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Record W4400015388 · doi:10.1016/j.hlc.2024.04.301

Assessing the Role of Echocardiography in Pregnancy in First Nations Australian Women: Is it an Underutilised Resource?

2024· article· en· W4400015388 on OpenAlexaboutno aff
James Marangou, Dominic Ferguson, Holger W. Unger, Alex Kaethner, Marcus Ilton, Bo Remenyi, Anna P. Ralph

Bibliographic record

VenueHeart Lung and Circulation · 2024
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsMedicinePregnancyIndigenousReferralHeart diseasePopulationPediatricsRetrospective cohort studyObstetricsDiseaseFamily historyInternal medicineFamily medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Rheumatic heart disease (RHD) remains prevalent within First Nations Australian communities. RHD is more common in females and peak prevalence corresponds with childbearing age. Significant valvular disease can complicate pregnancy. Current practice in Northern Australia is to refer pregnant women for echocardiography if there are signs or symptoms of possible cardiac pathology or a history of acute rheumatic fever (ARF) or RHD. It is not currently routine practice to offer echocardiographic screening for all pregnant women at high risk of RHD. AIM: This study aimed to assess the current referral practices for echocardiography and disease patterns in pregnant women in the Northern Territory, Australia-a region with a known high prevalence of RHD in the First Nations population. METHOD: A retrospective analysis of all echocardiography referrals of pregnant women over a 4-year period was performed. Data included indication for echocardiography, clinical history, echocardiographic findings, and location of delivery. Comparisons were made using Fisher's exact and Mann-Whitney U tests. RESULTS: A total of 322 women underwent echocardiography during pregnancy: 195 First Nations and 127 non-Indigenous women (median age, 25 vs 30 years, respectively; p<0.01). Indications for echocardiography differed by ethnicity, with history of ARF or RHD being the most common indication in First Nations women, and incidental murmur the most common in non-Indigenous women. First Nations women were more likely to have abnormal echocardiograms (35.9% vs 11.0% in non-Indigenous women; p<0.01) or a history of ARF or RHD (39.5% vs 0.8%; p<0.01), but less likely to have documented cardiac symptoms as an indication for echocardiography (8.2% vs 20.5%; p<0.01). New cardiac diagnoses were made during pregnancy in 11 (5.6%) First Nations and two (1.6%) non-Indigenous women (p=0.02). Moderate or severe valve lesions were detected in 26 (13.3%) First Nations women (all previously diagnosed), and 11 (5.6%) had previous cardiac surgery. No severe valve lesions were identified in the non-Indigenous group. Interstate transfer to a tertiary centre with valve intervention services was required during pregnancy or the puerperium for 12 (6.2%) First Nations women and no non-Indigenous women. CONCLUSIONS: Amongst pregnant women in the Northern Territory who had an indication for echocardiography, First Nations women were more likely to have abnormal echocardiograms. This was mainly due to valvular disease secondary to RHD. Cardiac symptoms were infrequently recorded as an indication for echocardiography in First Nations women, suggesting possible underappreciation of symptoms. Having a low threshold for echocardiographic investigation, including consideration of universal screening during pregnancy, is important in a high RHD-burden setting such as ours. A better understanding of the true prevalence and spectrum of disease severity in this population would enable health services to invest in appropriate resources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.034
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.342
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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