Quality of end-of-life care for patients with multiple myeloma: A 12-year analysis of a population-based cohort.
Notice bibliographique
Résumé
12031 Background: Despite treatment advances, multiple myeloma (MM) remains a significant source of morbidity and mortality. The end of life for patients with MM has not previously been examined within the context of a population-based cohort in a publicly funded health system. Methods: We retrospectively analyzed patients with death attributable to MM between 2006-2018 using ICES linked databases in the public health care system in Ontario, Canada. Aggressive care was defined as two or more emergency department visits in the last 30 days before death, at least two new hospitalizations within 30 days of death, or an ICU admission within 30 days of death. Supportive care was defined as physician house call 2 weeks before death, or a palliative nursing or personal support visit at home in last 30 days before death. Multivariable logistic regression models were used to assess for factors predisposing to aggressive or supportive care. Patients were stratified based on receipt of autologous stem cell transplant (ASCT). Results: In total, 5095 patients were included (Table). Overall, 23.2% of patients received chemotherapy in last two weeks of life and 55.6% of patients died in the hospital. Most patients were admitted to hospital within the last 30 days of life (73.4%:ASCT cohort, 61.4%:non-ASCT cohort). A minority received aggressive care at end of life (28.3%:ASCT cohort, 20.4%:non-ASCT cohort), and a majority received supportive care at end of life (65.4%:ASCT cohort, 61.5%:non-ASCT cohort). Multivariate regression models showed that patients ≥ 80 years (compared to 60-69) were less likely to receive aggressive care (OR=0.54, 95% CI=0.42-0.68), and those with residence in smaller size community of < 10,000 were more likely to receive aggressive care (OR=1.89, 95% CI=1.5-2.4). Supportive care was significantly less likely to be received by patients (OR=0.72, 95% CI= 0.59 to 0.88) and more likely to be received by patients aged 18-49 (OR=1.9, 95% CI=1.2-3.1). Neighbourhoods with lowest income quintiles (OR=0.65, 95% CI=0.53-0.78) were less likely to receive supportive care. When trended over time, patients receiving supportive care at end of life increased (56.0% in 2006 to 70.3% in 2018). Conclusions: We demonstrate that despite improvements over time, a substantial number of patients with MM experience aggressive care and hospitalizations at the end of life. Despite this being a publicly funded system, disparities in end-of-life care based on age, income and area of residence are present.[Table: see text]
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».