London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.
BMC Nephrol. 2023 Aug 9;24(1):234. doi: 10.1186/s12882-023-03269-0.
Acute Kidney Injury (AKI) is a multifactorial condition which presents a substantial burden to healthcare systems. There is limited evidence on whether it is seasonal. We sought to investigate the seasonality of AKI hospitalisations in England and use unsupervised machine learning to explore clustering of underlying comorbidities, to gain insights for future intervention.
We used Hospital Episodes Statistics linked to the Clinical Practice Research Datalink to describe the overall incidence of AKI admissions between 2015 and 2019 weekly by demographic and admission characteristics. We carried out dimension reduction on 850 diagnosis codes using multiple correspondence analysis and applied k-means clustering to classify patients. We phenotype each group based on the dominant characteristics and describe the seasonality of AKI admissions by these different phenotypes.
Between 2015 and 2019, weekly AKI admissions peaked in winter, with additional summer peaks related to periods of extreme heat. Winter seasonality was more evident in those diagnosed with AKI on admission. From the cluster classification we describe six phenotypes of people admitted to hospital with AKI. Among these, seasonality of AKI admissions was observed among people who we described as having a multimorbid phenotype, established risk factor phenotype, and general AKI phenotype.
We demonstrate winter seasonality of AKI admissions in England, particularly among those with AKI diagnosed on admission, suggestive of community triggers. Differences in seasonality between phenotypes suggests some groups may be more likely to develop AKI as a result of these factors. This may be driven by underlying comorbidity profiles or reflect differences in uptake of seasonal interventions such as vaccines.
急性肾损伤 (AKI) 是一种多因素疾病，给医疗保健系统带来了巨大负担。关于它是否具有季节性的证据有限。我们试图调查英格兰 AKI 住院的季节性，并使用无监督机器学习来探索潜在合并症的聚类，为未来的干预措施提供见解。
我们使用医院住院统计数据与临床实践研究数据库相关联，按人口统计学和入院特征每周描述 2015 年至 2019 年 AKI 入院的总体发生率。我们使用多元对应分析对 850 个诊断代码进行降维，并应用 k-均值聚类对患者进行分类。我们根据主要特征对每个组进行表型，并根据这些不同的表型描述 AKI 入院的季节性。
在 2015 年至 2019 年期间，每周 AKI 入院人数在冬季达到高峰，夏季与极端高温时期相关的额外高峰。在入院时诊断为 AKI 的患者中，冬季季节性更为明显。从聚类分类中，我们描述了因 AKI 住院的六种表型人群。在这些人群中，我们描述为患有多种合并症表型、已确立的危险因素表型和一般 AKI 表型的人群中观察到 AKI 入院的季节性。
我们证明了英格兰 AKI 入院的季节性，尤其是在入院时诊断为 AKI 的患者中，这表明存在社区触发因素。不同表型之间季节性的差异表明，某些人群可能由于这些因素更容易发生 AKI。这可能是由潜在的合并症谱驱动的，或者反映了季节性干预措施（如疫苗）的采用差异。
