Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data

被引：117

作者：

Ravizza, Stefan ^{[1
]}

Huschto, Tony ^{[2
]}

Adamov, Anja ^{[1
]}

Boehm, Lars ^{[1
]}

Buesser, Alexander ^{[1
]}

Floether, Frederik F. ^{[1
]}

Hinzmann, Rolf ^{[2
]}

Konig, Helena ^{[2
]}

McAhren, Scott M. ^{[3
]}

Robertson, Daniel H. ^{[4
]}

Schleyer, Titus ^{[5
]}

Schneidinger, Bernd ^{[2
]}

Petrich, Wolfgang ^{[2
]}

机构：

[1] IBM Switzerland Ltd, Zurich, Switzerland

[2] Roche Diabet Care GmbH, Mannheim, Germany

[3] Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA

[4] Indiana Biosci Res Inst, Indianapolis, IN USA

[5] Regenstrief Inst Inc, Indianapolis, IN USA

来源：

NATURE MEDICINE | 2019年 / 25卷 / 01期

关键词：

BIG DATA; HEALTH; OUTCOMES; CARE;

D O I：

10.1038/s41591-018-0239-8

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：57 / +

页数：5

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