Applying diagnosis support systems in electronic health records to identify wild-type transthyretin amyloid cardiomyopathy risk

Connor Willis, Alexandre H. Watanabe, Justin Hughes, Kimberly Nolen, Jason O'meara, Alexander Schepart, Marianna Bruno, Joseph Biskupiak, Kensaku Kawamoto, Nawar Shara, Thomas Kannampallil

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Aim: Wild-type transthyretin amyloid cardiomyopathy (ATTRwt-CM) is frequently misdiagnosed, and delayed diagnosis is associated with substantial morbidity and mortality. At three large academic medical centers, combinations of phenotypic features were implemented in electronic health record (EHR) systems to identify patients with heart failure at risk for ATTRwt-CM. Methods: Phenotypes/phenotype combinations were selected based on strength of correlation with ATTRwt-CM versus non-amyloid heart failure; different clinical decision support and reporting approaches and data sources were evaluated on Cerner and Epic EHR platforms. Results: Multiple approaches/sources showed potential usefulness for incorporating predictive analytics into the EHR to identify at-risk patients. Conclusion: These preliminary findings may guide other medical centers in building and implementing similar systems to improve recognition of ATTRwt-CM in patients with heart failure.

Original languageEnglish
Pages (from-to)367-376
Number of pages10
JournalFuture Cardiology
Volume18
Issue number5
DOIs
StatePublished - May 2022

Keywords

  • amyloidosis
  • artificial intelligence
  • cardiomyopathy
  • diagnosis
  • electronic health record
  • heart failure
  • machine learning
  • screening
  • transthyretin

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