Payer budget impact of an artificial intelligence in vitro diagnostic to modify diabetic kidney disease progression

Abstract

To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic to predict kidney function decline in Type 2 Diabetic Kidney Disease (T2DKD) patients, stages 1-3b. Materials and MethodsWe developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stage 1-3b T2DKD patients was followed for five years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions. ResultsProjected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year two after adoption. Sensitivity analysis based on assessment of the most conservative definition of progression and a 5% reduction in progression rate attributed to KidneyIntelX, resulted in a projected 5-year savings of $145 million associated with KidneyIntelX. A c c e p t e d M a n u s c r i p t Limitations and ConclusionsLimitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.

Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression

“…Inclusion of time‐to‐event outcomes ensures that clinicians are provided with performance information to support the interpretation of test results. We performed additional analyses on KidneyIntelX which have demonstrated that cost savings are derived from deployment of KidneyIntelX testing for a large population of patients with DKD, compared to the standard of care 24 …”

Artificial Intelligence in the Identification, Management, and Follow-Up of CKD

“…Third, an economic analysis of these models may be overly optimistic because it suggests delay or prevention of 5000 dialysis starts in a hypothetical cohort of 100,000 patients. Given the internal validation cohort has a kidney failure rate of 5% over 5 years, this would suggest that simply providing a risk score from a modestly accurate model would affect every dialysis start (9,10).…”

Risk stratification using Artificial Intelligence: Could it be useful to reduce the burden of chronic kidney disease in low- and middle-income Countries?

“…Machine learning-based predictive models have demonstrated their ability to outperform risk calculators developed using conventional statistical methods for cardiovascular disease events and comorbidities such as diabetes and hypertension (10), demonstrating their potential to improve risk prediction and aid medical decision-making. If early detection of patients with a higher risk of CKD is desired, previous evidence on the implementation of machine learning algorithms to stratify risk of CKD suggests that this could be more efficient and cost-effective than traditional population-based screening methods (9,11,12). Furthermore, risk stratification could be associated with a significant decrease in the number of individuals who required closer monitoring of the glomerular filtration rate (eGFR) and an increase in the proportion of patients for whom a treatment change is indicated.…”

“…For example, a study estimated the budgetary impact in the United States of implementing an AI-based risk stratification system for patients with type 2 diabetic kidney disease (stages 1–3b). The overall result is that the undiscounted savings in the 5-year base case for 100,000 patients tested with the system were $1.052 billion, mainly attributed to slowing disease progression ( 9 ).…”

“…If early detection of patients with a higher risk of CKD is desired, previous evidence on the implementation of machine learning algorithms to stratify risk of CKD suggests that this could be more efficient and cost-effective than traditional population-based screening methods ( 9 , 11 , 12 ). Furthermore, risk stratification could be associated with a significant decrease in the number of individuals who required closer monitoring of the glomerular filtration rate (eGFR) and an increase in the proportion of patients for whom a treatment change is indicated.…”