The need for risk stratification in type 2 diabetes and chronic kidney disease: Proposed clinical value of KidneyIntelX
Abstract Chronic kidney disease (CKD) develops in > 40% of people living with diabetes and affects > 7 million people in the United States. Of the 15 million individuals with type 2 diabetes and CKD in the United States, > 90% are in the “early stages of CKD” (stages G1–G3). Standard risk stratification tools for progression of kidney disease have limitations, and lack precision at an individual level. Individualized risk tools, such as KidneyIntelX™, that incorporate well-validated prognostic protein biomarkers integrated with key clinical variables and are integrated into the electronic health record (EHR) can help address these challenges. KidneyIntelX can identify patients earlier in their disease course when intervention would be most impactful. Herein, 4 case studies are presented to demonstrate how 3 different physicians utilized KidneyIntelX to make clinical decisions and optimize the management of patients with type 2 diabetes and CKD..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
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| Enthalten in: |
Diabetic Nephropathy - 3(2023), 1 vom: 01. März, Seite 1-9 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Basina, Marina [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Anmerkungen: |
© 2023 Marina Basina et al., published by Sciendo |
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| doi: |
10.2478/dine-2022-0019 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract Chronic kidney disease (CKD) develops in > 40% of people living with diabetes and affects > 7 million people in the United States. Of the 15 million individuals with type 2 diabetes and CKD in the United States, > 90% are in the “early stages of CKD” (stages G1–G3). Standard risk stratification tools for progression of kidney disease have limitations, and lack precision at an individual level. Individualized risk tools, such as KidneyIntelX™, that incorporate well-validated prognostic protein biomarkers integrated with key clinical variables and are integrated into the electronic health record (EHR) can help address these challenges. KidneyIntelX can identify patients earlier in their disease course when intervention would be most impactful. Herein, 4 case studies are presented to demonstrate how 3 different physicians utilized KidneyIntelX to make clinical decisions and optimize the management of patients with type 2 diabetes and CKD. | ||
| 700 | 1 | |a McLaughlin, Tracey L. |4 aut | |
| 700 | 1 | |a Tokita, Joji |4 aut | |
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