Transforming Heart Failure Treatment: Leveraging CausalML Powered Real-World Evidence for Personalized Guideline-Directed Medical Therapy

Music City Center 

Heart failure (HF) is a complex clinical syndrome with a significant global burden. Recent clinical trials and observational studies have demonstrated that guideline-directed medical therapy (GDMT) for heart failure, encompassing both preserved and reduced ejection fraction phenotypes, can significantly reduce cardiovascular mortality, heart failure hospitalization, and all-cause mortality.
The current GDMT recommendations include four medication classes: β-blockers, renin-angiotensin-aldosterone system inhibitors (angiotensin receptor neprilysin inhibitors or angiotensin-converting enzyme inhibitors/angiotensin receptor blockers), sodium-glucose cotransporter-2 inhibitors, and mineralocorticoid receptor antagonists. However, real-world studies have revealed challenges in implementing GDMT due to increased polypharmacy and treatment complexity, leading to suboptimal initiation and titration of medications.
Several implementation strategies have been developed to improve the usage of GDMT, but most of them focus on treatment for various stages of heart failure with limited sample sizes. There is a critical need for a personalized GDMT model that can assess different treatment plans based on individual patient characteristics to maximize improvements in patient outcomes.
In this talk, we propose a causal Machine Learning (causalML) method-based predictive modeling approach. This innovative data-driven method aims to evaluate individual treatment effectiveness, potentially supporting clinical decision-making for more tailored patient care. The model is designed to generate timely treatment suggestions that could serve as an additional resource for clinicians to develop personalized approaches for HF patients. Additionally, this work shares learning experiences and lessons learned regarding the application of causal machine learning methods to real-world evidence studies.
Keywords: Causal Machine Learning, Personalized Medicine, Guideline-Directed Medical Therapy, Heart Failure, Real-World Evidence, Real-World Data

Causal Machine Learning

personalized medicine

guideline-directed medical therapy

Heart Failure

real-world evidence

real-world data