Visual and Data Analytics Lab, Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia.
University of New South Wales, South West Sydney Local Health District, Sydney, New South Wales, Australia.
Intern Med J. 2021 Aug;51(8):1278-1285. doi: 10.1111/imj.14924.
Chronic kidney disease (CKD) causes a significant health burden in Australia, and up to 50% of Australians with CKD remain undiagnosed.
To estimate the 5-year risk for CKD from general practice (GP) clinical records and to investigate the spatial variation and hot spots of CKD risk in an Australian community.
A cross-sectional study was designed using de-identified GP clinical data recorded from 2010 to 2015. A total of 16 GP participated in this study from West Adelaide, Australia. We used health records of 36 565 patients aged 35-74 years, with no prior history of CKD. The 5-year estimated CKD risk was calculated using the QKidney algorithm. Individuals' risk score was aggregated to Statistical Area Level 1 to predict the community CKD risk. A spatial hotspot analysis was applied to identify the communities with greater risk.
The mean estimated 5-year risk for CKD in the sample population was 0.95% (0.93-0.97). Overall, 2.4% of the study population was at high risk of CKD. Significant hot spots and cold spots of CKD risk were identified within the study region. Hot spots were associated with lower socioeconomic status.
This study demonstrated a new approach to explore the spatial variation of CKD risk at a community level, and implementation of a risk prediction model into a clinical setting may aid in early detection and increase disease awareness in regions of unmet CKD care.
慢性肾脏病（CKD）在澳大利亚造成了巨大的健康负担，多达 50%的 CKD 患者未被诊断。
从全科医生（GP）临床记录中估计 CKD 的 5 年风险，并调查澳大利亚社区 CKD 风险的空间变化和热点。
本研究采用了从 2010 年至 2015 年记录的去识别 GP 临床数据的横断面研究设计。来自澳大利亚阿德莱德西部的 16 家全科医生参与了这项研究。我们使用了 36565 名年龄在 35-74 岁、无 CKD 既往史的患者的健康记录。使用 QKidney 算法计算 5 年估计 CKD 风险。将个体的风险评分汇总到统计区域一级，以预测社区 CKD 风险。应用空间热点分析来识别风险较高的社区。
在样本人群中，估计的 5 年 CKD 风险平均值为 0.95%（0.93-0.97）。总体而言，2.4%的研究人群有较高的 CKD 风险。在研究区域内发现了 CKD 风险的显著热点和冷点。热点与较低的社会经济地位有关。
本研究展示了一种新的方法来探索社区层面 CKD 风险的空间变化，将风险预测模型应用于临床环境可能有助于在未满足 CKD 护理需求的地区早期发现并提高疾病意识。
