Predictors of Potentially Burdensome Transitions of Care for Hospitalized Patients With Advanced Cancer
Notice bibliographique
Résumé
Background: Patients with advanced cancer experience frequent hospitalizations and potentially burdensome transitions of care post-discharge that could negatively impact the quality of their end-of-life care. We examined predictors of discharge location for patients with advanced cancer, including patient-reported physical and psychological symptoms, and assessed the relationship between discharge location and survival.\n\nMethods: We prospectively enrolled patients with advanced cancer who experienced an unplanned hospitalization at the Massachusetts General Hospital from September 2014 to March 2016. Upon admission, we assessed patients’ physical symptoms (Edmonton Symptom Assessment System [ESAS]; 0-10) and psychological distress (Patient Health Questionnaire 4 [PHQ-4]; 0-12). The PHQ-4 includes depression and anxiety subscales. We used logistic regression models to identify predictors of discharge to location other than home, including post-acute care (PAC) [skilled nursing facility or long term acute care hospital] or hospice [any setting]. We used Cox proportional hazards models adjusted for clinical variables to assess the relationship between discharge location and survival. \n\nResults: Out of 932 patients, 726 (77.9%) were discharged home, 118 (12.7%) to PAC and 88 (9.4%) to hospice. Compared with patients discharged home, those discharged to PAC or hospice had higher symptom burden, including dyspnea, constipation, low appetite, drowsiness, low wellbeing, fatigue, depression, and anxiety (all p < 0.05). Using logistic regression, patients not discharged home vs. home were more likely to be older (OR 1.03, p<0.0001), live alone (OR 1.95, 95%CI: 1.25-3.02, p<0.003), have impaired mobility (OR 5.08, 95%CI: 3.46-7.45, p<0.0001), longer hospital length-of-stay (OR 1.15, 95%CI: 1.11-1.20, p<0.0001), higher ESAS physical symptoms (OR 1.02, 95%CI: 1.003-1.032, p<0.017), and higher PHQ-4 depression symptoms (OR 1.13, 95%CI: 1.01-1.25, p<0.027). Patients discharged to hospice vs. PAC (reference) were more likely to receive palliative care consultation (OR 4.44, 95% CI: 2.12 to 9.29, p < 0.0001) and have shorter length of stay (OR 0.84, 95% CI: 0.77 to 0.91, p < 0.0001). Compared with patients discharged home, those discharged to PAC had lower survival (HR 1.53, 95% CI 1.22-1.93, p < 0.0001).\n\nConclusions: Patients with advanced cancer discharged to PAC or hospice have substantial physical and psychological symptom burden and poor physical function, and those discharged to PAC have similar symptom burden and clinical characteristics compared to those discharged to hospice, except for higher rates of palliative care consultation and shorter lengths-of-stay for the hospice group. Patients discharged to PAC also have inferior survival compared with those discharged home. This study has identified a sub-population of patients with advanced cancer discharged to PAC after an unplanned admission, which may benefit from targeted interventions to reduce potentially burdensome care transitions and improve the quality of their end of life care. Future studies should attempt to replicate these findings in a larger, more diverse population, and explore the role of care financing issues and patient preferences in driving post-discharge decision-making at the end of life.
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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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».