14 results on '"FLEMING, FERGUS"'
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2. Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease
- Author
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Connolly, Patricia, Stapleton, Sharon, Mosoyan, Gohar, Fligelman, Ilya, Tonar, Ya-Chen, Fleming, Fergus, and Donovan, Michael J.
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- 2021
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3. 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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Tokita, Joji, Lam, David, Vega, Aida, Wang, Stephanie, Amoruso, Leonard, Muller, Tamara, Naik, Nidhi, Rathi, Shivani, Martin, Sharlene, Zabetian, Azadeh, Liu, Catherine, Sinfield, Catherine, McNicholas, Tony, Fleming, Fergus, Coca, Steven G., Nadkarni, Girish N, Tun, Roger, Kattan, Mike, Donovan, Michael J., and Rahim, Arshad K.
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BIOMARKERS ,IN vitro studies ,KIDNEYS ,CONFIDENCE intervals ,MACHINE learning ,TYPE 2 diabetes ,PRE-tests & post-tests ,T-test (Statistics) ,RESEARCH funding ,ODDS ratio ,EARLY diagnosis ,DIABETIC nephropathies - 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 m
2 , 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. [ABSTRACT FROM AUTHOR]- Published
- 2024
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4. Derivation and independent validation of kidneyintelX.dkd: A prognostic test for the assessment of diabetic kidney disease progression.
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Nadkarni, Girish N., Stapleton, Sharon, Takale, Dipti, Edwards, Katherine, Moran, Kara, Mosoyan, Gohar, Hansen, Michael K., Donovan, Michael J., Heerspink, Hiddo J. L., Fleming, Fergus, and Coca, Steven G.
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DIABETIC nephropathies ,PROGNOSTIC tests ,CHRONIC kidney failure ,DISEASE progression ,GLOMERULAR filtration rate ,RANDOM forest algorithms - Abstract
Aims: To develop and validate an updated version of KidneyIntelX (kidneyintelX.dkd) to stratify patients for risk of progression of diabetic kidney disease (DKD) stages 1 to 3, to simplify the test for clinical adoption and support an application to the US Food and Drug Administration regulatory pathway. Methods: We used plasma biomarkers and clinical data from the Penn Medicine Biobank (PMBB) for training, and independent cohorts (BioMe and CANVAS) for validation. The primary outcome was progressive decline in kidney function (PDKF), defined by a ≥40% sustained decline in estimated glomerular filtration rate or end‐stage kidney disease within 5 years of follow‐up. Results: In 573 PMBB participants with DKD, 15.4% experienced PDKF over a median of 3.7 years. We trained a random forest model using biomarkers and clinical variables. Among 657 BioMe participants and 1197 CANVAS participants, 11.7% and 7.5%, respectively, experienced PDKF. Based on training cut‐offs, 57%, 35% and 8% of BioMe participants, and 56%, 38% and 6% of CANVAS participants were classified as having low‐, moderate‐ and high‐risk levels, respectively. The cumulative incidence at these risk levels was 5.9%, 21.2% and 66.9% in BioMe and 6.7%, 13.1% and 59.6% in CANVAS. After clinical risk factor adjustment, the adjusted hazard ratios were 7.7 (95% confidence interval [CI] 3.0‐19.6) and 3.7 (95% CI 2.0‐6.8) in BioMe, and 5.4 (95% CI 2.5‐11.9) and 2.3 (95% CI 1.4‐3.9) in CANVAS, for high‐ versus low‐risk and moderate‐ versus low‐risk levels, respectively. Conclusions: Using two independent cohorts and a clinical trial population, we validated an updated KidneyIntelX test (named kidneyintelX.dkd), which significantly enhanced risk stratification in patients with DKD for PDKF, independently from known risk factors for progression. [ABSTRACT FROM AUTHOR]
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- 2023
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5. The Alps and the Imagination
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Fleming, Fergus
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- 2004
6. Real World Evidence and Clinical Utility of KidneyIntelX on Patients With Early-Stage Diabetic Kidney Disease: Interim Results on Decision Impact and Outcomes.
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Tokita, Joji, Vega, Aida, Sinfield, Catherine, Naik, Nidhi, Rathi, Shivani, Martin, Sharlene, Wang, Stephanie, Amoruso, Leonard, Zabetian, Azadeh, Coca, Steven G., Nadkarni, Girish N., Fleming, Fergus, Donovan, Michael J., and Fields, Robert
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EVALUATION of medical care ,GLOMERULAR filtration rate ,CONFIDENCE intervals ,DECISION making in clinical medicine ,ODDS ratio ,DIABETIC nephropathies - Abstract
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 m
2 , 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. [ABSTRACT FROM AUTHOR]- Published
- 2022
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7. Clinical Utility of KidneyIntelX in Early Stages of Diabetic Kidney Disease in the CANVAS Trial.
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Lam, David, Nadkarni, Girish N., Mosoyan, Gohar, Neal, Bruce, Mahaffey, Kenneth W., Rosenthal, Norman, Hansen, Michael K., Heerspink, Hiddo J.L., Fleming, Fergus, and Coca, Steven G.
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CANAGLIFLOZIN ,DIABETIC nephropathies ,DISEASE risk factors ,TUMOR necrosis factor receptors ,DISEASE progression ,GLOMERULAR filtration rate - Abstract
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 and how changes in the risk score associate with future kidney outcomes are unknown.Methods: We measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30-59 mL/min/1.73 m2 or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. We assessed the association of baseline and changes in KidneyIntelX with subsequent DKD progression (composite outcome of an eGFR decline of ≥5 mL/min/year [using the 6-week eGFR as the baseline in the canagliflozin group], ≥40% sustained decline in the eGFR, or kidney failure).Results: We included 1,325 CANVAS participants with concurrent DKD and available baseline plasma samples (mean eGFR 65 mL/min/1.73 m2 and median UACR 56 mg/g). During a mean follow-up of 5.6 years, 131 participants (9.9%) experienced the composite kidney outcome. Using risk cutoffs from prior validation studies, KidneyIntelX stratified patients to low- (42%), intermediate- (44%), and high-risk (15%) strata with cumulative incidence for the outcome of 3%, 11%, and 26% (risk ratio 8.4; 95% confidence interval [CI]: 5.0, 14.2) for the high-risk versus low-risk groups. The differences in eGFR slopes for canagliflozin versus placebo were 0.66, 1.52, and 2.16 mL/min/1.73 m2 in low, intermediate, and high KidneyIntelX risk strata, respectively. KidneyIntelX risk scores declined by 5.4% (95% CI: -6.9, -3.9) in the canagliflozin arm at year 1 versus an increase of 6.3% (95% CI: 3.8, 8.7) in the placebo arm (p < 0.001). Changes in the KidneyIntelX score at year 1 were associated with future risk of the composite outcome (odds ratio per 10 unit decrease 0.80; 95% CI: 0.77, 0.83; p < 0.001) after accounting for the treatment arm, without evidence of effect modification by the baseline KidneyIntelX risk stratum or by the treatment arm.Conclusions: KidneyIntelX successfully risk-stratified a large multinational external cohort for progression of DKD, and greater numerical differences in the eGFR slope for canagliflozin versus placebo were observed in those with higher baseline KidneyIntelX scores. Canagliflozin treatment reduced KidneyIntelX risk scores over time and changes in the KidneyIntelX score from baseline to 1 year associated with future risk of DKD progression, independent of the baseline risk score and treatment arm. [ABSTRACT FROM AUTHOR]- Published
- 2022
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8. The rhubarb road
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Fleming, Fergus
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The Silk Road: Two Thousand Years in the Heart of China (Book) -- Book reviews ,Books -- Book reviews ,Literature/writing - Published
- 2003
9. Travels, Explorations and Empires 1770-1835
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Fleming, Fergus
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Travels, Explorations and Empires 1770-1835 (Book) -- Criticism and interpretation ,Travels Explorations and Empires 1770-1835, Vols. 1-4 (Book) -- Book reviews ,Books -- Book reviews ,Literature/writing - Published
- 2001
10. To the end of the world
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Fleming, Fergus
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Dead Reckoning: Great Adventure Writing from the Golden Age of Exploration, 1800-1900 (Book) -- Book reviews ,Encyclopedia of Exploration to 1800 (Book) -- Book reviews ,Literature/writing - Published
- 2003
11. 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.
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CHAN, LILI, NADKARNI, GIRISH N., FLEMING, FERGUS, SALEM, FADI E., MURPHY, BARBARA, DONOVAN, MICHAEL J., COCA, STEVEN, and DAMRAUER, SCOTT M.
- Abstract
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 for patients with early stages of T2DKD. We obtained EHR and plasma from patients with T2DKD (Kidney Disease Improving Global Outcomes [KDIGO] G1-G2, A2-A3 or G3a-G3b, A1-A3) from the Mount Sinai BioMe Biobank and the Penn Medicine Biobank and measured soluble tumor necrosis factor receptor (sTNFR1, sTNFR2) and kidney injury molecule-1 (KIM-1) via a validated quantitative electrochemiluminescent assay. A machine learning model was trained using the EHR/biomarker data to predict RKFD, defined as eGFR decline of ≥5 ml/min/year or ≥40% sustained decline in eGFR or kidney failure within 5 years, and performance was compared to a clinical model and KDIGO risk-matrix. A total of 1146 subjects with a mean age of 63, baseline eGFR 54 ml/min, and median UACR 61 mg/g were randomly divided into 60% training (n=686) and 40% validation (n=460) sets. The median follow-up was 4.3 years and 241 (21%) experienced RKFD. The AUC of KidneyIntelX for RFKD was 0.85 (95% CI 0.84-0.86) in training and 0.77 (95% CI 0.76-0.79) in the validation set vs. AUC of 0.67 (95% CI 0.64-0.70) for a clinical model. Using pre-specified cutoffs from the training set, KidneyIntelX stratified 47%, 37% and 16% of the validation set into low-, intermediate- and high-risk strata, with a PPV of 62% (vs. 41% for KDIGO) in the high-risk group (p <0.001), a net reclassification improvement for events of 41% (p <0.05), and a NPV of 91% in the low-risk group (vs. 85% for KDIGO). KidneyIntelX, a machine learning model combining biomarkers and EHR data, improved prediction of RKFD over standard clinical metrics in patients with T2DKD. Future studies will prospectively evaluate KidneyIntelX in clinical decision-making and impact. Disclosure: L. Chan: None. G.N. Nadkarni: Advisory Panel; Self; Renalytix AI. Consultant; Self; AstraZeneca, Reata, Renalytix AI. Stock/Shareholder; Self; Renalytix AI. Other Relationship; Self; Renalytix AI. F. Fleming: Board Member; Self; Renalytix AI plc. F.E. Salem: None. B. Murphy: Board Member; Self; RenalytixAI. M.J. Donovan: Consultant; Self; Renalytix AI plc. S. Coca: Advisory Panel; Self; Renalytix AI plc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., CHF Solutions, Relypsa, Inc., Takeda Pharmaceutical Company Limited. Stock/Shareholder; Self; Renalytix AI plc. S.M. Damrauer: Consultant; Spouse/Partner; Rhythm Pharmaceuticals. Research Support; Self; CytoVas LLC, Renalytix AI plc. Funding: Renalytix AI plc [ABSTRACT FROM AUTHOR]
- Published
- 2020
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12. The Alps and the Imagination
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Fleming, Fergus
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- 2020
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13. A Post Hoc Analysis of KidneyIntelX and Cardiorenal Outcomes in Diabetic Kidney Disease.
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Nadkarni GN, Takale D, Neal B, Mahaffey KW, Yavin Y, Hansen MK, Fleming F, Heerspink HJL, and Coca SG
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- Canagliflozin therapeutic use, Humans, Cardiovascular System, Diabetes Mellitus, Type 2 complications, Diabetic Nephropathies drug therapy, Sodium-Glucose Transporter 2 Inhibitors therapeutic use
- Abstract
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 more of benefit from treatment with canagliflozin versus placebo.These findings may serve to increase adoption of underutilized therapies for cardiorenal risk reduction in patients with diabetic kidney disease., Competing Interests: S.G. Coca reports consultancy for Axon Therapies, Bayer, Boehringer Ingelheim, Nuwellis, Renalytix, Reprieve Cardiovascular, Takeda, Vifor, and 3ive; ownership interest in pulseData and Renalytix; research funding from ProKidney, Renalytix, Renal Research Institute, and XORTX; patents or royalties from Renalytix; being a scientific co-founder of Renalytix and having equity and royalties and being a consultant and member of the scientific advisory board; an advisory or leadership role with Renalytix and Reprieve Cardiovascular; and being an associate editor for Kidney360 and on the editorial boards of JASN, CJASN, and Kidney International. F. Fleming reports being the chief technology officer and co-founder of Renalytix; being an employee of Renalytix; ownership interest in Renalytix and Verici Dx; and having an advisory or leadership role with Renalytix. M. Hansen reports being an employee of Janssen Research & Development, and ownership interest in Johnson & Johnson. H.L. Heerspink reports being an employee of University Medical Center Groningen; ongoing consultancy agreements with AstraZeneca, Bayer, Boehringer Ingelheim, CSL Behring, Chinook, Dimerix, Eli-Lilly, Gilead, GoldFinch, Janssen, Merck, NovoNordisk, and Travere Pharmaceuticals; research funding from AstraZeneca, Janssen research support (grant funding directed to employer), and NovoNordisk; honoraria from AstraZeneca (lecture fees); and participating in a speakers’ bureau for AstraZeneca. K. Mahaffey reports consultancy for Amgen, Applied Therapeutics, AstraZeneca, Bayer, CSL Behring, Elsevier, Fribrogen, Invo, Johnson & Johnson, Lexicon, Myokardia, Novartis, Novo Nordisk, Otsuka, Phasebio, Portola, Precordior, Quidel, Sanofi, and Theravance; research funding from the American Heart Assocation, Apple, Inc., Bayer, California Institute Regenerative Medicine, Eidos, Ferring, Gilead, Google (Verily), Idorsia, Johnson & Johnson, Luitpold, PAC-12, Precordior, and Sanifit; and honoraria from Amgen, Anthos, Applied Therapeutics, AstraZeneca, Bayer, CSL Behring, Elsevier, Inova, Intermountain Health, Johnson & Johnson, Medscae, Mount Sinai, Mundi Pharma, Myokardia, Novartis, Novo Nordisk, Otsuka, Portola, Sanofi, SmartMedics, and Theravance. G.N. Nadkarni reports consultancy for Daiichi Sankyo, GLG consulting, Qiming Capital, Reata, Renalytix, Siemens Healthineers, and Variant Bio; ownership interest in Data2Wisdom, LLC, Doximity, Nexus iConnect, Pensieve Health, Renalytix, and Verici; research funding from Renalytix; honoraria from Daiichi Sankyo; patents or royalties from Renalytix; an advisory or leadership role with Renalytix; participating in a speakers’ bureau with Daiichi Sankyo; and being a scientific co-founder of Renalytix and having equity and royalties and being a consultant and member of the scientific advisory board. B. Neal reports consultancy for Janssen Research & Development, LLC; research funding from the Australian National Health and Medical Research Council Principal Research Fellowship and Janssen; honoraria from Janssen Research & Development, LLC (paid to the institution); and serving on advisory boards and/or involvement in continuing medical education (CME) programs for Janssen, with any consultancy, honoraria, or travel support paid to his institution. D. Takale reports being an employee of Persistent. Y. Yavin reports being an employer of Janssen, and ownership interest in Bristol-Myers Squibb and Johnson & Johnson., (Copyright © 2022 by the American Society of Nephrology.)
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- 2022
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14. Initial Validation of a Machine Learning-Derived Prognostic Test (KidneyIntelX) Integrating Biomarkers and Electronic Health Record Data To Predict Longitudinal Kidney Outcomes.
- Author
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Chauhan K, Nadkarni GN, Fleming F, McCullough J, He CJ, Quackenbush J, Murphy B, Donovan MJ, Coca SG, and Bonventre JV
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- Apolipoprotein L1 genetics, Biomarkers, Glomerular Filtration Rate genetics, Humans, Kidney, Machine Learning, Prognosis, Diabetes Mellitus, Type 2 diagnosis, Electronic Health Records
- Abstract
Background: Individuals with type 2 diabetes (T2D) or the apolipoprotein L1 high-risk ( APOL1 -HR) genotypes are at increased risk of rapid kidney function decline (RKFD) and kidney failure. We hypothesized that a prognostic test using machine learning integrating blood biomarkers and longitudinal electronic health record (EHR) data would improve risk stratification., Methods: We selected two cohorts from the Mount Sinai Bio Me Biobank: T2D ( n =871) and African ancestry with APOL1 -HR ( n =498). We measured plasma tumor necrosis factor receptors (TNFR) 1 and 2 and kidney injury molecule-1 (KIM-1) and used random forest algorithms to integrate biomarker and EHR data to generate a risk score for a composite outcome: RKFD (eGFR decline of ≥5 ml/min per year), or 40% sustained eGFR decline, or kidney failure. We compared performance to a validated clinical model and applied thresholds to assess the utility of the prognostic test (KidneyIntelX) to accurately stratify patients into risk categories., Results: Overall, 23% of those with T2D and 18% of those with APOL1 -HR experienced the composite kidney end point over a median follow-up of 4.6 and 5.9 years, respectively. The area under the receiver operator characteristic curve (AUC) of KidneyIntelX was 0.77 (95% CI, 0.75 to 0.79) in T2D, and 0.80 (95% CI, 0.77 to 0.83) in APOL1 -HR, outperforming the clinical models (AUC, 0.66 [95% CI, 0.65 to 0.67] and 0.72 [95% CI, 0.71 to 0.73], respectively; P <0.001). The positive predictive values for KidneyIntelX were 62% and 62% versus 46% and 39% for the clinical models ( P <0.01) in high-risk (top 15%) stratum for T2D and APOL1 -HR, respectively. The negative predictive values for KidneyIntelX were 92% in T2D and 96% for APOL1 -HR versus 85% and 93% for the clinical model, respectively ( P =0.76 and 0.93, respectively), in low-risk stratum (bottom 50%)., Conclusions: In patients with T2D or APOL1 -HR, a prognostic test (KidneyIntelX) integrating biomarker levels with longitudinal EHR data significantly improved prediction of a composite kidney end point of RKFD, 40% decline in eGFR, or kidney failure over validated clinical models., Competing Interests: J. Bonventre is a coinventor on KIM-1 patents assigned to Partners Healthcare. He is a consultant to Aldeyra, Angion, Cadent, Praxis, and Seattle Genetics, and owns equity in DxNow, Goldfinch, Innoviva, MediBeacon, Sensor Kinesis, Sentien, and Verinano. J. Bonventre is also a nonpaid advisory board member for Renalytix. S. Coca has received consulting fees from Bayer, Boehringer-Ingelheim, CHF Solutions, Relypsa, and Takeda Pharmaceuticals in the past 3 years. S. Coca, C. He, B. Murphy, G. Nadkarni, and J. Quackenbush receive financial compensation as consultants and advisory board members for RenalytixAI, Inc., and own equity in Renalytix. S. Coca and G. Nadkarni are scientific cofounders of RenalytixAI. M. Donovan, F. Fleming, J. McCullough, and B. Murphy are officers of RenalytixAI. G. Nadkarni has received consulting fees from BioVie Inc and GLG consulting and has received operational funding from Goldfinch Bio in the past 3 years. All remaining authors have nothing to disclose., (Copyright © 2020 by the American Society of Nephrology.)
- Published
- 2020
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