A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease

Author:

Tokita Joji1,Lam David1,Vega Aida1,Wang Stephanie1,Amoruso Leonard1,Muller Tamara1,Naik Nidhi1,Rathi Shivani1,Martin Sharlene1,Zabetian Azadeh2,Liu Catherine1,Sinfield Catherine1,McNicholas Tony2,Fleming Fergus2,Coca Steven G.1,Nadkarni Girish N1,Tun Roger2,Kattan Mike3,Donovan Michael J.12ORCID,Rahim Arshad K.1

Affiliation:

1. Icahn School of Medicine at Mount Sinai, New York, NY, USA

2. Renalytix AI, Inc., New York, NY, USA

3. Cleveland Clinic, Cleveland, OH, USA

Abstract

Introduction/Objective: The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to predict a patient’s risk for a progressive decline in kidney function over 5 years. We report the 1-year pre- and post-test clinical impact on care management, eGFR slope, and A1C along with engagement of population health clinical pharmacists and patient coordinators to promote a program of sustainable kidney, metabolic, and cardiac health. Methods: The KidneyIntelX in vitro prognostic test was previously validated for patients with type 2 diabetes and diabetic kidney disease (DKD) to predict kidney function decline within 5 years was introduced into the RWE study (NCT04802395) across the Health System as part of a population health chronic disease management program from [November 2020 to April 2023]. Pre- and post-test patients with a minimum of 12 months of follow-up post KidneyIntelX were assessed across all aspects of the program. Results: A total of 5348 patients with DKD had a KidneyIntelX assay. The median age was 68 years old, 52% were female, 27% self-identified as Black, and 89% had hypertension. The median baseline eGFR was 62 ml/min/1.73 m2, urine albumin-creatinine ratio was 54 mg/g, and A1C was 7.3%. The KidneyIntelX risk level was low in 49%, intermediate in 40%, and high in 11% of cases. New prescriptions for SGLT2i, GLP-1 RA, or referral to a specialist were noted in 19%, 33%, and 43% among low-, intermediate-, and high-risk patients, respectively. The median A1C decreased from 8.2% pre-test to 7.5% post-test in the high-risk group ( P < .001). UACR levels in the intermediate-risk patients with albuminuria were reduced by 20%, and in a subgroup treated with new scripts for SGLT2i, UACR levels were lowered by approximately 50%. The median eGFR slope improved from −7.08 ml/min/1.73 m2/year to −4.27 ml/min/1.73 m2/year in high-risk patients ( P = .0003), −2.65 to −1.04 in intermediate risk, and −3.26 ml/min/1.73 m2/year to +0.45 ml/min/1.73 m2/year in patients with low-risk ( P < .001). Conclusions: Deployment and risk stratification by KidneyIntelX was associated with an escalation in action taken to optimize cardio-kidney-metabolic health including medications and specialist referrals. Glycemic control and kidney function trajectories improved post-KidneyIntelX testing, with the greatest improvements observed in those scored as high-risk.

Funder

Renalytix AI, Inc

Publisher

SAGE Publications

Reference31 articles.

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