Decision tree supports the interpretation of CSF biomarkers in Alzheimer's disease

We developed and validated a clinically applicable decision tree for the use of cerebrospinal fluid biomarkers in the diagnosis of Alzheimer's disease (AD).

Subjects with probable AD (n = 1004) and controls (n = 442) were included. A decision tree was modeled using Classification And Regression Tree analysis in a training cohort (AD n = 221; controls n = 221) and validated in an independent cohort (AD n = 783; controls n = 221). Diagnostic performance was compared to previously defined cutoffs (amyloid β 1-42 < 813 pg/ml; tau>375 pg/ml).

Two cerebrospinal fluid AD biomarker profiles were revealed: the "classical" AD biomarker profile (amyloid β 1-42: 647-803 pg/ml; tau >374 pg/ml) and an "atypical" AD biomarker profile with strongly decreased amyloid β 1-42 (<647 pg/ml) and normal tau concentrations (<374 pg/ml). Compared to previous cutoffs, the decision tree performed better on diagnostic accuracy (86% [84-88] vs 80% [78-83]).

Two cerebrospinal fluid AD biomarker profiles were identified and incorporated in a readily applicable decision tree, which improved diagnostic accuracy.