Evidence map›Paper›PMID 40996687›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2025

Risk of Progression and Costs of Care for Patients with Type 2 Diabetes and Chronic Kidney Disease.

Navdeep Tangri, Rakesh Singh, Keith A Betts, Yuxian Du, Sophie Gao, Arvind Katta, Youssef M K Farag, Samuel T Fatoba, Hongjiao Liu, Jingyi Chen and 4 more

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Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Navdeep TangriDepartment of Community Health Services, Max Rady College of Medicine, Seven Oaks General Hospital, University of Manitoba, 2LB19 - 2300 McPhillips Street, Winnipeg, MB, R2V 3M3, Canada. ntangri@sogh.mb.ca.
Rakesh SinghBayer U.S. LLC, Whippany, NJ, USA.
Keith A BettsAnalysis Group, Inc, Los Angeles, CA, USA.
Yuxian DuBayer U.S. LLC, Whippany, NJ, USA.
Sophie GaoAnalysis Group, Inc, Los Angeles, CA, USA.
Arvind KattaBayer U.S. LLC, Whippany, NJ, USA.
Youssef M K FaragBayer U.S. LLC, Whippany, NJ, USA.
Samuel T FatobaBayer U.S. LLC, Whippany, NJ, USA.
Hongjiao LiuAnalysis Group, Inc, Los Angeles, CA, USA.
Jingyi ChenAnalysis Group, Inc, Boston, MA, USA.
Thomas FergusonDepartment of Community Health Services, Max Rady College of Medicine, Seven Oaks General Hospital, University of Manitoba, 2LB19 - 2300 McPhillips Street, Winnipeg, MB, R2V 3M3, Canada.
Reid WhitlockDepartment of Community Health Services, Max Rady College of Medicine, Seven Oaks General Hospital, University of Manitoba, 2LB19 - 2300 McPhillips Street, Winnipeg, MB, R2V 3M3, Canada.
Silvia J LeonDepartment of Community Health Services, Max Rady College of Medicine, Seven Oaks General Hospital, University of Manitoba, 2LB19 - 2300 McPhillips Street, Winnipeg, MB, R2V 3M3, Canada.
Ajay K SinghRenal Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic kidney disease (CKD) progression is associated with a significant incremental economic burden. Previous work has demonstrated high accuracy of the laboratory-based machine learning model, Klinrisk, in predicting the risk of CKD progression. We sought to use the Klinrisk model to evaluate the association of risk of CKD progression with healthcare resource utilization (HRU) and costs of care in adults with type 2 diabetes and CKD.

methodsThis retrospective observational study included 413,177 eligible patients from Optum's electronic health records database (1/1/2007-9/30/2022). Patients were classified into low-, medium-, and high-risk groups based on their 2-year risk of CKD progression as predicted by the Klinrisk model. All-cause HRU and medical costs during the 1 year after CKD were estimated for each group.

resultsOf the 413,177 patients included, 110,399 (26.7%) were classified as low-risk of CKD progression, 253,188 (61.3%) as medium-risk, and 49,590 (12.0%) as high-risk. The observed risk of CKD progression at 2 years, 5 years, and 10 years was 18.6%, 36.5%, and 54.1% for high-risk patients, 3.7%, 11.7%, and 26.4% for medium-risk patients, and 1.5%, 5.7%, and 15.8% for low-risk patients, which were similar to the predicted risks of CKD progression. High-risk patients had higher HRU and more than 2-3 times higher costs than lower-risk patients. Inpatient costs were the major cost driver for high-risk patients.

conclusionsThe Klinrisk model accurately identified patients with type 2 diabetes and CKD requiring the most healthcare resources. Such tools can support the identification and targeting of high-risk patients for interventions that may lead to a more cost-effective model of care.

Indexed as

Chronic kidney diseaseCKD progressionEconomic outcomesHealthcare costsHealthcare resource utilizationRisk predictionType 2 diabetes

Identifiers

PMID40996687
PMCPMC12579019

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.