Cost-Effectiveness Analysis of Artificial Intelligence-Driven Risk Stratification in Patients With Diabetic Kidney Disease in the US Veterans Population

Rationale & Objective: Efficient risk stratification is essential to optimize care and allocate resources for treatment of diabetic kidney disease (DKD). This study evaluates the cost-effectiveness of an artificial intelligence-driven in vitro kidney disease risk assay (AIKD). Study Design: Cost-effectiveness analysis using a hybrid model, combining a decision tree followed by a Markov model. Setting & Population: Patients with early-stage DKD receiving care within the US Veterans Health Administration health care system. Interventions: Risk stratification using AIKD versus Kidney Disease: Improving Global Outcomes (KDIGO) standard of care (SoC). Outcomes: Five-year health care costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Model Perspective & Timeframe: The decision tree delineated clinical pathways based on the prevalence of progressive decline in kidney function and risk stratification performance of AIKD versus KDIGO. The subsequent Markov model simulated DKD stage transitions across the underlying risk-treatment pathways. Model inputs included test performance characteristics, risk prevalence, transition probabilities, costs, and utilities. One-way and probabilistic sensitivity analyses assessed uncertainty. The analysis was conducted from the perspective of the Veterans Health Administration health care system over a 5-year time horizon. Results: AIKD-guided care resulted in a total cost of $146,437 and 2.8277 QALYs, compared with $145,120 and 2.8164 QALYs for the SoC arm. The ICER for AIKD relative to SoC was $116,349 per QALY gained. One-way sensitivity analysis showed that the sensitivity and specificity of AIKD and SoC, as well as the prevalence of underlying risk of progressive decline in kidney function, were the most influential inputs affecting the ICER. From the probabilistic sensitivity analysis, AIKD has 69% likelihood of being accepted at the conventional willingness-to-pay threshold of $150,000 per QALY gained. Limitations: Model assumptions regarding risk stratification performance and long-term treatment effects may limit generalizability. Conclusions: AIKD is cost-effective compared to KDIGO for patients with early-stage DKD. Its adoption could improve health outcomes and support efficient health care resource utilization management.