Evidence map›Paper›PMID 36332942›Full record

ReviewOpen heart2022

Development of algorithms for determining heart failure with reduced and preserved ejection fraction using nationwide electronic healthcare records in the UK.

Varun Sundaram, Rosita Zakeri, Klaus K Witte, Jennifer Kathleen Quint

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Observational
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Varun Sundaram *National Heart and Lung Institute, Imperial College London, London, UK.
Rosita Zakeri *King's College London, London, UK.
Klaus K WitteDivision of cardiovascular and Diabetes Research, University of Leeds, Leeds, UK.ORCID 0000-0002-7146-7105
Jennifer Kathleen QuintNational Heart and Lung Institute, Imperial College London, London, UK j.quint@imperial.ac.uk.ORCID 0000-0003-0149-4869

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Heart FailureAlgorithmsDelivery of Health CareElectronicsHumansStroke VolumeUnited KingdomElectronic Health RecordsEpidemiologyHeart Failure

Identifiers

PMID36332942
PMCPMC9639145

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.