Personalized Risk Stratification in Atrial Fibrillation using Portable, Explainable Artificial Intelligence

点击翻译按钮获取中文摘要

PROJECT SUMMARY/ABSTRACT Implementation of contemporary strategies to reduce stroke related to atrial fibrillation (AF) is limited by (1) rudimentary stroke risk stratification tools and (2) disparities in care and outcomes of AF. There remains a critical need for personalized, socially-aware, equitable stroke risk prediction among patients with AF, in order to optimally implement contemporary stroke-prevention therapies. A major long-term goal is to develop a portable, equitable risk-stratification tool to improve stroke-prevention among patients with AF. The objectives of this project are to (i) discover new risk-factor relationships for patients with AF that incorporate social determinants of health (SDoH), using an innovative comorbidity discovery framework (Poisson Binomial Comorbidity [PBC]); (ii) combine these with established risk factors using explainable, artificial-intelligence (AI) methods; and (iii) develop, deploy and test an augmented, personalized stroke risk stratification tool for AF patients across different health systems in a disparity-aware fashion. Our central hypothesis is that stroke prevention can be improved through methods that: leverage all available data, including SDoH; capture and quantify synergies among known and newly-discovered risk factors in socioeconomic context; and can be ported to other health systems, adapting to different populations. The rationale for this project is that current AF-related stroke risk management lacks the precision and awareness required to optimally implement treatments because it does not adequately account for (1) population diversity, (2) SDoH and disparities, (3) synergistic interactions among risk factors, and (4) novel, emerging risk factors. The central hypothesis will be tested by pursuing three specific aims: 1) Discover new clinical and socioeconomic relationships that determine stroke risk in patients with AF; 2) Develop a socially-conscious, AI-based machinery for calculating personalized stroke risk among patients with AF; and 3) Benchmark an AI-based, socially-aware stroke risk predictor across a diverse cohort of health systems using PCORnet and use it to discover biases and drivers of downstream care disparities. In the first aim, the PBC approach will be used to leverage large datasets that include SDoH, in order identify new risk markers. The second aim will focus on building novel, Probabilistic Graphical Models (PGMs) to understand the impact of SDoH on AF-related stroke risk. In the third aim, the models will be tested across a diverse set of healthcare systems to understand portability, diversity, and bias. The research proposed in this application is innovative because it (1) leverages uniquely-available data on SDoH, (2) employs a much more powerful and portable analytic approach to understand risk; and (3) is designed with an eye towards understanding and reducing disparities and bias in risk prediction models. The proposed research is significant because it will improve care across the spectrum of patients with AF, while at the same time addressing disparities and bias in the present standard of care. Ultimately, the results will yield a much more personalized and equitable approach to stroke prevention in the setting of AF.