The dawn of robust individualised risk models for dementia
The dawn of robust individualised risk models for dementia
复制标题
痴呆症稳健个体化风险模型的曙光
DOI:
10.1016/s1474-4422(19)30353-9
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发表时间:
2019
期刊:
影响因子:
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通讯作者:
C. Masters
中科院分区:
文献类型:
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作者:
S. Burnham;S. Loi;J. Doecke;V. Fedyashov;V. Doré;V. Villemagne;C. Masters
Mild cognitive impairment (MCI) typically represents a hold ing pattern for individuals who live for many years without knowing their long-term prognosis. Such uncertainty between improvement or remaining stable versus progressing to a diagnosis of dementia is unsatis fying. Scarcity of specific information or advice com pounds this issue. People with MCI do not have access to treatments such as cholinesterase inhibitors or meman tine, they usually cannot partake in therapeutic trials, and the absence of a path forward can add to their anxiety and that of family members. 1 In The Lancet Neurology, Ingrid van Maurik and colleagues2 attempt to clarify this situation. They provide a method for individualised prognosis by indicating which of the participants with MCI in their study were most likely to progress to dementia over 1, 3, and 5 year timeframes. They assessed four separate prognostic models: first, a model incorporating age, sex, and the Mini-Mental State Examination (MMSE); second, a model of age, MMSE, and hippocampal volume; third, a model of MMSE, CSF amyloid β (1–42), and CSF total tau; 3 and fourth, the ATN model4 of CSF amyloid β (1–42), CSF phosphorylated tau, and hippocampal volume. These models were applied to 2611 MCI participants across the European Medical Information Framework for Alzheimer’s disease (EMIF), the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Amsterdam Dementia Cohort (ADC), and the Swedish BioFINDER studies. Of these 2611 MCI participants, 1007 (39%) progressed to dementia within a mean follow-up period of 3 years (SD 2). 808 (80%) participants progressed to dementia due to Alzheimer’s disease. Van Maurik and colleagues had to overcome many difficulties in harmonising the data from so many participants and to ensure that robust and appropriate analyses
DOI:
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发表时间:
2017
期刊:
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影响因子:
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作者:
C. Jack;D. Bennett;K. Blennow;B. Dunn;C. Elliott;S. Haeberlein;D. Holtzman;M. Jagust;F. Jessen-F.
通讯作者:
C. Jack;D. Bennett;K. Blennow;B. Dunn;C. Elliott;S. Haeberlein;D. Holtzman;M. Jagust;F. Jessen-F.