A B S T R A C T PurposeTo determine which hospice patients with cancer prefer to die at home and to define factors associated with an increased likelihood of dying at home. MethodsAn electronic health record-based retrospective cohort study was conducted in three hospice programs in Florida, Pennsylvania, and Wisconsin. Main measures included preferred versus actual site of death. ResultsOf 7,391 patients, preferences regarding place of death were determined at admission for 5,837 (79%). After adjusting for other characteristics, patients who preferred to die at home were more likely to die at home (adjusted proportions, 56.5% v 37.0%; odds ratio [OR], 2.21; 95% CI, 1.77 to 2.76). Among those patients (n ϭ 3,152) who preferred to die at home, in a multivariable logistic regression model, patients were more likely to die at home if they had at least one visit per day in the first 4 days of hospice care (adjusted proportions, 61% v 54%; OR, 1.23; 95% CI, 1.07 to 1.41), if they were married (63% v 54%; OR, 1.35; 95% CI, 1.10 to 1.44), and if they had an advance directive (65% v 50%; OR, 2.11; 95% CI, 1.54 to 2.65). Patients with moderate or severe pain were less likely to die at home (OR, 0.56; 95% CI, 0.45 to 0.64), as were patients with better functional status (higher Palliative Performance Scale score: Ͻ 40, 64.8%; 40 to 70, 50.2%; OR, 0.79; 95% CI, 0.67 to 0.93; Ͼ 70, 40.5%; OR, 0.53; 95% CI, 0.35 to 0.82). ConclusionIncreased hospice visit frequency may increase the likelihood of patients being able to die in the setting of their choice.

Hospices might use several variables to identify patients with a relatively low risk for 6-month mortality and who therefore may become ineligible to continue hospice services if they fail to show significant disease progression.

Objectives To describe the trajectory of patients’ functional decline after they are referred to hospice. Design Electronic health record-based retrospective cohort study. Setting Three hospice programs in the southeast, northeast and Midwest US. Participants Hospice patients. Main outcome measures Palliative Performance Scale (PPS) scores measured at intervals between hospice enrollment and death, on a scale from 10–100. Results Of 8,669 decedents, there was an average 13.8-point decline in PPS scores. After adjusting for baseline PPS scores and length of stay in hospice, three distinct trajectories were identified, each of which was composed of two diagnoses whose rates of decline had 95% confidence intervals that overlapped. The most rapid decline was observed for patients with cancer (adjusted decline of 8.44 points/week; 95% CI 8.03–8.82) and stroke (7.67; 7.08–8.29). A significantly slower decline was observed in patients with pulmonary disease (5.02; 4.24–5.75) and cardiac disease (4.53; 4.05–5.05). Patients with debility (1.86; 0.95–2.78) and dementia (1.98; 1.01–2.89) had the slowest decline. In an inverse probability-weighted sample of patients who had a PPS score recorded in the last day of life (n=1,959; 22.6%), 35.9% had PPS scores of at least 40, indicating some oral intake, variable mental status, limited self-care, and an ability to get out of bed for at least part of the day. Conclusion Although functional status generally declines among hospice patients, this decline is heterogeneous. Some patients retain some physical and cognitive function on up until the last day of life.

Objective: To determine whether a prognostic index could predict one-week mortality more accurately than hospice nurses can. Method: An electronic health record-based retrospective cohort study of 21,074 hospice patients was conducted in three hospice programs in the Southeast, Northeast, and Midwest United States. Model development used logistic regression with bootstrapped confidence intervals and multiple imputation to account for missing data. The main outcome measure was mortality within 7 days of hospice enrollment. Results: A total of 21,074 patients were admitted to hospice between October 1, 2008 and May 31, 2011, and 5562 (26.4%) died within 7 days. An optimal predictive model included the Palliative Performance Scale (PPS) score, admission from a hospital, and gender. The model had a c-statistic of 0.86 in the training sample and 0.84 in the validation sample, which was greater than that of nurses' predictions (0.72). The index's performance was best for patients with pulmonary disease (0.89) and worst for patients with cancer and dementia (both 0.80). The index's predictions of mortality rates in each index category were within 5.0% of actual rates, whereas nurses underestimated mortality by up to 18.9%. Using the optimal index threshold ( < 3), the index's predictions had a better c-statistic (0.78 versus 0.72) and higher sensitivity (74.4% versus 47.8%) than did nurses' predictions but a lower specificity (80.6% versus 95.1%). Conclusions: Although nurses can often identify patients who will die within 7 days, a simple model based on available clinical information offers improved accuracy and could help to identify those patients who are at high risk for short-term mortality.