Evidence map›Paper›PMID 31744503›Full record

ArticleBMC medicine2019

Bleeding in cardiac patients prescribed antithrombotic drugs: electronic health record phenotyping algorithms, incidence, trends and prognosis.

Laura Pasea, Sheng-Chia Chung, Mar Pujades-Rodriguez, Anoop D Shah, Samantha Alvarez-Madrazo, Victoria Allan, James T Teo, Daniel Bean, Reecha Sofat, Richard Dobson and 5 more

Abstract read
In one paragraph

Article in BMC medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–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

9 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. [Pathophysiology of bleeding].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2021
    Review
  9. 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

15 authors.

Laura PaseaHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Sheng-Chia ChungHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Mar Pujades-RodriguezLeeds Institute of Health Sciences, University of Leeds, Leeds, UK.
Anoop D ShahHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Samantha Alvarez-MadrazoHealth Data Research UK Scotland, Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK.
Victoria AllanHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
James T TeoDepartment of Stroke and Neurology, King's College Hospital NHS Foundation Trust, London, UK.
Daniel BeanDepartment of Biostatistics and Health Informatics, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.
Reecha SofatDepartment of Clinical Pharmacology, University College London Hospital NHS Foundation Trust, London, UK.
Richard DobsonHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Amitava BanerjeeHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Riyaz S PatelInstitute of Health Informatics, University College London, London, UK.
Adam TimmisBart's Heart Centre, Queen Mary University London, London, UK.
Spiros DenaxasHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK.
Harry HemingwayHealth Data Research UK, University College London, 222 Euston Road, London, NW1 2DA, UK. h.hemingway@ucl.ac.uk.ORCID 0000-0003-2279-0624

Funding

Arthritis Research UKBritish Heart Foundation FS/14/76/30933Cancer Research UKChief Scientist OfficeMedical Research Council G0902393/99558Medical Research Council MR/K006584/1Medical Research Council MR/M015084/1Medical Research Council MR/S00310X/1Wellcome Trust
6 · The paper itself

Abstract

backgroundClinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy.

methodsWe examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998-2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina with the aim to develop algorithms for bleeding events. Using the developed bleeding phenotypes, Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess the prognosis for all-cause mortality, atherothrombotic events and further bleeding.

resultsWe present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures and haemoglobin values. In validation of the phenotype, we estimated a positive predictive value of 0.88 (95% CI 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27,259 (21.2%) had at least 1 bleeding event, with 5-year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina, respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% CI 0.83, 1.22) in January 1998 to 2.68 (95% CI 2.49, 2.88) in December 2009 coinciding with the increased rates of antiplatelet and vitamin K antagonist prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example, the hazard ratio for all-cause mortality was 1.98 (95% CI 1.86, 2.11) for primary care bleeding with markers of severity and 1.99 (95% CI 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding.

conclusionsElectronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects one in four cardiovascular disease patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic.

Indexed as

AgedAlgorithmsAnticoagulantsAntithrombinsElectronic Health RecordsEnglandFemaleHeart DiseasesHemorrhageHumansIncidenceMalePrognosisRisk FactorsAnticoagulantsAntithrombinsAntithrombotic therapyBleedingElectronic health recordsPhenotypePrognosis

Identifiers

PMID31744503
PMCPMC6864929

What OpenQuestion holds

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LicenceCC BY
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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.