Alavijeh Omid Sadeghi, Bansal Jas, Hadfield Katie, Laing Christopher, Dawnay Anne
Department of Nephrology and Hypertension, Royal Free London NHS Foundation Trust, London, UK.
Nephron. 2017;135(3):189-195. doi: 10.1159/000452928. Epub 2016 Dec 29.
Acute kidney injury (AKI) is often detected late, leading to worse clinical outcomes. In 2012, we pioneered an AKI-alerting system for primary care clinicians (PCCs). We retrospectively analysed the alerts and evaluated PCC satisfaction to assess the feasibility of the system.
The study used a 2-pronged approach. AKI alerts, generated by an algorithm designed by University College London Hospital biochemistry department between June 2012 and June 2014, were analysed to reveal the demographics and outcomes of each patient generating an alert. Second, a survey was sent to all PCCs assessing awareness and satisfaction with the service. Simple statistical methods were applied (mean, median, SD and interquartile range).
One hundred forty-two alerts were generated, of which 101 were genuine. Generally, the patient demographics, AKI stratification and aetiology were in keeping with the inpatient AKI population. Forty-eight percent of cases were referred to the hospital with a median length of stay of 9.9 days. Three-month mortality was 12%. Among PCCs, there was good awareness of the system with most finding it valuable. The key complaints around the system were to do with lack of knowledge of its existence.
Our evaluation has demonstrated that the implementation of AKI alerts in the community is technically feasible, does not result in excessive demand on hospital services, appears to influence PCC behaviour and was perceived overwhelmingly as a useful service by these clinicians. This experience should inform further developments including behavioural interventions (such as clinician alerts) to improve community AKI care.
急性肾损伤（AKI）常常被发现得较晚，从而导致更差的临床结局。2012年，我们为基层医疗临床医生（PCC）开创了一种AKI警报系统。我们对这些警报进行了回顾性分析，并评估了PCC的满意度，以评估该系统的可行性。
该研究采用了双管齐下的方法。对2012年6月至2014年6月期间由伦敦大学学院医院生物化学部设计的算法生成的AKI警报进行分析，以揭示每个产生警报的患者的人口统计学特征和结局。其次，向所有PCC发送了一份调查问卷，评估他们对该服务的认知度和满意度。应用了简单的统计方法（均值、中位数、标准差和四分位间距）。
共生成了142条警报，其中101条是真实的。总体而言，患者的人口统计学特征、AKI分层和病因与住院AKI患者群体相符。48%的病例被转诊至医院，中位住院时间为9.9天。3个月死亡率为12%。在PCC中，对该系统的认知度良好，大多数人认为它很有价值。围绕该系统的主要抱怨与对其存在缺乏了解有关。
我们的评估表明，在社区实施AKI警报在技术上是可行的，不会对医院服务造成过度需求，似乎会影响PCC的行为，并且这些临床医生绝大多数认为这是一项有用的服务。这一经验应为进一步的发展提供参考，包括行为干预（如临床医生警报）以改善社区AKI护理。
