Machine Learning Informs a New Tool to Guide Treatment for Acute Decompensated Heart Failure

February 6, 2024 — A current examine co-authored by Dr. Matthew Segar, a third-year heart problems fellow at The Texas Heart Institute and led by his analysis and residency mentor, University of Texas Southwestern Medical Center’s Dr. Ambarish Pandey, utilized a machine learning-based method to establish, perceive, and predict diuretic responsiveness in sufferers with acute decompensated coronary heart failure (ADHF).
The examine “A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure,” revealed within the prestigious Journal of American College Cardiology Heart Failure (JACC Heart Failure), leverages many years of medical and registry datasets funded by the National Institutes of Health and American Heart Association.
The researchers leveraged machine-learning-based approaches to develop a prediction instrument known as the BAN-ADHF rating, which confirmed promising leads to precisely predicting diuretic response. After validation in different medical populations, implementing this instrument might probably lead to customized methods for successfully managing the congestion of sufferers hospitalized with ADHF.
There stays a lack of settlement amongst consultants relating to the simplest method to deal with diuretic resistance in sufferers with coronary heart failure who’re secure hemodynamically and have an extra quantity of fluid. It is mostly really useful to optimize the dosage of loop diuretics earlier than contemplating mixture remedy; nonetheless, there isn’t any consensus on how a lot the dosage ought to be elevated earlier than introducing one other diuretic.
“Inefficient diuretic response in hospitalized sufferers can hinder remedy progress and enhance the chance of post-discharge rehospitalization and mortality. It’s essential to establish people with low diuretic effectivity early on to tailor decongestion methods and enhance medical outcomes,” in accordance to Dr. Segar.
ADHF is a public well being difficulty that’s changing into more and more regarding. The illness leads to emergency room visits, hospital admissions, and related excessive healthcare prices. ADHF is characterised by the physique having an excessive amount of fluid, which regularly requires hospitalization or altering a affected person’s present remedy plan.
“Today, a major purpose of treating ADHF is to relieve congestion utilizing loop diuretic medicine. However, there’s nonetheless uncertainty about the most effective dose of those brokers to administer. Additionally, due to the heterogeneity of ADHF sufferers, a extra customized method to predicting optimum dosing methods is required,” mentioned Dr. Joseph G. Rogers, President and CEO of The Texas Heart Institute.
In the examine, researchers from establishments throughout the United States utilized machine studying (ML) algorithms to establish subgroups of sufferers with acute coronary heart failure based mostly on their responsiveness to diuretic remedy. Specifically, the researchers developed a diuretic effectivity phenomapping method for sufferers with ADHF by utilizing publicly accessible and deidentified knowledge from a number of medical trials and registries, together with DOSE, ROSE-AHF, CARRESS-HF, ATHENA-HF, ESCAPE, and the American Heart Association Precision Medicine Platform Get with the Guidelines-HF (GWTG-HF) registry. This participant-level pooled knowledge enabled the investigators to develop a phenomapping method and diuretic effectivity rating. The sufferers inside every subgroup shared comparable traits however had been clinically distinct from different subgroups, notably of their response to diuretic remedy. In addition to variations of their diuretic response, the affected person subgroups additionally had meaningfully completely different medical outcomes, highlighting the prognostic utility of the phenogrouping method. The investigators subsequently developed and validated the BAN-ADHF rating to predict the chance of being within the phenogroup with the least diuretic response.
“We know the BAN-ADHF rating can precisely establish, characterize, and predict diuretic resistance amongst people with ADHF mathematically. Now we should take this medical data and conduct a medical examine to consider whether or not implementing the BAN-ADHF rating in our care protocols improves outcomes for sufferers hospitalized with acute decompensated coronary heart failure,” shared Dr. Segar.
Notably, the work described on this examine acquired recognition from the National Institutes of Health’s National Heart, Lung, and Blood Institute (NHLBI) as a profitable resolution to the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The problem inspired the event of novel, open-source illness fashions to outline subgroups of coronary heart failure and help additional developments in managing the illness. Additionally, Dr. Segar acquired the American Heart Association’s Samuel A. Levine Early Career Clinical Investigator Award for his function in growing the phenomapping instrument and the diuretic resistance medical threat rating. As a part of the distinction, he introduced his analysis on “Development and Validation of a Phenomapping Tool To Identify Patients With Diuretic Resistance in Acute Decompensated Heart Failure: A Multi-Cohort Analysis” on the American Heart Association’s 2022 Scientific Sessions.
Study collaborators included investigators from The Texas Heart Institute, Duke University School of Medicine, Cleveland Clinic, Houston Methodist DeBakey Heart and Vascular Center, University of Mississippi Medical Center, Baylor Scott and White Research Institute, St. Vincent Heart Center, The University of Texas Southwestern Medical Center, Ronald Reagan UCLA Medical Center, Institute for Precision Cardiovascular Medicine on the American Heart Association, Stony Brook University School of Medicine, Northwestern University School of Medicine, and University of Colorado.
Segar MW, Khan MS, Patel KV, Butler J, Ravichandran AK, Walsh MN, Willett D, Fonarow GC, Drazner MH, Mentz RJ, Hall J, Farr MA, Hedayati SS, Yancy C, Allen LA, Tang WHW, Pandey A. A Phenomapping Tool and Clinical Score to Identify Low Diuretic Efficiency in Acute Decompensated Heart Failure. JACC Heart Fail. 2023 Dec 13:S2213-1779(23)00688-1. doi: 10.1016/j.jchf.2023.09.029. Online forward of print.
For extra data: www.texasheart.org

https://www.dicardiology.com/content/machine-learning-informs-new-tool-guide-treatment-acute-decompensated-heart-failure

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