The impact of admitting ward on resource utilization and outcomes among hospitalized cancer survivors.

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.