Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease : Interim Results on Decision Impact and Outcomes
INTRODUCTION AND OBJECTIVE: The lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study.
METHODS: KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes.
RESULTS: A total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m2, urine albumin-creatinine ratio was 69 mg/g, and HbA1c was 7.7%. After testing, a clinical encounter in the first month occurred in 13%, 43%, and 53% of low-risk, intermediate-risk, and high-risk patients, respectively and 46%, 61%, and 71% had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on SGLT2 inhibitors (OR = 4.56; 95% CI 3.00-6.91 vs low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95% CI 1.53-4.01) compared to low-risk patients.
CONCLUSIONS: The combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:13 |
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| Enthalten in: |
Journal of primary care & community health - 13(2022) vom: 01. Jan., Seite 21501319221138196 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Tokita, Joji [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarkers |
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| Anmerkungen: |
Date Completed 22.11.2022 Date Revised 13.12.2022 published: Print Citation Status MEDLINE |
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| doi: |
10.1177/21501319221138196 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM349179654 |
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| 100 | 1 | |a Tokita, Joji |e verfasserin |4 aut | |
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| 500 | |a Date Revised 13.12.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a INTRODUCTION AND OBJECTIVE: The lack of precision to identify patients with early-stage diabetic kidney disease (DKD) at near-term risk for progressive decline in kidney function results in poor disease management often leading to kidney failure requiring unplanned dialysis. The KidneyIntelX is a multiplex, bioprognostic, immunoassay consisting of 3 plasma biomarkers and clinical variables that uses machine learning to generate a risk score for progressive decline in kidney function over 5-year in adults with early-stage DKD. Our objective was to assess the impact of KidneyIntelX on management and outcomes in a Health System in the real-world evidence (RWE) study | ||
| 520 | |a METHODS: KidneyIntelX was introduced into a large metropolitan Health System via a population health-defined approved care pathway for patients with stages 1 to 3 DKD between [November 2020 to March 2022]. Decision impact on visit frequency, medication management, specialist referral, and selected lab values was assessed. We performed an interim analysis in patients through 6-months post-test date to evaluate the impact of risk level with clinical decision-making and outcomes | ||
| 520 | |a RESULTS: A total of 1686 patients were enrolled in the RWE study and underwent KidneyIntelX testing and subsequent care pathway management. The median age was 68 years, 52% were female, 26% self-identified as Black, and 94% had hypertension. The median baseline eGFR was 59 ml/minute/1.73 m2, urine albumin-creatinine ratio was 69 mg/g, and HbA1c was 7.7%. After testing, a clinical encounter in the first month occurred in 13%, 43%, and 53% of low-risk, intermediate-risk, and high-risk patients, respectively and 46%, 61%, and 71% had at least 1 action taken within the first 6 months. High-risk patients were more likely to be placed on SGLT2 inhibitors (OR = 4.56; 95% CI 3.00-6.91 vs low-risk), and more likely to be referred to a specialist such as a nephrologist, endocrinologist, or dietician (OR = 2.49; 95% CI 1.53-4.01) compared to low-risk patients | ||
| 520 | |a CONCLUSIONS: The combination of KidneyIntelX, clinical guidelines and educational support resulted in changes in clinical management by clinicians. After testing, there was an increase in visit frequency, referrals for disease management, and introduction to guideline-recommended medications. These differed by risk category, indicating an impact of KidneyIntelX risk stratification on clinical care | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Real World Evidence | |
| 650 | 4 | |a decision impact study | |
| 650 | 4 | |a diabetic kidney disease | |
| 650 | 4 | |a early-stage | |
| 650 | 7 | |a Sodium-Glucose Transporter 2 Inhibitors |2 NLM | |
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| 700 | 1 | |a Vega, Aida |e verfasserin |4 aut | |
| 700 | 1 | |a Sinfield, Catherine |e verfasserin |4 aut | |
| 700 | 1 | |a Naik, Nidhi |e verfasserin |4 aut | |
| 700 | 1 | |a Rathi, Shivani |e verfasserin |4 aut | |
| 700 | 1 | |a Martin, Sharlene |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Stephanie |e verfasserin |4 aut | |
| 700 | 1 | |a Amoruso, Leonard |e verfasserin |4 aut | |
| 700 | 1 | |a Zabetian, Azadeh |e verfasserin |4 aut | |
| 700 | 1 | |a Coca, Steven G |e verfasserin |4 aut | |
| 700 | 1 | |a Nadkarni, Girish N |e verfasserin |4 aut | |
| 700 | 1 | |a Fleming, Fergus |e verfasserin |4 aut | |
| 700 | 1 | |a Donovan, Michael J |e verfasserin |4 aut | |
| 700 | 1 | |a Fields, Robert |e verfasserin |4 aut | |
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