Can hospices predict which patients will die within six months?

J Palliat Med. 2014 Aug;17(8):894-8. doi: 10.1089/jpm.2013.0631. Epub 2014 Jun 12.

Abstract

Objective: To determine whether it is possible to predict, at the time of hospice enrollment, which patients will die within 6 months.

Design: Electronic health record-based retrospective cohort study.

Setting: Patients admitted to 10 hospices in the CHOICE network (Coalition of Hospices Organized to Investigate Comparative Effectiveness).

Participants: Hospice patients.

Main outcome measures: Mortality at 6 months following hospice admission.

Results: Among 126,620 patients admitted to 10 hospices, 118,532 (93.6%) died within 6 months. In a multivariable logistic regression model, five characteristics were independent predictors of 6-month mortality. For instance, patients younger than 65 years were less likely to die within 6 months (odds ratio [OR] 0.64; 95% confidence interval [CI] 0.45-0.91; p=0.014). Conversely, male patients were more likely to die within 6 months (OR 1.47; 95% CI 1.05-2.02; p=;0.036). After adjusting for other variables in this model, there were several subgroups with a low probability of 6-month probability (e.g., stroke and Palliative Performance Scale [PPS] score=50; adjusted probability of 6-month mortality=39.4%; 95% CI: 13.9%-72.5%). However, 95% confidence intervals of these 6-month mortality predictions extended above 50%.

Conclusions: 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Disease Progression
  • Female
  • Hospices*
  • Humans
  • Length of Stay / statistics & numerical data
  • Male
  • Middle Aged
  • Mortality / trends*
  • Predictive Value of Tests
  • Probability
  • Retrospective Studies
  • Risk Factors
  • Sex Factors
  • Time Factors