Sawhney Simon, Fluck Nick, Marks Angharad, Prescott Gordon, Simpson William, Tomlinson Laurie, Black Corri
Division of Applied Renal Research Collaboration, University of Aberdeen, Aberdeen, UK NHS Grampian, Aberdeen, UK.
NHS Grampian, Aberdeen, UK.
Nephrol Dial Transplant. 2015 Nov;30(11):1853-61. doi: 10.1093/ndt/gfv094. Epub 2015 Apr 28.
Early detection of acute kidney injury (AKI) is important for safe clinical practice. NHS England is implementing a nationwide automated AKI detection system based on changes in blood creatinine. Little has been reported on the similarities and differences of AKI patients detected by this algorithm and other definitions of AKI in the literature.
We assessed the NHS England AKI algorithm and other definitions using routine biochemistry in our own health authority in Scotland in 2003 (adult population 438 332). Linked hospital episode codes (ICD-10) were used to identify patients where AKI was a major clinical diagnosis. We compared how well the algorithm detected this subset of AKI patients in comparison to other definitions of AKI. We also evaluated the potential 'alert burden' from using the NHS England algorithm in comparison to other AKI definitions.
Of 127 851 patients with at least one blood test in 2003, the NHS England AKI algorithm identified 5565 patients. The combined NHS England algorithm criteria detected 91.2% (87.6-94.0) of patients who had an ICD-10 AKI code and this was better than any individual AKI definition. Some of those not captured could be identified by algorithm modifications to identify AKI in retrospect after recovery, but this would not be practical in real-time. Any modifications also increased the number of alerted patients (2-fold in the most sensitive model).
The NHS England AKI algorithm performs well as a diagnostic adjunct in clinical practice. In those without baseline data, AKI may only be seen in biochemistry in retrospect, therefore proactive clinical care remains essential. An alternative algorithm could increase the diagnostic sensitivity, but this would also produce a much greater burden of patient alerts.
急性肾损伤（AKI）的早期检测对安全的临床实践至关重要。英国国民医疗服务体系（NHS England）正在基于血肌酐变化实施一项全国性的自动AKI检测系统。关于该算法检测出的AKI患者与文献中其他AKI定义之间的异同，鲜有报道。
2003年，我们在苏格兰自己的卫生部门使用常规生化方法评估了NHS England的AKI算法及其他定义（成年人口438332）。通过关联医院病历编码（ICD - 10）来识别AKI为主要临床诊断的患者。我们比较了该算法与其他AKI定义相比，检测这一AKI患者子集的效果如何。我们还评估了与其他AKI定义相比，使用NHS England算法可能产生的“警报负担”。
在2003年至少进行过一次血液检测的127851名患者中，NHS England的AKI算法识别出5565名患者。NHS England算法的综合标准检测出了91.2%（87.6 - 94.0）具有ICD - 10 AKI编码的患者，这比任何单一的AKI定义都要好。一些未被检测到的患者可以通过算法修改来识别，以便在恢复后回顾性地诊断AKI，但这在实时情况下并不实用。任何修改也会增加警报患者的数量（在最敏感的模型中增加了两倍）。
NHS England的AKI算法在临床实践中作为诊断辅助手段表现良好。在没有基线数据的情况下，AKI可能只能在回顾性的生化检查中发现，因此积极的临床护理仍然至关重要。一种替代算法可以提高诊断敏感性，但这也会产生更大的患者警报负担。
