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Record W3188941058 · doi:10.1093/eurjcn/zvab060.029

Provision of palliative care to adults with congenital heart disease at the end of life

2021· article· en· W3188941058 on OpenAlexaff
Liesbet Van Bulck, Elise Goossens, Koen Luyckx, Fouke Ombelet, Ruben Willems, Michèle de Hosson, Lieven Annemans, Werner Budts, Julie De Backer, S Moniotte, Arianne Marelli, Katya De Groote, Philip Moons

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2021
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsPalliative careMedicineFamily medicineDiseaseHeart diseaseEnd-of-life careGerontologyPediatricsNursingInternal medicine

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was supported by Research Foundation Flanders [grant numbers G097516N to PM, 12E9816N to EG and 1154719N to LVB]; the King Baudouin Foundation (Fund Joseph Oscar Waldmann-Berteau & Fund Walckiers Van Dessel); the National Foundation on Research in Pediatric Cardiology; and the Swedish Research Council for Health, Working Life and Welfare -FORTE (grant number STYA-2018/0004). OnBehalf BELCODAC consortium Background Although recent position papers have discussed and advocated for the integration of palliative care in the treatment course of adults with congenital heart defects (CHD), empirical studies reporting to what extent palliative care is currently provided, are still lacking. Purpose (1) To explore the current provision of palliative care to adults with CHD in the last 6 months of their life; and (2) to describe the profile of patients who received palliative care. Methods In this retrospective study, data of deceased adult patients included in the BELgian COngenital heart disease Database combining Administrative and Clinical data (BELCODAC) were analysed. Palliative care provision (i.e., admission to palliative care ward, or palliative care at home) was identified using nomenclature codes. The level of anatomical complexity was based on the Bethesda classification. Descriptive analyses were performed. Results During the period 2006-2016, 480 adults with CHD died (mean age: 54.4y; 45% simple CHD, 43% moderate CHD, 12% complex CHD). We identified that 75 patients (16%) had at least one nomenclature code linked to palliative care in the last 6 months of their life. More specifically, 16 patients were admitted to an inpatient palliative care service and 67 patients received palliative care at home. Of the patients who received palliative care at home, 40 patients were cared for by a multidisciplinary team specialized in palliative care provision and 59 patients received care from nurses and/or general practitioners while being recognized as a palliative patient. A total of 8 patients received palliative care both at the inpatient palliative care service and at home. Of the 75 patients receiving palliative care, 44 (59%) had a neoplasm as the primary cause of death and a cardiac cause of death was reported for 10 patients (13%) (see Figure 1). The mean age of patients receiving palliative care was 57.9 years. Most patients receiving palliative care had a simple CHD (n = 40; 53%), 29 patients (39%) had a moderate lesion, and 6 patients (8%) had a complex lesion. That means that, respectively, 19%, 14%, and 11% of all deceased patients with a simple, moderate, and complex heart lesion received palliative care. Conclusions This is the first exploratory study on palliative care in adults with CHD. About one in six patients who died received palliative care. Of those who received palliative care, the cause of death was in most cases of a non-cardiac nature. Further research is needed to investigate the care trajectories and care needs of adults with CHD in the last months of life. Figure 1. Causes of death of adults with CHD who received palliative care in the last 6 months of life (n = 75).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.255
Teacher spread0.240 · 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 teacher head, 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".

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Citations1
Published2021
Admission routes1
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