Evidence map›Paper›PMID 42337657›Full record

ArticleCost effectiveness and resource allocation : C/E2026

Cost-effectiveness analysis of a prognostic risk assessment for early-stage 1-3b diabetic kidney disease patients in the United States.

Jacie T Cooper, John E Schneider, Thomas Mclain, Steven Coca, Michael J Donovan

Abstract read
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Article in Cost effectiveness and resource allocation : C/E, 2026. 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

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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

5 authors.

Jacie T CooperAvalon Health Economics, Miami, FL, USA. jacie.cooper@avalonecon.com.ORCID http://orcid.org/0000-0001-8456-7589
John E SchneiderAvalon Health Economics, Miami, FL, USA.ORCID http://orcid.org/0000-0003-1878-8118
Thomas MclainRenalytix, New York, NY, USA.ORCID http://orcid.org/0009-0008-4014-8073
Steven CocaRenalytix, Mount Sinai, New York, USA.ORCID http://orcid.org/0000-0002-0928-9168
Michael J DonovanRenalytix, New York, NY, USA.ORCID http://orcid.org/0000-0002-0102-8684

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) accounts for 44% of new chronic kidney disease cases and is associated with significant morbidity and mortality. Although sodium-glucose co-transporter-2 inhibitors (SGLT2is) can reduce cardiorenal outcomes, current DKD staging relying on eGFR and albuminuria (KDIGO risk categories) leaves SGLT2is underutilized in clinical practice. A new biomarker-enriched, AI-enabled risk score (KidneyIntelX™; Renalytix, Inc.) was developed to predict a progressive decline in kidney function in patients with early-stage DKD. KidneyIntelX categorizes patients as low, intermediate, or high risk for disease progression, which can guide resource utilization, prescribing of drugs such as SGLT2is, and improvements in efficiency of care. We report a cost-effectiveness analysis comparing DKD patient stratification with KidneyIntelX to KDIGO by generating an incremental cost effectiveness ratio (ICER).

methodsThe model adopted a U.S. Medicare perspective and consisted of patients with DKD in stages G1-3b using KidneyIntelX or KDIGO. A 10-state Markov state transition structure was employed over a lifetime horizon which includes DKD stages 1-5, dialysis, kidney transplant, cardiovascular (CV) death, and non-CV death. Transition probabilities and risk group distributions for KidneyIntelX and KDIGO were sourced from a KidneyIntelX validation study. Evaluation resulting from KidneyIntelX or KDIGO informed SGLT2i use in the model. Cost inputs included testing, medications, and office visit costs, as well as annual costs for each DKD stage, dialysis, and kidney transplant. Quality of life for each disease state was captured as utility values informed by literature.

resultsThe modeled use of SGLT2i directed by KidneyIntelX led to a reduction in kidney disease progression (ESKD) and CV events, as well as dialysis starts, dialysis crashes, and kidney transplants compared to KDIGO-guided treatment. KidneyIntelX patients also spent less time in DKD stages 4 and 5 and more time in 1 through 3b. KidneyIntelX led to cost savings of about $514 per patient and quality-adjusted life year (QALY) gains of 0.028, resulting in a negative ICER. Combining SGLT2i with MRA's further increased per patient cost savings to $530 for Medicare participants.

conclusionsWide deployment of KidneyIntelX in Medicare patients with DKD G1-3b is expected to be cost-effective compared to KDIGO from the Medicare perspective, with a negative, dominant ICER.

Indexed as

BiomarkerChronic kidney diseaseCost effectivenessDiabetic kidney diseaseKidney functionSodium-glucose co-transporter-2 inhibitors

Identifiers

PMID42337657
PMCPMC13548659

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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.