The chronic kidney disease model: a general purpose model of disease progression and treatment.

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Date

2011-06-16

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Abstract

Background

Chronic kidney disease (CKD) is the focus of recent national policy efforts; however, decision makers must account for multiple therapeutic options, comorbidities and complications. The objective of the Chronic Kidney Disease model is to provide guidance to decision makers. We describe this model and give an example of how it can inform clinical and policy decisions.

Methods

Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death. Each cycle is 1 month. Projections account for race, age, gender, diabetes, proteinuria, hypertension, cardiac disease, and CKD stage. Treatment strategies include hypertension control, diabetes control, use of HMG-CoA reductase inhibitors, use of angiotensin converting enzyme inhibitors, nephrology specialty care, CKD screening, and a combination of these. The model architecture is flexible permitting updates as new data become available. The primary outcome is quality adjusted life years (QALYs). Secondary outcomes include health state events and CKD progression rate.

Results

The model was validated for GFR change/year -3.0 ± 1.9 vs. -1.7 ± 3.4 (in the AASK trial), and annual myocardial infarction and mortality rates 3.6 ± 0.9% and 1.6 ± 0.5% vs. 4.4% and 1.6% in the Go study. To illustrate the model's utility we estimated lifetime impact of a hypothetical treatment for primary prevention of vascular disease. As vascular risk declined, QALY improved but risk of dialysis increased. At baseline, 20% and 60% reduction: QALYs = 17.6, 18.2, and 19.0 and dialysis = 7.7%, 8.1%, and 10.4%, respectively.

Conclusions

The CKD Model is a valid, general purpose model intended as a resource to inform clinical and policy decisions improving CKD care. Its value as a tool is illustrated in our example which projects a relationship between decreasing cardiac disease and increasing ESRD.

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Subjects

Humans, Disease Progression, Monte Carlo Method, Risk Factors, Comorbidity, Quality-Adjusted Life Years, Models, Theoretical, Renal Insufficiency, Chronic

Citation

Published Version (Please cite this version)

10.1186/1472-6947-11-41

Publication Info

Orlando, Lori A, Eric J Belasco, Uptal D Patel and David B Matchar (2011). The chronic kidney disease model: a general purpose model of disease progression and treatment. BMC medical informatics and decision making, 11(1). p. 41. 10.1186/1472-6947-11-41 Retrieved from https://hdl.handle.net/10161/22838.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Patel

Uptal Dinesh Patel

Adjunct Professor in the Department of Medicine

Uptal Patel, MD is an Adjunct Professor interested in population health with a broad range of clinical and research experience. As an adult and pediatric nephrologist with training in health services and epidemiology, his work seeks to improve population health for patients with kidney diseases through improvements in prevention, diagnosis and treatment. He has led clinical and translational research programs to improve detection and management of kidney disease in a variety of populations.

His current efforts seek to advance targeted therapies for immune-mediated diseases as the Senior Vice President and Head of Development at HI-Bio, at Biogen. Prior to being CMO at HI-Bio, he led clinical strategy, translation, and development of the kidney portfolios at AstraZeneca (within the early cardiovascular, renal, and metabolism therapeutic area) and Gilead Sciences (within the inflammation therapeutic area).

He currently also serves as Chair of the Board of Directors for the Kidney Health Initiative, a public-private partnership between the American Society of Nephrology and the FDA to catalyze innovation and the development of safe and effective patient-centered therapies for people with kidney diseases. He completed training at the University of Michigan in internal medicine, pediatrics, adult nephrology, pediatric nephrology, and health services research after attending medical school at UCSF.

Matchar

David Bruce Matchar

Professor of Medicine

My research relates to clinical practice improvement - from the development of clinical policies to their implementation in real world clinical settings. Most recently my major content focus has been cerebrovascular disease. Other major clinical areas in which I work include the range of disabling neurological conditions, cardiovascular disease, and cancer prevention.
Notable features of my work are: (1) reliance on analytic strategies such as meta-analysis, simulation, decision analysis and cost-effectiveness analysis; (2) a balancing of methodological rigor the needs of medical professionals; and (3) dependence on interdisciplinary groups of experts.
This approach is best illustrated by the Stroke Prevention Patient Outcome Research Team (PORT), for which I served as principal investigator. Funded by the AHCPR, the PORT involved 35 investigators at 13 institutions. The Stroke PORT has been highly productive and has led to a stroke prevention project funded as a public/private partnership by the AHCPR and DuPont Pharma, the Managing Anticoagulation Services Trial (MAST). MAST is a practice improvement trial in 6 managed care organizations, focussing on optimizing anticoagulation for individuals with atrial fibrillation.
I serve as consultant in the general area of analytic strategies for clinical policy development, as well as for specific projects related to stroke (e.g., acute stroke treatment, management of atrial fibrillation, and use of carotid endarterectomy.) I have worked with AHCPR (now AHRQ), ACP, AHA, AAN, Robert Wood Johnson Foundation, NSA, WHO, and several pharmaceutical companies.
Key Words: clinical policy, disease management, stroke, decision analysis, clinical guidelines


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