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Record W4283783982 · doi:10.1093/eurjcn/zvac060.028

Healthcare use at the end of life of patients with congenital heart disease: does heart failure matter?

2022· article· en· W4283783982 on OpenAlexaff
Liesbet Van Bulck, Elise Goossens, Luc Morin, Koen Luyckx, Fouke Ombelet, Ruben Willems, Werner Budts, Katya De Groote, Julie De Backer, Stéphane Moniotte, Michèle de Hosson, Arianne Marelli, Philip Moons

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2022
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsMcGill University Health Centre
FundersForskningsrådet om Hälsa, Arbetsliv och VälfärdKoning BoudewijnstichtingFonds Wetenschappelijk OnderzoekEuropean Society of Cardiology
KeywordsMedicineHeart failureCarvedilolMedical prescriptionBisoprololHeart diseaseDigoxinLoop diureticEmergency departmentSpironolactoneInternal medicineIntensive care medicineEmergency medicinePediatrics

Abstract

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Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Research Foundation Flanders (to PM, EG, and LVB) European Society of Cardiology (Nursing Training Grant to LVB) Background Heart failure (HF) is a common cause of morbidity and mortality in patients with congenital heart disease (CHD). Although limited in scope, previous studies suggest that patients with heart failure follow a specific end-of-life trajectory with episodes of serious complications, which may impact the patterns of care as death approaches. Aims The study aims to identify differences in characteristics and patterns of care in the last year of life in deceased CHD patients with and without HF. Methods This retrospective study used data of deceased adult patients included in the BELgian COngenital heart disease Database combining Administrative and Clinical data (BELCODAC). To describe patterns of care in the last year of life, we captured information about hospitalisations, emergency department visits, and visits to the general practitioner using nomenclature codes. Heart failure was identified as having HF as cause of death and/or at least one prescription of a loop diuretic in the last year of life. Sensitivity analyses with a stricter definition for HF (HF as cause of death or ≥ 1 prescription of a loop diuretic combined with a prescription of digoxin, dopamine, dobutamine, other non-glycoside stimulants, metoprolol, bisoprolol, carvedilol, aldosterone antagonists, ACE inhibitors or ARBs) were performed as well. Results During the period 2007–2016, 390 adults with CHD died, of which 170 patients with HF (44%). Patients with HF were older, died more often due to a cardiovascular cause of death, and had more complex heart lesions, compared to patients without HF (Table 1). While the number of emergency department visits and hospitalisations in the last year was similar, patients with HF had almost twice as much monthly visits at the general practitioner in their last year of life (Table 1). As shown in Figure 1, the mean number of hospitalisations and emergency department visits increased in a similar fashion throughout the last year of life, but the pattern of general practitioner visits was substantially different for patients with and without HF. The sensitivity analyses, in which a stricter definition for HF was used, yield very similar results. In these analyses, the difference in mean monthly hospitalisations was also significant between the two groups. Conclusions This study shows clinically important differences in characteristics and patterns of care of deceased patients with CHD with and without heart failure. Patients with HFhave different needs and should receive a tailored approach at the end of life. Future research is needed to understand these differences and investigate these patients' end-of-life care needs in more detail. Funding acknowledgments: This work was supported by Research Foundation Flanders; European Society of Cardiology; the King Baudouin Foundation; the National Foundation on Research in Pediatric Cardiology; and the Swedish Research Council for Health, Working Life and Welfare-FORTE.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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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Citations0
Published2022
Admission routes1
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