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Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and

Jasjit S Suri¹, Sudip Paul², Maheshrao A Maindarkar²

¹Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA.

|April 21, 2022

Insights

Parkinson's disease (PD) is linked to cardiovascular disease (CVD) and stroke. Artificial intelligence (AI) can now predict CVD/stroke risk in PD patients, addressing prior limitations.

Area of Science:

Neurology
Cardiology
Artificial Intelligence

Background:

Parkinson's disease (PD) is a severe neurodegenerative disorder often leading to heart failure, yet its link with cardiovascular disease (CVD) and stroke remains unclear.
Existing Artificial Intelligence (AI) models for CVD/stroke risk stratification lack sufficient validation and large-scale data within the PD context.
Previous studies have been limited by small sample sizes, comorbidities, and inadequate clinical data, hindering unbiased AI investigations.

Purpose of the Study:

To establish a definitive link between Parkinson's disease (PD) and cardiovascular disease (CVD)/stroke.
To develop and apply an AI framework for accurate CVD/stroke risk stratification specifically within the PD patient population.
To address limitations in sample size, comorbidity, and data configuration for AI-driven risk prediction in PD.

Main Methods:

A systematic PRISMA search identified 223 relevant studies, analyzing links between PD, CVD, and stroke.
Sequential biological pathways were investigated to form a hypothesis connecting PD neurodegeneration to cardiac autonomic dysfunction.
AI models were designed using PD risk factors as covariates to predict CVD/stroke risk, validated against established outcomes.

Main Results:

A strong link between PD and CVD/stroke was established, primarily through neurodegeneration-induced cardiac autonomic dysfunction.
The study validated the hypothesis that cardiac autonomic dysfunction is a fundamental cause of CVD/stroke damage in PD patients.
Novel AI solutions were developed for predicting CVD/stroke risk within the PD framework, demonstrating improved accuracy.

Conclusions:

Cardiac autonomic dysfunction resulting from neurodegeneration is a key factor linking PD to CVD and stroke.
The developed AI paradigm offers a robust tool for CVD/stroke risk stratification in Parkinson's disease patients.
Strategies for bias reduction in AI models for PD-related CVD/stroke risk prediction are recommended.

Read full analysis

Abstract:

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Keywords:

Parkinson’s disease artificial intelligence cardiac autonomic dysfunction cardiovascular disease deep learning machine learning recommendations stroke

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