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Record W3215116288 · doi:10.1186/s12904-021-00850-y

Prevalence and characteristics of patients with heart failure needing palliative care

2021· article· en· W3215116288 on OpenAlexaboutno aff
Luisa Fernanda Arenas Ochoa, Valentina González-Jaramillo, Clara Saldarriaga, Mariantonia Lemos, Alicia Krikorian, John Jairo Vargas, Xavier Gómez‐Batiste, Nathalia González-Jaramillo, Steffen Eychmüller

Bibliographic record

VenueBMC Palliative Care · 2021
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePsychosocialPalliative careQuality of life (healthcare)Heart failureCross-sectional studyCardiomyopathyNeeds assessmentPhysical therapyInternal medicineFamily medicinePsychiatryNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Few hospitals and heart failure (HF) clinics offer concurrent palliative care (PC) together with life-prolonging therapies. To know the prevalence of patients in HF clinics needing PC and useful tools to recognize them are the first steps to extending PC in those settings. However, it is still unknown whether tools commonly used to identify patients with HF needing PC can correctly distinguish them. Two systematic reviews found that the NECesidades PALiativas (NECPAL) tool was one of the two most commonly used tools to asses PC needs in HF patients. Therefore, we assessed 1) the prevalence of PC needs in HF clinics according to the NECPAL tool, and 2) the characteristics of the patients identified as having PC; mainly, their quality of life (QoL), symptom burden, and psychosocial problems. METHODS: This cross-sectional study was conducted at two HF clinics in Colombia. We assessed the prevalence of PC in the overall sample and in subgroups according to clinical and demographic variables. We assessed QoL, symptom burden, and psychosocial problems using the 12-Item Short-Form Health Survey (SF-12), the Kansas City Cardiomyopathy Questionnaire (KCCQ) and the Edmonton Symptom Assessment System (ESAS). We compared the results of these tools between patients identified as having PC needs (+NECPAL) and patients identified as not having PC needs (-NECPAL). RESULTS: Among the 178 patients, 78 (44%) had PC needs. The prevalence of PC needs was twice as nigh in patients NYHA III/IV as in patients NYHA I/II and almost twice as high in patients older than 70 years as in patients younger than 70 years. Compared to -NECPAL patients, +NECPAL patients had worse QoL, more severe shortness of breath, tiredness, drowsiness, and pain, and more psychosocial problems. CONCLUSION: The prevalence of PC needs in outpatient HF clinics is high and is even higher in older patients and in patients at more advanced NYHA stages. Compared to patients identified as not having PC needs, patients identified as having PC needs have worse QoL, more severe symptoms, and greater psychosocial problems. Including a PC provider in the multidisciplinary team of HF clinics may help to assess and cover these needs.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.280
Teacher spread0.257 · 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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

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