Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
Department of Renal Medicine, Aarhus University Hospital, Aarhus, Denmark.
Clin J Am Soc Nephrol. 2021 Apr 7;16(4):543-551. doi: 10.2215/CJN.15691020. Epub 2021 Mar 11.
Despite CKD consensus definitions, epidemiologic studies use multiple different algorithms to identify CKD. We aimed to elucidate if this affects the patient characteristics and the estimated prevalence and prognosis of CKD by applying six different algorithms to identify CKD in population-based medical databases and compare the cohorts.
DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Patients with CKD in Northern Denmark (2009-2016) were identified using six different algorithms: five were laboratory based defined by () one measured outpatient eGFR <60 ml/min per 1.73 m (, =103,435), () two such findings ≥90 days apart (Kidney Disease Improving Global Outcomes, =84,688), () two such findings ≥90 days apart with no eGFR >60 ml/min per 1.73 m observed in-between (Kidney Disease Improving Global Outcomes, , =68,994), () two such findings ≥90 and <365 days apart (Kidney Disease Improving Global Outcomes, , =75,031), and () two eGFRs <60 ml/min per 1.73 m or two urine albumin-creatinine ratios >30 mg/g ≥90 days apart Kidney Disease Improving Global Outcomes, =100,957). The sixth included patients identified by reported in- and outpatient hospital International Classification of Diseases diagnoses of CKD (, =27,947). For each cohort, we estimated baseline eGFR, CKD prevalence, and 1-year mortality using the Kaplan-Meier method.
The five different laboratory-based algorithms resulted in large differences in the estimated prevalence of CKD from 4637-8327 per 100,000 population. In contrast, 1-year mortality varied only slightly (7%-9%). Baseline eGFR levels at diagnosis were comparable (53-56 ml/min per 1.73 m), whereas median time since first recorded eGFR <60 ml/min per 1.73 m varied from 0 months () to 17 months (Kidney Disease Improving Global Outcomes, ). The algorithm yielded markedly lower CKD prevalence (775 per 100,000 population), a lower baseline eGFR (47 ml/min per 1.73 m), longer time since first eGFR <60 ml/min per 1.73 m (median 70 months), and much higher 1-year mortality (22%).
Population prevalence of CKD identified in medical databases greatly depends on the applied algorithm to define CKD. Despite these differences, laboratory-based algorithms produce cohorts with similar prognosis.
This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_03_11_CJN15691020_final.mp3.
尽管有 CKD 共识定义，但流行病学研究使用多种不同的算法来识别 CKD。我们旨在阐明，如果将六种不同的算法应用于基于人群的医疗数据库以识别 CKD，并比较队列，这是否会影响患者特征以及 CKD 的估计患病率和预后。
在丹麦北部（2009-2016 年），使用六种不同的算法识别 CKD 患者：五种是基于实验室的，定义为（）一种测量门诊 eGFR <60 ml/min per 1.73 m（，=103,435），（）两种发现相隔 90 天以上（肾脏病改善全球结局，=84,688），（）两种发现相隔 90 天以上，其间无 eGFR >60 ml/min per 1.73 m 观察到（肾脏病改善全球结局，，=68,994），（）两种发现相隔 90 至 365 天（肾脏病改善全球结局，，=75,031），和（）两种 eGFR <60 ml/min per 1.73 m 或两种尿白蛋白-肌酐比值 >30 mg/g 相隔 90 天以上肾脏病改善全球结局，=100,957）。第六种包括通过报告的门诊和住院国际疾病分类 CKD 诊断识别的患者（，=27,947）。对于每个队列，我们使用 Kaplan-Meier 方法估计基线 eGFR、CKD 患病率和 1 年死亡率。
五种不同的基于实验室的算法导致估计的 CKD 患病率从每 100,000 人口 4637-8327 人之间存在很大差异。相比之下，1 年死亡率仅略有差异（7%-9%）。诊断时的基线 eGFR 水平相当（53-56 ml/min per 1.73 m），而首次记录的 eGFR <60 ml/min per 1.73 m 后中位时间则有所不同（0 个月（）至 17 个月（肾脏病改善全球结局，））。算法得出的 CKD 患病率明显较低（775 人/100,000 人口），基线 eGFR 较低（47 ml/min per 1.73 m），首次 eGFR <60 ml/min per 1.73 m 后时间较长（中位数 70 个月），1 年死亡率较高（22%）。
在医疗数据库中识别的 CKD 人群患病率在很大程度上取决于用于定义 CKD 的应用算法。尽管存在这些差异，但基于实验室的算法产生的队列具有相似的预后。
