Cargando…

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

BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-perfo...

Descripción completa

Detalles Bibliográficos
Autores principales: Mitchell, Joshua D, Lenihan, Daniel J, Reed, Casey, Huda, Ahsan, Nolen, Kim, Bruno, Marianna, Kannampallil, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9663613/
https://www.ncbi.nlm.nih.gov/pubmed/36386406
http://dx.doi.org/10.1177/11795468221133608
_version_ 1784830919699333120
author Mitchell, Joshua D
Lenihan, Daniel J
Reed, Casey
Huda, Ahsan
Nolen, Kim
Bruno, Marianna
Kannampallil, Thomas
author_facet Mitchell, Joshua D
Lenihan, Daniel J
Reed, Casey
Huda, Ahsan
Nolen, Kim
Bruno, Marianna
Kannampallil, Thomas
author_sort Mitchell, Joshua D
collection PubMed
description BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. METHODS: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. RESULTS: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. CONCLUSION: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive.
format Online
Article
Text
id pubmed-9663613
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-96636132022-11-15 Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center Mitchell, Joshua D Lenihan, Daniel J Reed, Casey Huda, Ahsan Nolen, Kim Bruno, Marianna Kannampallil, Thomas Clin Med Insights Cardiol Original Research BACKGROUND: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. METHODS: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. RESULTS: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. CONCLUSION: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive. SAGE Publications 2022-11-14 /pmc/articles/PMC9663613/ /pubmed/36386406 http://dx.doi.org/10.1177/11795468221133608 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page(https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Mitchell, Joshua D
Lenihan, Daniel J
Reed, Casey
Huda, Ahsan
Nolen, Kim
Bruno, Marianna
Kannampallil, Thomas
Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title_full Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title_fullStr Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title_full_unstemmed Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title_short Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center
title_sort implementing a machine-learning-adapted algorithm to identify possible transthyretin amyloid cardiomyopathy at an academic medical center
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9663613/
https://www.ncbi.nlm.nih.gov/pubmed/36386406
http://dx.doi.org/10.1177/11795468221133608
work_keys_str_mv AT mitchelljoshuad implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT lenihandanielj implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT reedcasey implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT hudaahsan implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT nolenkim implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT brunomarianna implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter
AT kannampallilthomas implementingamachinelearningadaptedalgorithmtoidentifypossibletransthyretinamyloidcardiomyopathyatanacademicmedicalcenter