Analyses of Diagnostic Patterns at 30 Alzheimer’s Disease Centers in the USSteenland K.a, b · Macneil J.a · Bartell S.c · Lah J.b
aRollins School of Public Health and bAlzheimer’s Disease Research Center, Emory University, Atlanta, Ga., and cProgram in Public Health, University of California at Irvine, Irvine, Calif., USA
Dr. Kyle Steenland
Emory University, Rollins School of Public Health
1518 Clifton Road
Atlanta, GA 30322 (USA)
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Background: The US Alzheimer’s Disease Centers (ADCs) (n = 30) recently created a uniform data set. We sought to determine which variables were most important in making a diagnosis, and how these differed across ADCs. Methods: A cross-sectional analysis of first visits to ADCs via polytomous logistic regression. We analyzed subjects with complete data (n = 7,555, 89%), and also used multiple imputation to infer missing data. Results: There were 8,495 subjects; 50, 26, and 24% were diagnosed as normal, having mild cognitive impairment (MCI), or mild Alzheimer’s disease [Clinical Dementia Rating (CDR) score <1], respectively. The model using 7,555 subjects was 86% accurate in predicting diagnosis. Important predictors were physician-reported decline and the CDR sum of boxes, followed by 4 cognitive tests (Mini Mental State Examination, Category Fluency Tests, Logical Memory Test, Boston Naming Test). Multiple imputation revealed Trail Making Test B to be additionally important. Consensus versus single-clinician diagnoses were 2–3 times more likely to result in MCI than normal diagnoses. Excluding clinical judgment variables, functional assessment and psychiatric symptoms were important additional predictors; model accuracy remained high (78%). There were significant differences between centers in the use of different cognitive tests in making diagnoses. Conclusions: We recommend creating a hypothetic data set to use across ADCs to improve diagnostic consistency, and a survey on the use of raw or adjusted cognitive test scores by different ADCs.
© 2010 S. Karger AG, Basel
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