4.7 Article

A prognostic model of Alzheimer's disease relying on multiple longitudinal measures and time-to-event data

期刊

ALZHEIMERS & DEMENTIA
卷 14, 期 5, 页码 644-651

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.jalz.2017.11.004

关键词

Mild cognitive impairment; Multivariate functional component analysis; Prediction; External validation; ADNI

资金

  1. National Institute of Neurological Disorders and Stroke [R01NS091307]
  2. Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
  3. DOD ADNI (Department of Defense) [W81XWH-12-2-0012]
  4. National Institute on Aging
  5. National Institute of Biomedical Imaging and Bioengineering
  6. AbbVie
  7. Alzheimer's Association
  8. Alzheimer's Drug Discovery Foundation
  9. Araclon Biotech
  10. BioClinica, Inc.
  11. Biogen
  12. Bristol-Myers Squibb Company
  13. CereSpir, Inc.
  14. Cogstate
  15. Eisai Inc.
  16. Elan Pharmaceuticals, Inc.
  17. Eli Lilly and Company
  18. Euroimmun
  19. F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.
  20. Fujirebio
  21. GE Healthcare
  22. IXICO Ltd.
  23. Janssen Alzheimer Immunotherapy Research & Development, LLC.
  24. Johnson & Johnson Pharmaceutical Research & Development LLC.
  25. Lumosity
  26. Lundbeck
  27. Merck Co., Inc.
  28. Meso Scale Diagnostics, LLC.
  29. NeuroRx Research
  30. Neurotrack Technologies
  31. Novartis Pharmaceuticals Corporation
  32. Pfizer Inc.
  33. Piramal Imaging
  34. Servier
  35. Takeda Pharmaceutical Company
  36. Transition Therapeutics
  37. Canadian Institutes of Health Research

向作者/读者索取更多资源

Introduction: Characterizing progression in Alzheimer's disease is critically important for early detection and targeted treatment. The objective was to develop a prognostic model, based on multivariate longitudinal markers, for predicting progression-free survival in patients with mild cognitive impairment. Methods: The information contained in multiple longitudinal markers was extracted using multivariate functional principal components analysis and used as predictors in the Cox regression models. Cross-validation was used for selecting the best model based on Alzheimer's Disease Neuroimaging Initiative-1. External validation was conducted on Alzheimer's Disease Neuroimaging Initiative-2. Results: Model comparison yielded a prognostic index computed as the weighted combination of historical information of five neurocognitive longitudinal markers that are routinely collected in observational studies. The comprehensive validity analysis provided solid evidence of the usefulness of the model for predicting Alzheimer's disease progression. Discussion: The prognostic model was improved by incorporating multiple longitudinal markers. It is useful for monitoring disease and identifying patients for clinical trial recruitment. (C) 2017 the Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

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