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Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an

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Summary
This summary is machine-generated.

Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is an underdiagnosed heart failure cause. Electronic health record (EHR) phenotypes identified patients at risk for ATTR-CM, improving disease recognition.

Keywords:
Cardiac amyloidosiselectronic health recordidentificationmachine learningtransthyretin amyloidosis

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Genetics

Background:

  • Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a common, underdiagnosed cause of heart failure (HF) in elderly individuals.
  • Early identification of ATTR-CM is crucial for timely intervention and improved patient outcomes.
  • Electronic health records (EHRs) offer a valuable platform for developing tools to aid in disease detection.

Purpose of the Study:

  • To operationalize cardiac and non-cardiac phenotypes within EHR systems to identify patients at high risk for wild-type ATTR-CM.
  • To evaluate the effectiveness of different EHR configurations and data sources in detecting potential ATTR-CM cases.
  • To improve the early recognition of wild-type ATTR-CM in a clinical setting.

Main Methods:

  • A pilot program was implemented at a large academic medical center using Epic EHR configurations.
  • Nine cardiac/non-cardiac phenotypes and 20 phenotype combinations predictive of wild-type ATTR-CM were operationalized.
  • Inclusion criteria were age >50 years and HF; exclusion criteria included end-stage renal disease and prior amyloidosis diagnoses. Data sources included problem lists, diagnoses, medical history, and billing transactions.

Main Results:

  • Among 45,051 patients with HF, 8.9% (4006 patients) exhibited at least one phenotype combination associated with increased wild-type ATTR-CM risk.
  • The most effective phenotypes for identifying at-risk patients were cardiomegaly and osteoarthrosis.
  • Specific phenotype combinations, such as carpal tunnel syndrome with HF, and a four-phenotype combination including atrial fibrillation, heart block, cardiomegaly, and osteoarthrosis, showed high predictive value.

Conclusions:

  • All tested EHR configurations successfully operationalized phenotypes for identifying at-risk patients.
  • The Clarity EHR report demonstrated the most comprehensive approach to identifying patients for wild-type ATTR-CM screening.
  • Leveraging EHR data and phenotype combinations is a feasible strategy to enhance the detection of under-recognized conditions like wild-type ATTR-CM.