Evidence map›Paper›PMID 38070890›Full record

ArticleBMJ open2023

Risk of atrial fibrillation and association with other diseases: protocol of the derivation and international external validation of a prediction model using nationwide population-based electronic health records.

Ramesh Nadarajah, Jianhua Wu, Ronen Arbel, Moti Haim, Doron Zahger, Talish Razi Benita, Lior Rokach, J Campbell Cowan, Chris P Gale

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05837364 (Risk of Atrial Fibrillation and Association With Other Diseases), which is not on this map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
1.1field-weighted citation impact, top 19% of its field
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.

NCT05837364 completednot on this map

Risk of Atrial Fibrillation and Association With Other Diseases: Protocol of the Derivation and International External Validation of a Prediction Model Using Nationwide Population-based Electronic Health Records

TypeobservationalSponsorUniversity of LeedsRan2020 to 2023Enrolled2,159,663ConditionsAtrial Fibrillation, Arrhythmias, Cardiac, Heart Diseases, Cardiovascular DiseasesArmsDevelopment of an algorithm
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Article
  3. Article
  4. 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

9 authors at 4 institutions in 2 countries.

Ramesh NadarajahLeeds Institute of Data Analytics, University of Leeds, Leeds, UK r.nadarajah@leeds.ac.uk.ORCID 0000-0001-9895-9356
Jianhua WuLeeds Institute of Data Analytics, University of Leeds, Leeds, UK.
Ronen ArbelHealth Systems Management, Ben-Gurion University of the Negev, Beer-Sheva, Israel.ORCID 0000-0002-6058-8665
Moti HaimDepartment of Cardiology, Soroka University Medical Center, Beer Sheva, Israel.
Doron ZahgerSoroka University Medical Center, Beer Sheva, Israel.
Talish Razi BenitaBen-Gurion University of the Negev, Beer-Sheva, Israel.
Lior RokachDepartment of Information Systems and Software Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
J Campbell CowanDepartment of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
Chris P GaleLeeds Institute of Data Analytics, University of Leeds, Leeds, UK.
Ben-Gurion University of the Negev · ILUniversity of Leeds · GBLeeds Teaching Hospitals NHS Trust · GBSoroka Medical Center · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAtrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies. METHODS AND ANALYSIS: We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states. ETHICS AND DISSEMINATION: Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences. TRIAL REGISTRATION NUMBER: A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).

Indexed as

Atrial FibrillationStrokeElectronic Health RecordsHumansIncidenceSystematic Reviews as TopicElectronic Health RecordsPacing & electrophysiologyPREVENTIVE MEDICINEPrimary Care

Identifiers

PMID38070890
PMCPMC10729260
OpenAlexW4389504942

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.