Predictors of Unplanned Hospitalizations Among Older Adults Receiving Cancer Chemotherapy

Purpose: Hospitalizations during cancer treatment are costly, can impair quality of life, and negatively affect therapy completion. Our objective was to identify risk factors for unplanned hospitalization among older adults receiving chemotherapy.

Methods: This is a secondary analysis of a multisite cohort study (N = 750) of patients ≥ 65 years of age evaluated with a geriatric assessment (GA) to predict chemotherapy toxicity. The primary outcome of this analysis was unplanned hospitalizations during treatment; the secondary outcome was length of stay (LOS) of the first hospitalization. Independent variables included pretreatment GA measures, laboratory values, cancer type and stage, and treatment intensity characteristics. We used logistic regression to estimate the odds of hospitalization and generalized linear models for LOS in multivariable analyses.

Results: The sample median age was 72 years (range, 65-94 years); 59% had stage IV disease. At least one unplanned hospitalization occurred in 193 patients (25.7%) during receipt of chemotherapy. In multivariable analyses controlling for cancer type, the following baseline characteristics were significantly associated with increased odds of hospitalization: needing help bathing or dressing (odds ratio [OR], 1.8; 95% CI, 1.0 to 3.1), polypharmacy (≥ 5 meds) (OR, 1.6; 95% CI, 1.1 to 2.4), more comorbid conditions (OR, 1.1; 95% CI, 1.0 to 1.3), availability of someone to take them to the doctor (OR, 2.0; 95% CI, 1.0 to 4.1), CrCl < 60 mL/min (OR, 1.7; 95% CI, 1.1 to 2.4), and albumin < 3.5 g/dL (OR, 1.8; 95% CI, 1.2 to 2.8). In multivariable analyses, older age, self-reported presence of liver or kidney disease, living alone and depressive symptoms were associated with longer LOS.

Conclusion: Readily available GA variables and laboratory data, but not age, were associated with unplanned hospitalizations among older adults receiving chemotherapy. If validated, these data can inform prediction models and the design of interventions to decrease unplanned hospitalizations.