ReviewOpen heart2022
Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK.
Review in Open heart, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis of heart failure with preserved ejection fraction: a systematic narrative review of the evidence.Heart failure reviews · 2024Pooled it
- Impact of a durable left ventricular assist device on inflammation and cardiac remodelling in subjects with advanced heart failure.Open heart · 2026Observational
- 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
- Development and validation of algorithms to predict left ventricular ejection fraction class from healthcare claims data.ESC heart failure · 2024Article
- Temporal Risk of Nonfatal Cardiovascular Events After Chronic Obstructive Pulmonary Disease Exacerbation: A Population-based Study.American journal of respiratory and critical care medicine · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining heart failure (HF) phenotypes in routine electronic health records (EHR) is challenging. We aimed to develop and validate EHR algorithms for identification of specific HF phenotypes, using Read codes in combination with selected patient characteristics.
methodsWe used The Healthcare Improvement Network (THIN). The study population included a random sample of individuals with HF diagnostic codes (HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF) and non-specific HF) selected from all participants registered in the THIN database between 1 January 2015 and 30 September 2017. Confirmed diagnoses were determined in a randomly selected subgroup of 500 patients via GP questionnaires including a review of all available cardiovascular investigations. Confirmed diagnoses of HFrEF and HFpEF were based on four criteria. Based on these data, we calculated a positive predictive value (PPV) of predefined algorithms which consisted of a combination of Read codes and additional information such as echocardiogram results and HF medication records.
resultsThe final cohort from which we drew the 500 patient random sample consisted of 10 275 patients. Response rate to the questionnaire was 77.2%. A small proportion (18%) of the overall HF patient population were coded with specific HF phenotype Read codes. For HFrEF, algorithms achieving over 80% PPV included definite, possible or non-specific HF HFrEF codes when combined with at least two of the drugs used to treat HFrEF. Only in non-specific HF coding did the use of three drugs (rather than two) contribute to an improvement of the PPV for HFrEF. HFpEF was only accurately defined with specific codes. In the absence of specific coding for HFpEF, the PPV was consistently below 50%.
conclusionsPrescription for HF medication can reliably be used to find HFrEF patients in the UK, even in the absence of a specific Read code for HFrEF. Algorithms using non-specific coding could not reliably find HFpEF patients.
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