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

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 +5 more·2019·Nature Medicine

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.

Integrity signals

Registry

Crossref · 10.1038/s41591-018-0239-8

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ravizza et al. employed a data-driven feature selection strategy to identify seven key predictors, including age, BMI, eGFR, creatinine, glucose, albumin, and HbA1c, for CKD prediction with large-scale EHR data.

    “Ravizza et al. [74] employed a data-driven feature selection strategy to identify seven key predictors, including age, BMI, eGFR, creatinine, glucose, albumin, and HbA1c, for chronic kidney disease (CKD) prediction with large-scale EHR data.”
  • A partnership among Roche Diabetes Care GmbH, IBM, Eli Lilly and Company, the Regenstrief Institute, and the Indiana Biosciences Research Institute developed a predictive algorithm identifying patients at risk for chronic kidney disease.

    “For example, a partnership among Roche Diabetes Care GmbH, IBM, Eli Lilly and Company, the Regenstrief Institute, and the Indiana Biosciences Research Institute developed a predictive algorithm identifying patients at risk for chronic kidney disease [43].”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

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
Venue
Nature Medicine
Published
2019-01
Volume
25
Issue
1
Pages
57-59
ISSN
  • 1078-8956 (ISSN-L)
  • 1546-170X
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review