Introduction: We developed and validated a clinically applicable decision tree for the use of cerebrospinal fluid biomarkers in the diagnosis of Alzheimer's disease (AD). Methods: Subjects with probable AD (n 5 1004) and controls (n 5 442) were included. A decision tree was modeled using Classification And Regression Tree analysis in a training cohort (AD n 5 221; controls n 5 221) and validated in an independent cohort (AD n 5 783; controls n 5 221). Diagnostic performance was compared to previously defined cutoffs (amyloid b 1-42 , 813 pg/ml; tau.375 pg/ml). Results: Two cerebrospinal fluid AD biomarker profiles were revealed: the "classical" AD biomarker profile (amyloid b 1-42: 647-803 pg/ml; tau .374 pg/ml) and an "atypical" AD biomarker profile with strongly decreased amyloid b 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]). Discussion: Two cerebrospinal fluid AD biomarker profiles were identified and incorporated in a readily applicable decision tree, which improved diagnostic accuracy.
Mofrad et al., 2019, created and approved a clinically pertinent decision tree in the finding of Alzheimer's disease (AD) for the utilization of cerebrospinal liquid biomarkers REF .
Decision tree supports the interpretation of CSF biomarkers in Alzheimer's disease