Yaakov Stern1, Eric Stallard2, Bruce Kinosian3, Carolyn Zhu4,5, Stephanie Cosentino1, Zhezhen Jin6, Yian Gu1. 1. Cognitive Neuroscience Division of the Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA. 2. Biodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, North Carolina, USA. 3. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA. 4. Brookdale Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA. 5. James J. Peters VA Medical Center, Bronx, New York, USA. 6. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.
Abstract
INTRODUCTION: Identifying the course of Alzheimer's disease (AD) for individual patients is important for numerous clinical applications. Ideally, prognostic models should provide information about a range of clinical features across the entire disease process. Previously, we published a new comprehensive longitudinal model of AD progression with inputs/outputs covering 11 interconnected clinical measurement domains. METHODS: Here, we (1) validate the model on an independent cohort; and (2) demonstrate the model's utility in clinical applications by projecting changes in 6 of the 11 domains. RESULTS: Survival and prevalence curves for two representative outcomes-mortality and dependency-generated by the model accurately reproduced the observed curves both overall and for patients subdivided according to risk levels using an independent Cox model. DISCUSSION: The new model, validated here, effectively reproduces the observed course of AD from an initial visit assessment, allowing users to project coordinated developments for individual patients of multiple disease features.
INTRODUCTION: Identifying the course of Alzheimer's disease (AD) for individual patients is important for numerous clinical applications. Ideally, prognostic models should provide information about a range of clinical features across the entire disease process. Previously, we published a new comprehensive longitudinal model of AD progression with inputs/outputs covering 11 interconnected clinical measurement domains. METHODS: Here, we (1) validate the model on an independent cohort; and (2) demonstrate the model's utility in clinical applications by projecting changes in 6 of the 11 domains. RESULTS: Survival and prevalence curves for two representative outcomes-mortality and dependency-generated by the model accurately reproduced the observed curves both overall and for patients subdivided according to risk levels using an independent Cox model. DISCUSSION: The new model, validated here, effectively reproduces the observed course of AD from an initial visit assessment, allowing users to project coordinated developments for individual patients of multiple disease features.
Authors: Kathleen Van Dyk; Stephanie Towns; Oksana Tatarina; Philip Yeung; Jhedy Dorrejo; Laura B Zahodne; Yaakov Stern Journal: Am J Alzheimers Dis Other Demen Date: 2015-09-03 Impact factor: 2.035
Authors: Y Stern; M X Tang; M S Albert; J Brandt; D M Jacobs; K Bell; K Marder; M Sano; D Devanand; S M Albert; F Bylsma; W Y Tsai Journal: JAMA Date: 1997-03-12 Impact factor: 56.272
Authors: David M Eddy; William Hollingworth; J Jaime Caro; Joel Tsevat; Kathryn M McDonald; John B Wong Journal: Value Health Date: 2012 Sep-Oct Impact factor: 5.725
Authors: Qolamreza R Razlighi; Eric Stallard; Jason Brandt; Deborah Blacker; Marilyn Albert; Nikolaos Scarmeas; Bruce Kinosian; Anatoliy I Yashin; Yaakov Stern Journal: J Alzheimers Dis Date: 2014 Impact factor: 4.472
Authors: W James Deardorff; Deborah E Barnes; Sun Y Jeon; W John Boscardin; Kenneth M Langa; Kenneth E Covinsky; Susan L Mitchell; Elizabeth L Whitlock; Alexander K Smith; Sei J Lee Journal: JAMA Intern Med Date: 2022-09-26 Impact factor: 44.409