Machine learning-based identification of the target population for angiotensin-neprilysin inhibition given its heterogeneous treatment effect: a post-hoc analysis of the PARADIGM-HF and PARAGON-HF trials

Machine learning-based identification of the target population for angiotensin-neprilysin inhibition given its heterogeneous treatment effect: a post-hoc analysis of the PARADIGM-HF and PARAGON-HF trials

Heart failure is a major health problem affecting millions in the US and causing a significant number of deaths. In 2018, it was a contributing factor in about 13.4% of all recorded deaths [1].Researchers are exploring a new drug called sacubitril-valsartan as a potential treatment for heart failure. The PARADIGM-HF trial showed promising results, indicating a 20% reduction in the risk of cardiovascular death or heart failure hospitalization when compared to another drug called enalapril in patients with reduced ejection fraction heart failure (HFrEF) in 2014 [2]. Ejection fraction (EF) is a measurement, expressed as a percentage, of how much blood the left ventricle pumps out with each contraction. An ejection fraction of 60 percent means that 60 percent of the total amount of blood in the left ventricle is pushed out with each heartbeat. A normal heart's ejection fraction is between 55 and 70 percent.However, the PARAGON-HF trial had less clear-cut results in 2019 [3]. It found no significant reduction in the risk of total heart failure hospitalizations and cardiovascular mortality when sacubitril-valsartan was compared to valsartan in patients with preserved ejection fraction heart failure (HFpEF). The differences in outcomes could be due to variations in levels of a substance called neprilysin (an enzyme), different underlying causes of heart failure, and the patient's gender [4,5]. In other words, the benefits of sacubitril-valsartan may be more pronounced in patients with mildly reduced ejection fraction and may vary between men and women.Currently, there are no specific guidelines on who should receive sacubitril-valsartan. Our goal is to use machine learning, an AI-based approach, to analyze data from the PARADIGM-HF and PARAGON-HF trials. By doing so, we hope to uncover hidden factors that determine how effective sacubitril-valsartan is for different patients. This information can aid doctors in making more informed decisions about prescribing sacubitril-valsartan.[References]1. Virani SS, Alonso A, Benjamin EJ, et al. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020;141(9):e139-e596. 2. McMurray JJ, Packer M, Desai AS, et al. Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med. 2014;371(11):993-1004. 3. Solomon SD, McMurray JJV, Anand IS, et al. Angiotensin-Neprilysin Inhibition in Heart Failure with Preserved Ejection Fraction. N Engl J Med. 2019;381(17):1609-1620. 4. O'Connor CM, deFilippi C. PARAGON-HF - Why We Do Randomized, Controlled Clinical Trials. N Engl J Med. 2019;381(17):1675-1676. 5. Solomon SD, Vaduganathan M, L Claggett B, et al. Sacubitril/Valsartan Across the Spectrum of Ejection Fraction in Heart Failure. Circulation. 2020;141(5):352-361.

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