Applying diagnosis support systems in electronic health records to identify wild-type transthyretin amyloid cardiomyopathy risk.
Citation: Future Cardiology. 2022 Jan 31PMID: 35098741Institution: MedStar Health Research InstituteForm of publication: Journal ArticleMedline article type(s): Journal ArticleSubject headings: IN PROCESS -- NOT YET INDEXEDYear: 2022Abstract: 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.Fiscal year: FY2022Digital Object Identifier: ORCID:- Hughes, Justin:
- https://orcid.org/0000-0002-3067-9902
| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
| Journal Article | MedStar Authors Catalog | Article | 35098741 | Available | 35098741 |
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.
English