Externally Validated Deep Learning Model to Identify Prodromal Parkinson’s Disease from Electrocardiogram
Authors: Karabayir Ibrahim , Gunturkun Fatma , Butler Liam , Goldman Samuel M , Kamaleswaran Rishikesan , Davis Robert L , Colletta Kalea , Chinthala Lokesh , Jefferies John L , Bobay Kathleen , Ross Webb , Petrovitch Helen , Masaki Kamal , Tanner Carolina , Akbilgic Oguz 
Published: November 2022

Abstract
Little is known about Electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before incidence of the disease. This retrospective case-control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Only data available at least 6 months before PD diagnosis was used as the model’s input. Data from LUC spanned back to May 2014 while that from MLH spanned to January 2015. PD was denoted by at least two primary ICD diagnostic codes, namely ICD9 332.0, ICD10 G20. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. Prediction of prodromal PD (6-months to 5-years preceding PD diagnosis) was the primary outcome of this research. Three time windows were set: 6 months-1year, 6months-3 years and 6months – 5 years. A novel deep neural network using standard 10-second 12-lead ECG was used to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for gender, race and age were also performed. A one-dimensional convolutional neural network (1D-CNN) was used to predict PD risk (or identify prodromal PD) from standard 10 second 12-lead ECGs collected between 6 months to 5 years before a clinical diagnosis. The prediction model was built using MLH data and externally validated on LUC data. 131 cases/1058 controls at MLH and 29 cases/165 controls at LUC were identified. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation of AUC = 0.67 when predicting prodromal PD at any time between 6 months and 5 years. The accuracy increased when using ECGs to predict prodromal PD within 6 months to 3 years, with an external validation AUC of 0.69 and achieving highest AUC when predicting PD within 1 year before onset (AUC of 0.74). A predictive model that can correctly classify individuals with prodromal PD was developed using only raw ECGs as inputs. The model was effective in predicting prodromal PD within an independent cohort, particularly closer to disease diagnosis. The ECG-based model outperformed multiple models built using ECG feature engineering. Subgroup analyses showed that some subgroups, including females and those of over 60 years of age, might benefit from closer monitoring, especially when symptoms start becoming more evident but not enough to make a diagnosis. This research highlights that standard ECGs may help identify individuals with prodromal PD for cost-effective early detection and inclusion in disease-modifying therapeutic trials.