Academic Unit of Primary Care and Population Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, United Kingdom; Anaesthetics Department, Worthing Hospital, Western Sussex Hospitals National Health Service Foundation Trust, Worthing, United Kingdom.
Centre for Kidney Research and Innovation, Division of Medical Sciences and Graduate Entry Medicine, University of Nottingham, Derby, United Kingdom; Department of Renal Medicine, Royal Derby Hospital, Derby, United Kingdom.
Semin Nephrol. 2019 Sep;39(5):421-430. doi: 10.1016/j.semnephrol.2019.06.002.
Acute kidney injury is a major health care problem. Improving recognition of those at risk and highlighting those who have developed AKI at an earlier stage remains a priority for research and clinical practice. Prediction models to risk-stratify patients and electronic alerts for AKI are two approaches that could address previously highlighted shortcomings in management and facilitate timely intervention. We describe and critique available prediction models and the effects of the use of AKI alerts on patient outcomes are reviewed. Finally, the potential for prediction models to enrich population subsets for other diagnostic approaches and potential research, including biomarkers of AKI, are discussed.
急性肾损伤是一个主要的医疗保健问题。提高对高危人群的认识，并尽早发现已经发生 AKI 的患者，仍然是研究和临床实践的重点。风险分层患者的预测模型和 AKI 的电子警报是两种可以解决以前在管理中突出的缺点并促进及时干预的方法。我们描述并评价了现有的预测模型，并回顾了使用 AKI 警报对患者结局的影响。最后，讨论了预测模型在为其他诊断方法和潜在研究（包括 AKI 的生物标志物）丰富人群亚组方面的潜力。
