ArticleInternational journal of medical informatics2018
Identifying heart failure using EMR-based algorithms.
Article in International journal of medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic accuracy of case-identification algorithms for heart failure in the general population using routinely collected health data: a systematic review.Systematic reviews · 2024Pooled it
- A population-based study of 92 clinically recognized risk factors for heart failure: co-occurrence, prognosis and preventive potential.European journal of heart failure · 2022Pooled it
- Accuracy of Electronic Health Record-Based Definitions for Patients with Heart Failure.The American journal of medicine · 2025Article
- A phenotyping algorithm for classification of single ventricle physiology using electronic health records.JAMIA open · 2025Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- Establishment of reliable identification algorithms for acute heart failure or acute exacerbation of chronic heart failure using clinical data from a medical information database network.Frontiers in cardiovascular medicine · 2025Article
- Social Determinants of Health Mediate Racial Disparities in Cardiovascular Disease in Men With Prostate Cancer.JACC. CardioOncology · 2024Article
- The impact of multimorbidity and functional limitation on quality of life in patients with heart failure: A multi-site study.Journal of the American Geriatrics Society · 2024Article
- A large, multi-center survey assessing health, social support, literacy, and self-management resources in patients with heart failure.BMC public health · 2024Article
- Article
- The clinical burden of newly diagnosed Heart failure among patients with Reduced, mildly Reduced, and preserved ejection fraction.International journal of cardiology. Heart & vasculature · 2023Article
- Trends in Incidence of Chronic Heart Failure in Patients With Rheumatoid Arthritis: A Population-Based Study Validating Different Heart Failure Definitions.The Journal of rheumatology · 2023Article
- Health Care Utilization and Death in Patients With Heart Failure During the COVID-19 Pandemic.Mayo Clinic proceedings. Innovations, quality & outcomes · 2023Article
- Trends and opportunities in computable clinical phenotyping: A scoping review.Journal of biomedical informatics · 2023Article
- Predicting Alzheimer's Disease and Related Dementias in Heart Failure and Atrial Fibrillation.The American journal of medicine · 2023Article
- Prediction of left ventricular ejection fraction changes in heart failure patients using machine learning and electronic health records: a multi-site study.Scientific reports · 2023Article
- A multicenter program for electronic health record screening for patients with heart failure with preserved ejection fraction: Lessons from the DELIVER-EHR initiative.Contemporary clinical trials · 2022Review
- Alzheimer's disease and related dementias and heart failure: A community study.Journal of the American Geriatrics Society · 2022Article
- Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety.Journal of biomedical informatics · 2022Article
- Article
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8 authors.
Funding
Abstract
backgroundHeart failure (HF) is a major clinical and public health problem, the management of which will benefit from large-scale pragmatic research that leverages electronic medical records (EMR). Requisite to using EMRs for HF research is the development of reliable algorithms to identify HF patients. We aimed to develop and validate computable phenotype algorithms to identify patients with HF using standardized data elements defined by the Patient Centered Outcomes Research Network (PCORnet) Common Data Model (CDM).
methodsWe built HF computable phenotypes utilizing the data domains of HF diagnosis codes, prescribed HF-related medications and N-terminal B-type natriuretic peptide (NT-proBNP). Algorithms were validated in a cohort (n = 76,254) drawn from Olmsted County, MN between 2010-2012 a sample of whose records were manually reviewed to confirm HF according to Framingham criteria.
resultsThe different algorithms we tested provided different tradeoffs between sensitivity and positive predictive value (PPV). The highest sensitivity (78.7%) algorithm utilized one HF diagnosis code and had the lowest PPV (68.5%). The addition of more algorithm components, such as additional HF diagnosis codes, HF medications or elevated NT-proBNP, improved the PPV while reducing sensitivity. When added to a diagnostic code, the addition of NT-proBNP (>450 pg/mL) had a similar impact compared to additional HF medication criteria, increasing PPV by ∼3-4% and decreasing sensitivity by ∼7-10%.
conclusionsAlgorithms derived from PCORnet CDM elements can be used to identify patients with HF without manual adjudication with reasonable sensitivity and PPV. Algorithm choice should be driven by the goal of the research.
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