Treatment of HF in an Era of Multiple Therapies: Statement From the HF Collaboratory

Statement From the HF Collaboratory

Ankeet S. Bhatt, MD, MBA; William T. Abraham, MD; JoAnn Lindenfeld, MD; Michael Bristow, MD; Peter E. Carson, MD; G. Michael Felker, MD, MHS; Gregg C. Fonarow, MD; Stephen J. Greene, MD; Mitchell A. Psotka, MD, PHD; Scott D. Solomon, MD; Norman Stockbridge, MD, PHD; John R. Teerlink, MD; Muthiah Vaduganathan, MD, MPH; Janet Wittes, PHD; Mona Fiuzat, PHARMD; Christopher M. O'Connor, MD; Javed Butler, MD, MPH, MBA

Disclosures

JACC Heart Fail. 2021;9(1):1-12. 

In This Article

Novel Approaches to Inform Future Decisions

As detailed earlier, each of the proposed options in isolation is inadequate. However, it soon may be possible for a patient with HFrEF to be eligible for ARNI, MRA, beta-blocker, SGLT2 inhibitor, ivabradine, a soluble guanylate cyclase stimulator, a myosin activator, combination vasodilator therapy, and intravenous iron therapy in addition to traditional diuretics, implantable device therapy, and pharmacotherapy for comorbid conditions. Therefore, current data must be leveraged more effectively and new data generated more efficiently to broadly implement evidence and create a structure for when choices needs to be made. Successful strategies will have to rely on the emergence of health data science and a deeper understanding of fundamental biology, pathophysiology, genomics, and phenotyping. These strategies may offer the opportunity to leverage data collection efforts from prior and ongoing trial-based and usual care settings in ways that can more readily inform patterns of therapy tailored for a particular patient.

Health Data Science Approaches

The emergence of novel data analytic tools and application of "artificial intelligence" into health care may provide unique opportunities to aid practitioners in drug selection, initiation, uptitration, and post-initiation monitoring in a complex field with an influx of new therapeutic options. Such approaches would collate data from multiple sources, including the electronic health records and prescription fill patterns, and suggest patterns of disease or patient presentations that might be more suitable for treatment with particular therapeutic combinations. These may be important considerations, particularly if patients are unwilling or unable to take a multitude of therapies due to drug–drug interactions, costs, or contraindications, allowing for greater precision in making clinical therapeutic choices. These approaches, while beginning to emerge, are subject to the limitations of the datasets from which they are derived, and must be continually updated as the choices in HF pharmacotherapy expand. Integration into electronic clinical decision support systems could be used to monitor patient characteristics, symptoms, health status, and biomarker profiles to offer suggestions to clinicians for drug use and titration based on trial criteria, comorbidities, or tolerability. Such systems may also allow for continuous monitoring of drug effects, elucidating earlier trends toward adverse effects and informing earlier clinical decisions surrounding follow-up, monitoring, or changes in pharmacological therapy.

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