Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
复制标题
DOI：
10.1038/s41591-018-0239-8
复制
发表时间：
2019-01-01
期刊：
影响因子：
82.9
通讯作者：
Petrich, Wolfgang
中科院分区：
文献类型：
--
作者：
Ravizza, Stefan;Huschto, Tony;Petrich, Wolfgang
诊断程序、治疗建议和医疗风险分层均基于专门的、严格控制的临床试验。然而，现实世界中存在大量的医疗数据，数据量的增加是以牺牲完整性、统一性和控制性为代价的。在这里，逐个案例的比较表明，我们基于现实世界数据的模型对糖尿病相关慢性肾病的预测能力优于已发布的算法，这些算法源自临床研究数据。
Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.