Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Abstract

Introduction: Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.

Methods: We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.

Results: Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.

Conclusion: Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

Keywords: LVAD; artificial intelligence; deep learning; heart transplantation; machine learning; mechanical circulatory support.

Figure 1

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only (4). ^*Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases). **Exclusion criteria included: review articles, meta-analyzes, conference abstracts, non-English language, animal and ex-vivo studies, non-AI methods, and those whose primary outcome is in the phase of care prior to transplantation or mechanical circulatory support.

Figure 2

Landscape overview of artificial intelligence applications in advanced heart failure practice, with annotations indicative the level of maturity of the available literature of each application; ¹Promising, but not yet mature for clinical use. ²Good support, ready for prospective testing. ³Theoretical potential, but no/negligible support.