TY - JOUR
T1 - Development of multivariable prediction models for institutionalization and mortality in the full spectrum of Alzheimer's disease
AU - Mank, Arenda
AU - van Maurik, Ingrid S.
AU - Rijnhart, Judith J. M.
AU - Bakker, Els D.
AU - Bouteloup, Vincent
AU - Le Scouarnec, Lisa
AU - Teunissen, Charlotte E.
AU - Barkhof, Frederik
AU - Scheltens, Philip
AU - Berkhof, Johannes
AU - van der Flier, Wiesje M.
N1 - Funding Information: Frederik Barkhof is a steering committee or iDMC member for Biogen, Merck, Roche, EISAI, and Prothena. FB is a consultant for Roche, Biogen, Merck, IXICO, Jansen, and Combinostics. Research agreements with Merck, Biogen, GE Healthcare, Roche. Co-founder and shareholder of Queen Square Analytics LTD. FB is supported by the NIHR biomedical research centre at UCLH. Funding Information: Charlotte E. Teunissen was supported by funding from the European Commission (Marie Curie International Training Network, grant agreement No 860197 (MIRIADE), and JPND), Health Holland, the Dutch Research Council (ZonMW), Alzheimer Drug Discovery Foundation, The Selfridges Group Foundation, Alzheimer Netherlands, Alzheimer Association. C.E. Teunissen has a collaboration contract with ADx Neurosciences, Quanterix, and Eli Lilly, performed contract research, or received grants from AC-Immune, Axon Neurosciences, Biogen, Brainstorm Therapeutics, Celgene, EIP Pharma, Eisai, PeopleBio, Roche, Toyama, and Vivoryon. C.E. Teunissen serves on editorial boards of Medidact Neurologie/Springer, Alzheimer Research and Therapy, and Neurology: Neuroimmunology & Neuroinflammation and is editor of a Neuromethods book Springer. Funding Information: Research of Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting VUmc fonds. The chair of W.M. van der Flier is supported by the Pasman stichting. Publisher Copyright: © 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Background: Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer’s disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia. Methods: This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell’s C statistics. Results: We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell’s C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell’s C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination. Conclusions: We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD.
AB - Background: Patients and caregivers express a desire for accurate prognostic information about time to institutionalization and mortality. Previous studies predicting institutionalization and mortality focused on the dementia stage. However, Alzheimer’s disease (AD) is characterized by a long pre-dementia stage. Therefore, we developed prediction models to predict institutionalization and mortality along the AD continuum of cognitively normal to dementia. Methods: This study included SCD/MCI patients (subjective cognitive decline (SCD) or mild cognitive impairment (MCI)) and patients with AD dementia from the Amsterdam Dementia Cohort. We developed internally and externally validated prediction models with biomarkers and without biomarkers, stratified by dementia status. Determinants were selected using backward selection (p<0.10). All models included age and sex. Discriminative performance of the models was assessed with Harrell’s C statistics. Results: We included n=1418 SCD/MCI patients (n=123 died, n=74 were institutionalized) and n=1179 patients with AD dementia (n=413 died, n=453 were institutionalized). For both SCD/MCI and dementia stages, the models for institutionalization and mortality included after backward selection clinical characteristics, imaging, and cerebrospinal fluid (CSF) biomarkers. In SCD/MCI, the Harrell’s C-statistics of the models were 0.81 (model without biomarkers: 0.76) for institutionalization and 0.79 (model without biomarker: 0.76) for mortality. In AD-dementia, the Harrell’s C-statistics of the models were 0.68 (model without biomarkers: 0.67) for institutionalization and 0.65 (model without biomarker: 0.65) for mortality. Models based on data from amyloid-positive patients only had similar discrimination. Conclusions: We constructed prediction models to predict institutionalization and mortality with good accuracy for SCD/MCI patients and moderate accuracy for patients with AD dementia. The developed prediction models can be used to provide patients and their caregivers with prognostic information on time to institutionalization and mortality along the cognitive continuum of AD.
KW - Alzheimer's disease
KW - Institutionalization
KW - Mild cognitive impairment
KW - Mortality
KW - Prognosis
KW - Subjective cognitive decline
UR - http://www.scopus.com/inward/record.url?scp=85135445470&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13195-022-01053-0
DO - https://doi.org/10.1186/s13195-022-01053-0
M3 - Article
C2 - 35932034
SN - 1758-9193
VL - 14
JO - Alzheimer's Research and Therapy
JF - Alzheimer's Research and Therapy
IS - 1
M1 - 110
ER -