Corey T McMillan1, Joanne Wuu2, Katya Rascovsky1, Stephanie Cosentino3, Murray Grossman1, Lauren Elman1, Colin Quinn1, Luis Rosario1, Jessica H Stark2, Volkan Granit2, Hannah Briemberg4, Sneha Chenji5, Annie Dionne6, Angela Genge7, Wendy Johnston8, Lawrence Korngut5, Christen Shoesmith9, Lorne Zinman10, Sanjay Kalra8,11, Michael Benatar2. 1. Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA. 2. Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA. 3. The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA. 4. Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, Canada. 5. Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada. 6. Department of Medicine, Université Laval, Canada. 7. Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada. 8. Division of Neurology, Department of Medicine, University of Alberta, Edmonton, Canada, and. 9. London Health Sciences Centre, Western University, London, Canada. 10. Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada. 11. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Canada.
Abstract
Objective: Amyotrophic lateral sclerosis (ALS) is a multi-system disorder characterized primarily by motor neuron degeneration, but may be accompanied by cognitive dysfunction. Statistically appropriate criteria for establishing cognitive impairment (CI) in ALS are lacking. We evaluate quantile regression (QR), that accounts for age and education, relative to a traditional two standard deviation (SD) cutoff for defining CI. Methods: QR of cross-sectional data from a multi-center North American Control (NAC) cohort of 269 healthy adults was used to model the 5th percentile of cognitive scores on the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). The QR approach was compared to traditional two SD cutoff approach using the same NAC cohort (2SD-NAC) and to existing UK-based normative data derived using the 2SD approach (2SD-UK) to assess the impact of cohort selection and statistical model in identifying CI in 182 ALS patients. Results: QR-NAC models revealed that age and education impact cognitive performance on the ECAS. Based on QR-NAC normative cutoffs, the frequency of CI in the 182 PENN ALS patients was 15.9% for ALS specific, 12.6% for ALS nonspecific, and 15.4% for ECAS total. This frequency of CI is substantially more conservative in comparison to the 2SD-UK (20.3%-34.6%) and modestly more conservative to the 2SD-NAC (14.3%-16.5%) approaches for estimating CI. Conclusions: The choice of normative cohort has a substantial impact and choice of statistical method a modest impact on defining CI in ALS. This report establishes normative ECAS thresholds to identify whether ALS patients in the North American population have CI.
Objective: Amyotrophic lateral sclerosis (ALS) is a multi-system disorder characterized primarily by motor neuron degeneration, but may be accompanied by cognitive dysfunction. Statistically appropriate criteria for establishing cognitive impairment (CI) in ALS are lacking. We evaluate quantile regression (QR), that accounts for age and education, relative to a traditional two standard deviation (SD) cutoff for defining CI. Methods: QR of cross-sectional data from a multi-center North American Control (NAC) cohort of 269 healthy adults was used to model the 5th percentile of cognitive scores on the Edinburgh Cognitive and Behavioral ALS Screen (ECAS). The QR approach was compared to traditional two SD cutoff approach using the same NAC cohort (2SD-NAC) and to existing UK-based normative data derived using the 2SD approach (2SD-UK) to assess the impact of cohort selection and statistical model in identifying CI in 182 ALS patients. Results: QR-NAC models revealed that age and education impact cognitive performance on the ECAS. Based on QR-NAC normative cutoffs, the frequency of CI in the 182 PENN ALS patients was 15.9% for ALS specific, 12.6% for ALS nonspecific, and 15.4% for ECAS total. This frequency of CI is substantially more conservative in comparison to the 2SD-UK (20.3%-34.6%) and modestly more conservative to the 2SD-NAC (14.3%-16.5%) approaches for estimating CI. Conclusions: The choice of normative cohort has a substantial impact and choice of statistical method a modest impact on defining CI in ALS. This report establishes normative ECAS thresholds to identify whether ALS patients in the North American population have CI.
Authors: Johannes Brettschneider; David J Libon; Jon B Toledo; Sharon X Xie; Leo McCluskey; Lauren Elman; Felix Geser; Virginia M Y Lee; Murray Grossman; John Q Trojanowski Journal: Acta Neuropathol Date: 2012-01-01 Impact factor: 17.088
Authors: Leonhard A Bakker; Carin D Schröder; Lauriane A Spreij; Marianne Verhaegen; Joke De Vocht; Philip Van Damme; Jan H Veldink; Johanna M A Visser-Meily; Leonard H van den Berg; Tanja C W Nijboer; Michael A van Es Journal: Amyotroph Lateral Scler Frontotemporal Degener Date: 2018-10-12 Impact factor: 4.092
Authors: Katerina Placek; Michael Benatar; Joanne Wuu; Evadnie Rampersaud; Laura Hennessy; Vivianna M Van Deerlin; Murray Grossman; David J Irwin; Lauren Elman; Leo McCluskey; Colin Quinn; Volkan Granit; Jeffrey M Statland; Ted M Burns; John Ravits; Andrea Swenson; Jon Katz; Erik P Pioro; Carlayne Jackson; James Caress; Yuen So; Samuel Maiser; David Walk; Edward B Lee; John Q Trojanowski; Philip Cook; James Gee; Jin Sha; Adam C Naj; Rosa Rademakers; Wenan Chen; Gang Wu; J Paul Taylor; Corey T McMillan Journal: EMBO Mol Med Date: 2020-12-03 Impact factor: 14.260