EPI-PHARE (French National Agency for Medicines and Health Products Safety [ANSM] and French National Health Insurance [CNAM]), Saint-Denis, France; Center for research epidemiology and population health, Radiation epidemiology team, Université Paris-Saclay, Université Paris-Sud, UVSQ, 94805 Villejuif, France.
University Rennes, EHESP, REPERES (Recherche en pharmaco-épidémiologie et recours aux soins)-EA 7449, 35000 Rennes, France.
Nephrol Ther. 2022 Jul;18(4):255-262. doi: 10.1016/j.nephro.2022.03.003. Epub 2022 Jun 27.
Published algorithms for identifying chronic kidney disease in healthcare claims databases have poor performance except in patients with renal replacement therapy. We propose and describe an algorithm to identify all stage chronic kidney disease in a French healthcare claims databases and assessed its performance by using data from the Renal Epidemiology and Information Network registry and the French Childhood Cancer Survivor Study cohort.
A group of experts met several times to define a list of items and combinations of items that could be related to chronic kidney disease. For the French Childhood Cancer Survivor Study cohort, information on confirmed chronic kidney disease cases extracted from medical records was considered the gold standard (KDIGO definition). Sensitivity, specificity, and positive and negative predictive value and kappa coefficients were estimated. The contribution of each component of the algorithm was assessed for 1 and 2 years before the start of renal replacement therapy for confirmed end-stage kidney disease in the Renal Epidemiology and Information Network registry.
The algorithm's sensitivity was 78%, specificity 97.4%, negative predictive value 98.4% and positive predictive value 68.7% in French Childhood Cancer Survivor Study cohort and the kappa coefficient was 0.79 for agreement with the gold standard. The algorithm 93.6% and 55.1% of confirmed incident end-stage kidney disease cases from the Renal Epidemiology and Information Network registry when considering 1 year and 2 years, respectively, before renal replacement therapy start.
The algorithm showed good performance among younger patients and those with end-stage kidney disease in the twol last years prior to renal replacement therapy. Future research will address the ability of the algorithm to detect early chronic kidney disease stages and to classify the severity of chronic kidney disease.
在医疗保健索赔数据库中，用于识别慢性肾脏病的已发表算法除在接受肾脏替代治疗的患者中外，性能都较差。我们提出并描述了一种算法，用于识别法国医疗保健索赔数据库中的所有慢性肾脏病分期，并使用肾脏流行病学和信息网络登记处和法国儿童癌症幸存者研究队列的数据来评估其性能。
一组专家多次开会，定义了一组可能与慢性肾脏病相关的项目和项目组合。对于法国儿童癌症幸存者研究队列，从病历中提取的确诊慢性肾脏病病例的信息被认为是金标准（KDIGO 定义）。估计了敏感性、特异性、阳性和阴性预测值以及kappa 系数。对于肾脏流行病学和信息网络登记处中确诊的终末期肾脏病开始接受肾脏替代治疗前 1 年和 2 年，评估了该算法的每个组成部分的贡献。
该算法在法国儿童癌症幸存者研究队列中的敏感性为 78%，特异性为 97.4%，阴性预测值为 98.4%，阳性预测值为 68.7%，kappa 系数为 0.79，与金标准一致。该算法在考虑开始肾脏替代治疗前 1 年和 2 年时，分别识别出肾脏流行病学和信息网络登记处中 93.6%和 55.1%的确诊的新发终末期肾脏病病例。
该算法在接受肾脏替代治疗前的最后 2 年中，在年轻患者和终末期肾脏病患者中表现出良好的性能。未来的研究将解决该算法检测早期慢性肾脏病阶段和对慢性肾脏病严重程度进行分类的能力。
