A Real-World Precision Medicine Program Including the KidneyIntelX Test Effectively Changes Management Decisions and Outcomes for Patients With Early-Stage Diabetic Kidney Disease
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Published:2024-01
Issue:
Volume:15
Page:
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ISSN:2150-1319
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Container-title:Journal of Primary Care & Community Health
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language:en
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Short-container-title:J Prim Care Community Health
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.
Publisher
SAGE Publications
Reference31 articles.
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