The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [74]
In the citing paper
“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].”
From Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care (2025) · cites this paper as [43]
Citations
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013) · Wiley Series in Probability and Statistics · doi:10.1002/9781118548387 · Cited by 13,861
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
Measuring the Accuracy of Diagnostic Systems
Swets, J. A. (1988) · Science · doi:10.1126/science.3287615 · Cited by 10,058
The use of the area under the ROC curve in the evaluation of machine learning algorithms
Bradley, A. P. (1997) · Pattern Recognition · doi:10.1016/s0031-3203(96)00142-2 · Cited by 7,217
The ASA Statement on p -Values: Context, Process, and Purpose
Wasserstein, R. L., & Lazar, N. A. (2016) · The American Statistician · doi:10.1080/00031305.2016.1154108 · Cited by 6,306
Purposeful selection of variables in logistic regression
Bursac, Z., Gauss, C. H., Williams, D. K., & Hosmer, D. W. (2008) · Source Code for Biology and Medicine · doi:10.1186/1751-0473-3-17 · Cited by 4,035
Empagliflozin and Progression of Kidney Disease in Type 2 Diabetes
Wanner, C., Inzucchi, S. E., Lachin, J. M., Fitchett, D., von Eynatten, M., Mattheus, M., Johansen, O. E., Woerle, H. J., Broedl, U. C., & Zinman, B. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1515920 · Cited by 3,655
Wu, X., Zhu, X., Wu, G.-Q., & Ding, W. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.109 · Cited by 2,410
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0041 · Cited by 1,235
The big challenges of big data
Marx, V. (2013) · Nature · doi:10.1038/498255a · Cited by 1,233
Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy
Levin, A., Tonelli, M. A., Bonventre, J. V., Coresh, J., Donner, J., Fogo, A. B., Fox, C. S., Gansevoort, R. T., Heerspink, H. J. L., Jardine, M., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)30788-2 · Cited by 961
Evidence for Health Decision Making — Beyond Randomized, Controlled Trials
Frieden, T. R. (2017) · New England Journal of Medicine · doi:10.1056/nejmra1614394 · Cited by 770
The global burden of chronic kidney disease: estimates, variability and pitfalls
Glassock, R. J., Warnock, D. G., & Delanaye, P. (2016) · Nature Reviews Nephrology · doi:10.1038/nrneph.2016.163 · Cited by 522
The Indiana Network For Patient Care: A Working Local Health Information Infrastructure
McDonald, C. J., Overhage, J. M., Barnes, M., Schadow, G., Blevins, L., Dexter, P. R., Mamlin, B., & the INPC Management Committee (2005) · Health Affairs · doi:10.1377/hlthaff.24.5.1214 · Cited by 330
Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors
Razavian, N., Blecker, S., Schmidt, A. M., Smith–McLallen, A., Nigam, S., & Sontag, D. A. (2015) · Big Data · doi:10.1089/big.2015.0020 · Cited by 221
Awareness of Chronic Kidney Disease Among Patients and Providers
Plantinga, L., Tuot, D. S., & Powe, N. R. (2010) · Advances in Chronic Kidney Disease · doi:10.1053/j.ackd.2010.03.002 · Cited by 209
Risk Scores for Predicting Outcomes in Patients with Type 2 Diabetes and Nephropathy
Keane, W. F., Zhang, Z., Lyle, P. A., Cooper, M. E., de Zeeuw, D., Grünfeld, J., Lash, J. P., McGill, J. B., Mitch, W. E., Remuzzi, G., et al. (2006) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.01381005 · Cited by 202
Treatment decisions in multiple sclerosis — insights from real-world observational studies
Trojano, M., Tintoré, M., Montalbán, X., Hillert, J. A., Kalinčík, T., Iaffaldano, P., Spelman, T. D., Sormani, M. P., & Butzkueven, H. (2017) · Nature Reviews Neurology · doi:10.1038/nrneurol.2016.188 · Cited by 190
Bag Constrained Structure Pattern Mining for Multi-Graph Classification
Wu, J. X., Xingquan Zhu, A., Chengqi Zhang, B., & Yu, P. S. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.2297923 · Cited by 170
Risk Prediction for Early CKD in Type 2 Diabetes
Dunkler, D., Gao, P., Lee, S. F., Heinze, G., Clase, C. M., Tobe, S. W., Teo, K. K., Gerstein, H. C., Mann, J. F. E., & Oberbauer, R. W. (2015) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.10321014 · Cited by 136
Prediction of Kidney-Related Outcomes in Patients With Type 2 Diabetes
Jardine, M., Hata, J., Woodward, M., Perkovic, V., Ninomiya, T., Arima, H., Zoungas, S., Cass, A., Patel, A. A., Marre, M., et al. (2012) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2012.04.025 · Cited by 135
Kaelber, D. C., Foster, W., Gilder, J. R., Love, T. E., & Jain, A. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000782 · Cited by 125
Residual microvascular risk in diabetes: unmet needs and future directions
Fioretto, P., Dodson, P. M., Ziegler, D., & Rosenson, R. S. (2009) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2009.213 · Cited by 110
Voosen, P. (2017) · Science · doi:10.1126/science.357.6346.22 · Cited by 101
Progression to microalbuminuria in type 1 diabetes: development and validation of a prediction rule
Vergouwe, Y., Soedamah‐Muthu, S. S., Zgibor, J. C., Chaturvedi, N., Forsblom, C. M., Snell‐Bergeon, J. K., Maahs, D. M., Groop, P., Rewers, M., Orchard, T. J., et al. (2009) · Diabetologia · doi:10.1007/s00125-009-1585-3 · Cited by 69
Big data in biomedicine: 4 big questions
Bender, E. (2015) · Nature · doi:10.1038/527s19a · Cited by 19
Research works printed but not linked: 3
- Liaw, A. & Wiener, M. R News 2, 18–22 (2002).
- Unger, J. & Schwartz, Z. Diabetes Management in Primary Care 2nd edn (Lippincott Williams & Wilkens, Philadelphia, 2013).
- Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction (Springer, New York, 2009).
Cited by
Sorted by citations
Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., Zha, Y., Liang, W., Wang, C., Wang, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.04.045 · Cited by 917
“Recent new and exciting advances in the applications of AI in many healthcare areas (Esteva et al., 2019; Gulshan et al., 2016; Li et al., 2018; Norgeot et al., 2019; Poplin et al., 2018; Ravizza et al., 2019; Ting et al., 2017; Topol, 2019) have inspired innovations in the development of novel AI-based radiological d…”
Rahman, A., Hossain, M. S., Muhammad, G., Kundu, D., Debnath, T., Rahman, M., Khan, M. S. I., Tiwari, P., & Band, S. S. (2023) · Cluster Computing · doi:10.1007/s10586-022-03658-4 · Cited by 399
Deep learning approach for diabetes prediction using PIMA Indian dataset
Naz, H. U., & Ahuja, S. (2020) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-020-00520-5 · Cited by 359
Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00745-6 · Cited by 324
Chronic kidney disease prediction based on machine learning algorithms
Islam, M. A., Majumder, M. Z. H., & Hussein, M. A. (2023) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2023.100189 · Cited by 313
Metadata
- 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
- 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
- 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
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
