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Background <p>Diabetic 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).</p> Methods <p>The 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.</p> Results <p>The 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.</p> Conclusions <p>Wide 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.</p>

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

  • 期刊论文

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  • 发表日期：

    2026年6月24日

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

摘要

Background

Diabetic 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).

Methods

The 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.

Results

The 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.

Conclusions

Wide 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.