Výsledky vyhledávání - "Fergus Fleming"

Akademický článek

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

Publikováno v: Journal of Primary Care & Community Health, Vol 15 (2024)

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

Externí odkaz: https://doaj.org/article/096b358f11364f89bec96d7956413255

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Akademický článek

Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes

Autor: Joji Tokita, Aida Vega, Catherine Sinfield, Nidhi Naik, Shivani Rathi, Sharlene Martin, Stephanie Wang, Leonard Amoruso, Azadeh Zabetian, Steven G. Coca, Girish N. Nadkarni, Fergus Fleming, Michael J. Donovan, Robert Fields

Publikováno v: Journal of Primary Care & Community Health, Vol 13 (2022)

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 requ

Externí odkaz: https://doaj.org/article/4fbcffdddb4c47dfa6f536c92243acdd

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19-OR: KidneyIntelX Association with Clinical Outcomes in Diabetic Kidney Disease

Autor: GIRISH N. NADKARNI, DIPTI TAKALE, BRUCE NEAL, KENNETH W. MAHAFFEY, YSHAI YAVIN, MICHAEL K. HANSEN, FERGUS FLEMING, HIDDO L. HEERSPINK, STEVEN COCA

Publikováno v: Diabetes. 71

Individuals with diabetic kidney disease (DKD) are at risk for progression, heart failure, and death. We assessed the association of KidneyIntelX, a bioprognostictest validated for DKD progression, with a composite time-to-event endpoint of 57% eGFR

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::105d51a6aa5f0b48e85f668ff1e783a5
https://doi.org/10.2337/db22-19-or

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Derivation and validation of a machine learning risk score using biomarker and electronic patient data to predict progression of diabetic kidney disease

Autor: Michael J. Donovan, Lili Chan, Gohar Mosoyan, Fergus Fleming, Michael W. Kattan, Patricia Connolly, James R. McCullough, Joseph A. Vassalotti, Girish N. Nadkarni, Fadi Salem, Steven G. Coca, Scott M. Damrauer, Barbara Murphy

Publikováno v: Diabetologia

Aim Predicting progression in diabetic kidney disease (DKD) is critical to improving outcomes. We sought to develop/validate a machine-learned, prognostic risk score (KidneyIntelX™) combining electronic health records (EHR) and biomarkers. Methods

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fa43ac7d6eef713c7b3eaeb9236f822
http://europepmc.org/articles/PMC8187208

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A Post Hoc Analysis of KidneyIntelX and Cardiorenal Outcomes in Diabetic Kidney Disease

Autor: Girish N. Nadkarni, Dipti Takale, Bruce Neal, Kenneth W. Mahaffey, Yshai Yavin, Michael K. Hansen, Fergus Fleming, Hiddo J.L. Heerspink, Steven G. Coca

Publikováno v: Kidney360

KidneyIntelX, a bioprognostic test for assessing risk of CKD progression, risk stratified individuals for kidney, heart failure, and death outcomes in the Canagliflozin Cardiovascular Assessment Study. Individuals scored as high risk seemed to derive

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::959e7f75caeda794cb5ebcfb507ef489
https://europepmc.org/articles/PMC9528375/

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Clinical utility of kidneyintelx in early stages of diabetic kidney disease the CANVAS trial

Autor: David Lam, Girish N. Nadkarni, Gohar Mosoyan, Bruce Neal, Kenneth W. Mahaffey, Norman Rosenthal, Michael K. Hansen, Hiddo J.L. Heerspink, Fergus Fleming, Steven G. Coca

Publikováno v: American Journal of Nephrology, 53(1). KARGER

Introduction: KidneyIntelX is a composite risk score, incorporating biomarkers and clinical variables for predicting progression of diabetic kidney disease (DKD). The utility of this score in the context of sodium glucose co-transporter 2 inhibitors

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e350d3d2a669936fce8f61e614c32098
http://hdl.handle.net/10044/1/93754

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Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease

Autor: Michael J. Donovan, Sharon Stapleton, Gohar Mosoyan, Fergus Fleming, Patricia Connolly, Ilya Fligelman, Ya-Chen Tonar

Publikováno v: Clinical Proteomics

Background The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::638849709e8cd396df9c314179427ce9
https://pubmed.ncbi.nlm.nih.gov/34789168

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499-P: Clinical Validation of an AI-Enabled Prognostic Test (KidneyintelX) to Accurately Predict Rapid Kidney Function Decline in Patients with Type 2 Diabetic Kidney Disease

Autor: Lili Chan, Girish N. Nadkarni, Barbara Murphy, Scott M. Damrauer, Fergus Fleming, Fadi Salem, Steven G. Coca, Michael J. Donovan

Publikováno v: Diabetes. 69

The ability to predict rapid kidney function decline (RKFD) in patients with early stages of type 2 diabetic kidney disease (T2DKD) can improve long-term health outcomes through earlier intervention. Our objective was to develop a robust risk score f

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1495e3af0ce0ca0dc8a5498bdefaed7b
https://doi.org/10.2337/db20-499-p

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Prediction of rapid kidney function decline using machine learning combining blood biomarkers and electronic health record data

Autor: Chirag R. Parikh, Michael J. Donovan, Barbara Murphy, Divya A. Verghese, Steven G. Coca, Joseph V. Bonventre, John Cijiang He, John Quackenbush, James R. McCullough, Kinsuk Chauhan, Fergus Fleming, Girish N. Nadkarni

IntroductionIndividuals with type 2 diabetes (T2DM) or the APOL1 high-risk genotype (APOL1) are at increased risk of rapid kidney function decline (RKFD) as compared to the general population. Plasma biomarkers representing inflammatory and kidney in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0adb8886011fcfdacc1a41708d5a6618
https://doi.org/10.1101/587774

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