Diagnostic Performance Evaluation of Clinical and Artificial Intelligence Risk Models in Patients Referred for Cardiac Amyloidosis Testing.

Armin Garmany¹, Jose K James², Gregorio Tersalvi², Patricia Carey³, Christopher G Scott⁴, Will Hawkes⁵, Ashley Akerman⁵, Ross Upton⁵, Angela Dispenzieri⁶, Martha Grogan², Omar F AbouEzzeddine², Patricia A Pellikka⁷

¹Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota.

²Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota.

³Department of Medicine, Mayo Clinic, Rochester, Minnesota.

⁴Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota.

⁵Ultromics, Oxford, United Kingdom.

⁶Division of Hematology, Mayo Clinic, Rochester, Minnesota.

⁷Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota. Electronic address: pellikka.patricia@mayo.edu.

PubMed PMC DOI PDF

Abstract

BACKGROUND: To improve screening for cardiac amyloidosis (CA), several models using artificial intelligence (AI) and conventional statistics have been developed. However, few data are available to compare the relative utility of these tools. In this study, models were compared to determine their potential roles in optimizing diagnostic algorithms. METHODS: In this retrospective cohort study at a…

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