Evidence map›Paper›PMID 42820039›Full record

ArticleKidney medicine2026

Risk of Progression and Costs of Care for Patients With 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 5 more

Abstract read
In one paragraph

Article in Kidney medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Navdeep TangriDepartment of Community Health Services, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Rakesh SinghBayer U.S. LLC, Whippany, NJ.
Keith A BettsAnalysis Group, Inc, Los Angeles, CA.
Yuxian DuBayer U.S. LLC, Whippany, NJ.
Sophie GaoAnalysis Group, Inc, Los Angeles, CA.
Arvind KattaBayer U.S. LLC, Whippany, NJ.
Youssef M K FaragBayer U.S. LLC, Whippany, NJ.
Samuel T FatobaBayer U.S. LLC, Whippany, NJ.
Hongjiao LiuAnalysis Group, Inc, Los Angeles, CA.
Jingyi ChenAnalysis Group, Inc, Boston, MA.
Thomas FergusonDepartment of Community Health Services, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Reid WhitlockDepartment of Community Health Services, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Ryan J BamforthDepartment of Community Health Services, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Silvia J LeonDepartment of Community Health Services, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada.
Ajay K SinghRenal Division, Brigham and Women's Hospital; Harvard Medical School, Boston, MA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rationale & Objective: Chronic kidney disease (CKD) is associated with substantial economic burden. Early identification of high-risk patients with CKD can facilitate timely intervention and reduce CKD-related medical costs. This study assessed the association between CKD progression risk and health care burden. Study Design: Retrospective observational analysis. Setting and Participants: 1,050,552 adult US patients in Optum's electronic health records database between January 1, 2007 and September 30, 2022. Exposures: Risk of CKD progression as determined by a laboratory-based machine learning model. Outcomes: All-cause health care resource utilization and medical costs. Analytical Approach: A previously published and validated machine learning model, Klinrisk, was applied to classify patients into 3 groups based on their risk of CKD progression (low, medium, and high). All-cause inpatient admissions, emergency room (ER) visits, and outpatient visits were evaluated in each risk group for the 1 year after CKD diagnosis. Average medical costs (2023 US Dollars) were calculated as the average length of stay for inpatient admissions and average number of ER and outpatient visits multiplied by the corresponding unit costs as estimated from a prior study. Results: Patients with higher predicted CKD progression risk had higher health care burden. High-risk patients averaged 1.49 inpatient admissions, 0.83 ER visits, and 35.75 outpatient visits per year compared with 0.31 inpatient admissions, 0.63 ER visits, and 24.45 outpatient visits among low-risk patients. The total annual medical costs for low-, medium-, and high-risk patients were $16,018, $22,090, and $50,218, respectively. Limitations: Our analysis only considered direct medical costs to the payer and omitted indirect costs (eg, transportation, capital, health records). The Klinrisk model only predicts CKD progression, not cardiovascular disease outcomes. Conclusions: Patients at high risk of CKD progression as predicted by the Klinrisk model had higher health care resource utilization and medical costs and may benefit from medical intervention and care management to reduce economic burden.

Indexed as

Chronic kidney diseaseCKD progressioneconomic outcomeshealth care costshealth care resource utilizationrisk prediction

Identifiers

PMID42820039
PMCPMC13625882

What OpenQuestion holds

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Registered trials

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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.