The impact of admitting ward on resource utilization and outcomes among hospitalized cancer survivors.
Notice bibliographique
Résumé
36 Background: With improvements in both early detection and cancer treatment, there is a growing population of cancer survivors; with a corresponding increase in acute care use. However, models of inpatient care delivery for cancer survivors differ between hospitals and regions, which may impact resource use and outcomes. Understanding how different models influence outcomes may help define optimal models for cancer inpatient care delivery. Methods: We created a multicenter cohort of all cancer patients admitted to medical wards across 26 hospitals in Ontario, Canada from 2015 to 2022, and deterministically linked population level administrative data including ambulatory oncology data, with each hospital’s patient-level electronic information (pharmacy, orders, notes, labs/imaging and results). Multivariable regression models compared characteristics and outcomes between patients admitted on oncology wards vs non-oncology wards adjusting for age, sex, income quintile, rurality, immigrant status, receiving IV systemic therapy within 120 days and comorbidity scores. Results: In total, there were 370,118 hospitalizations from 191,990 unique patients. Among these hospitalizations, 38,075 episodes (10%) were on an oncology ward. Median time from cancer diagnosis to hospitalization was 4 years; 10% received IV systemic therapy within 120 days and 16% within 1 year. The most common disease sites were GU (21%), GI (20%), breast (12%) and lung (10%). The most common discharge diagnoses from oncology wards were inpatient chemotherapy (9%), febrile neutropenia (7%), NHL (4%), AML (4%), myeloma (3%); while for non-oncology wards were heart failure (5%), palliative care (4%), UTI (2%), pneumonia (2%), acute renal failure (2%). In general, cancer patients admitted on oncology wards were younger (64 vs 76), had shorter length of stay (LOS; 9.6 vs 10.1 days), less in-hospital mortality (8% vs 11%), greater 30-day re-admission rates (30% vs 15%) and were more likely to undergo CTs (28% vs 21%), MRIs (11% vs 9%) and interventional procedures (8% vs 6%) (p<0.001, all). Subgroup analysis focusing on the top 5 discharge diagnoses from non-oncology wards, showed that despite no difference in in-hospital mortality rates (aOR 0.92 95% CI [0.58-1.46] p=0.73), admission to a non-oncology ward for those diagnoses was associated with shorter LOS (aOR 0.84 [0.78-0.90] p<0.001), reduced 30-day re-admission rates (aOR 0.60 [0.48-0.75] p<0.001), and reduced use of CTs (aOR 0.60 [0.49-0.74] p<0.001), MRIs (aOR 0.36 [0.25-0.52] p<0.001), and interventional procedures (aOR 0.43 [0.29-0.64] p<0.001). Conclusions: There are differences in resource use and outcomes for cancer survivors hospitalized on oncology versus non-oncology wards, including for patients with the same discharge diagnosis. To optimize inpatient cancer care delivery for hospitalized cancer survivors, further exploration of care models is needed.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».