Alzheimer's disease is one of the most prevalent neurodegenerative disorders. However, the available diagnostic tools are not efficient and there are severe limitations to predict the occurrence and the onset of the disease. Here, a prediction model is presented to estimate the probability of developing Alzheimer's disease based on amyloid-beta 42 and phosphorylated tau levels in cerebrospinal fluid together with patients' sex. The logistic regression analysis gives an estimation to calculate the probability of developing Alzheimer's disease and when this is applied to the validation data set, has a powerful discriminative ability. The researchers present, and validate, a prediction model that has the potential to be applied in memory clinics to assess the probability of patients developing Alzheimer's disease based on commonly used biomarkers.
A prediction model to calculate probability of Alzheimer’s disease using cerebrospinal fluid biomarkers
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