Evidence map›Paper›PMID 31870354›Full record

ArticleBMC health services research2019

Outcomes of aortic aneurysm surgery in England: a nationwide cohort study using hospital admissions data from 2002 to 2015.

Ahmed Aber, Thaison Tong, Jim Chilcott, Ravi Maheswaran, Steven M Thomas, Shah Nawaz, Jonathan Michaels

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.7field-weighted citation impact, top 25% 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.

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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
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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

7 authors at 2 institutions in 1 country.

Ahmed AberSchool of Health and Related Research, University of Sheffield, Sheffield, UK. a.aber@sheffield.ac.uk.ORCID http://orcid.org/0000-0002-5707-2430
Thaison TongSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Jim ChilcottSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Ravi MaheswaranSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
Steven M ThomasSheffield Vascular Institute, Sheffied Teaching Hospitals, Sheffield, UK.
Shah NawazSheffield Vascular Institute, Sheffied Teaching Hospitals, Sheffield, UK.
Jonathan MichaelsSchool of Health and Related Research, University of Sheffield, Sheffield, UK.
University of Sheffield · GBSheffield Teaching Hospitals NHS Foundation Trust · GB

Funding

Department of Health RP-PG-1210-12009Programme Grants for Applied Research RP-PG-1210-12009
6 · The paper itself

Abstract

backgroundThe United Kingdom aortic aneurysms (AA) services have undergone reconfiguration to improve outcomes. The National Health Service collects data on all hospital admissions in England. The complex administrative datasets generated have the potential to be used to monitor activity and outcomes, however, there are challenges in using these data as they are primarily collected for administrative purposes. The aim of this study was to develop standardised algorithms with the support of a clinical consensus group to identify all AA activity, classify the AA management into clinically meaningful case mix groups and define outcome measures that could be used to compare outcomes among AA service providers.

methodsIn-patient data about aortic aneurysm (AA) admissions from the 2002/03 to 2014/15 were acquired. A stepwise approach, with input from a clinical consensus group, was used to identify relevant cases. The data is primarily coded into episodes, these were amalgamated to identify admissions; admissions were linked to understand patient pathways and index admissions. Cases were then divided into case-mix groups based upon examination of individually sampled and aggregate data. Consistent measures of outcome were developed, including length of stay, complications within the index admission, post-operative mortality and re-admission.

resultsSeveral issues were identified in the dataset including potential conflict in identifying emergency and elective cases and potential confusion if an inappropriate admission definition is used. Ninety six thousand seven hundred thirty-five patients were identified using the algorithms developed in this study to extract AA cases from Hospital episode statistics. From 2002 to 2015, 83,968 patients (87% of all cases identified) underwent repair for AA and 12,767 patients (13% of all cases identified) died in hospital without any AA repair. Six thousand three hundred twenty-nine patients (7.5%) had repair for complex AA and 77,639 (92.5%) had repair for infra-renal AA.

conclusionThe proposed methods define homogeneous clinical groups and outcomes by combining administrative codes in the data. These methodologically robust methods can help examine outcomes associated with previous and current service provisions and aid future reconfiguration of aortic aneurysm surgery services.

Indexed as

State MedicineAortic AneurysmCohort StudiesDatasets as TopicDiagnosis-Related GroupsEnglandHospitalizationHumansTreatment OutcomeAdministrative datasetAortic aneurysmHospital episode statisticsOutcomes

Identifiers

PMID31870354
PMCPMC6929362
OpenAlexW2997305409

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.