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Enregistrement W2983033302 · doi:10.1182/blood-2019-129428

Improving End of Life Care for Hematology-Oncology Patients within the Rossy Cancer Network

2019· article· en· W2983033302 sur OpenAlexaffabout
Victoria Korsos, Alla'a Ali, Doneal Thomas, Kelly Davison, Sarit Assouline, Luca A. Petruccelli, Chantal Cassis

Notice bibliographique

RevueBlood · 2019
Typearticle
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensJewish General HospitalMcGill University Health CentreMcGill University
Organismes subventionnairesnon disponible
Mots-clésMedicinePalliative careInternal medicineHematologyCancerAdvance care planningHematologic NeoplasmsEmergency medicineIntensive care medicine

Résumé

récupéré en direct d'OpenAlex

Introduction: The clinical course of hematology-oncology patients differs from patients with solid malignancies as these patients are more likely to be admitted to receive life sustaining measures near end of life (EOL). In a survey conducted among hematologist-oncologists, EOL indicators validated for medical oncology patients and considered significant for hematology-oncology patients, included not being admitted to intensive care, intubated or receiving CPR within 30 days of death, not receiving chemotherapy within 14 days or a blood transfusion within 7 days of death, and dying outside of an acute care unit (Odejide et al., JCO 2016). To better understand EOL trajectories for patients with hematological malignancies, we conducted a retrospective chart review using the Rossy Cancer Network (RCN) registry of the McGill University hospitals, Montreal, Canada. The objectives were 1) to describe the demographics, trajectory and physician-established goals of therapy (GOT) (remission, slow progression, palliative) for patients from their final admission to hospital to death, 2) measure adherence during that period of time with regard to the six quality EOL indicators outlined above and 3) measure how palliative care (PC) involvement, level of intervention (LOI) discussions and physician GOT impacted performance on these indicators. Methods: Using the RCN registry, we identified patients who died from hematological malignancies between April 2014 and March 2016 (n=749) at the four participating McGill hospitals. Inclusion criteria required that the patient have a hematological malignancy confirmed by pathology, be treated at a McGill University hospital, and that the cause of death be related directly to the malignancy or its treatment. We performed retrospective chart reviews to delineate patient trajectories. All ICU, hematology and PC consultations, LOI, progress notes, discharge summaries, pharmacy prescriptions and death forms needed to be available for the chart to be considered complete. Median duration of last hospitalization to LOI discussion, PC consultation and death was determined. In addition, performance on all six EOL indicators was measured and we assessed the impact of physician's GOT, PC involvement and early LOI discussion on these indicators. The chi-square test was used to compare categorical variables. Results: Of the 749 patients assessed in the registry, 322 met all inclusion criteria. 427 were excluded: 215 did not meet inclusion criteria, 182 died outside of the established time window, 26 were duplicate entries and 4 of the charts were incomplete. The registry included 132 patients with lymphoma, 110 patients with leukemia, 53 patients with myeloma and 24 patients with myelodysplastic syndrome. The median number of days from admission to death was 15 (interquartile range (IQR) 6-36). The most common patient trajectory was a LOI discussion 9.5 days (IQR 4-22) and PC consultation 9 days (IQR 3.5-19.5) prior to death. For the whole cohort, 62% of patients had a consultation with PC prior to death and 34% of patients had a documented LOI prior to their last admission. The treating physician's GOT was to induce remission in 22%, to slow progression in 40% and to provide palliation in 37% of patients at the time of their last hospital admission. In addition, 17% of patients were administered chemotherapy less than 14 days prior to death, 20% were admitted to ICU, 14% were intubated and 5% received CPR less than 30 days prior to death, 18% received blood transfusion less than 7 days prior to death and 67% died in an acute care setting. EOL indicators significantly improved when stratified by physician GOT (p < 0.01, 6/6 indicators), a PC consult was completed (p < 0.001, 5/6 indicators), and a LOI discussion was held prior to admission (p < 0.02, 5/6 indicators). Conclusions: In this study we demonstrate a relationship between LOI discussions, PC consults and physician established GOT on EOL quality indicators for patients with hematological malignancies. Our findings suggest that setting appropriate GOT and having timely LOI discussions and early PC involvement may improve EOL experiences for patients. We plan to implement prospective quality improvement initiatives aimed at these factors and measure the performance on these EOL indicators. Ultimately, our goal is to improve the quality of end of life care for patients. Disclosures Off Label Use: Pembrolizumab - PD-1 inhibitor. Assouline:Pfizer: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Abbvie: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,284
Score d'incertitude au seuil0,565

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,041
Tête enseignante GPT0,360
Écart entre enseignants0,319 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission2
Résumé présentoui

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