Analyses included 100 ET brains from the Essential Tremor Centralized Brain Repository and 50 control brains. A standard tissue block from the cerebellar cortex was used to quantify 11 metrics of pathological change that reflected changes in the Purkinje cell and related neuronal populations. Three supervised classification algorithms were investigated, including logistic regression with ridge penalty, random forest and gradient boosted decision tree. Data were divided into training and validation samples.
All three algorithms performed similarly to correctly predict a diagnosis of ET. Using logistic regression with a ridge penalty algorithm, the simplest method, sensitivity ranged from 87.5% to 96.64%, with six of eight values >95%, and the specificity ranged from 92.87% to 98.11%, with five of eight values >95%. We also provide a web-based application that uses these metric values, and based on specified cut-offs, determines the likely diagnosis.
These analyses illustrate the ability to correctly predict a diagnosis of ET and set the stage for use of pathologically based criteria to distinguish clinically diagnosed ET cases from controls at the time of postmortem.