Cited by 168Machine Learning in HealthcareArtificial Intelligence in HealthcareChronic Kidney Disease and Diabetes

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

Ravizza, Stefan, Huschto, Tony, Adamov, Anja, Böhm, Lars, Büsser, Alexander, Flöther, Frederik F., Hinzmann, Rolf, König, Helena, McAhren, Scott M., Robertson, Daniel H., Schleyer, Titus, Schneidinger, Bernd, Petrich, Wolfgang·2019·Nature Medicine

DOI 10.1038/s41591-018-0239-8

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Abstract

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.

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Authors: Ravizza, S., Huschto, T., Adamov, A., Böhm, L., Büsser, A., Flöther, F. F., Hinzmann, R., König, H., McAhren, S. M., Robertson, D. H., Schleyer, T., Schneidinger, B., & Petrich, W.
Affiliations: Roche Diabetes Care GmbH (Germany) (DE)
Eli Lilly (United States) (US)
Indiana Biosciences Research Institute (US)
Regenstrief Institute (US)
Corresponding author: Petrich, Wolfgang
ORCID: Adamov, Anja
Büsser, Alexander
Flöther, Frederik F.
Hinzmann, Rolf
Robertson, Daniel H.
Schleyer, Titus
Petrich, Wolfgang
Venue: Nature Medicine
Publisher: Springer Science and Business Media LLC
Published: 2019-01
Volume: 25
Issue: 1
Pages: 57-59
ISSN: 1078-8956 (ISSN-L)
1546-170X
DOI: 10.1038/s41591-018-0239-8
OpenAlex: W2900940968
Cited by: 167
References: 29
Citations by year: 2026: 14 · 2025: 17 · 2024: 22 · 2023: 18 · 2022: 27 · 2021: 31 · 2020: 28 · 2019: 10
FWCI: 9.72
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Nature Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: Roche Diabetes Care
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
Keywords: chronic kidney disease, diabetes mellitus, early risk prediction, real-world data, electronic health records, risk prediction algorithm, clinical features
MeSH terms: Data Analysis, Algorithms, Diabetes Mellitus, Humans, Prognosis, Sample Size, Area Under Curve, Renal Insufficiency, Chronic

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

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