的模型。
