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Incorporating Routine Survival Prediction in a U.S. Hospital-Based Palliative Care Service

2010; Mary Ann Liebert, Inc.; Volume: 13; Issue: 12 Linguagem: Inglês

10.1089/jpm.2010.0152

1096-6218

Erik K. Fromme, Mary Denise Smith, Paul B. Bascom, Tawni Kenworthy-Heinige, Karen S. Lyons, Susan W. Tolle,

Cancer survivorship and care

Prognostication is a core component of palliative care consultation. We sought to incorporate predicted survival into the routine practice of our hospital-based palliative care team.The predicted survival was determined by the physician and/or nurse at the time of initial palliative care consultation using categories that parallel the rough time frames often shared with patients and used in planning care: (1) ≤3 days, (2) 4 days to 1 month, (3) >1 month to 6 months, (4) >6 months. One year later, survival status at 6 months was determined using death certificates, the Social Security online database, and other methods.Over 1 year, complete data were obtained for 429 of 450 (95.3%) consecutive new patient consults. Patients' mean and median age was 63, 48.5% had cancer, 83% were Caucasian, and 50% were female. For the 283 patients who were discharged alive, median survival was 18 days and 58 patients were still alive after 6 months. Fifty-eight percent of patients were assigned to the correct survival category, whereas 27% of prognoses were too optimistic and 16% were too pessimistic. In logistic regression analysis, predicted survivals of ≤3 days were much more likely to be accurate than longer predictions.The team recorded a predicted survival in 95% of new patient consults. Fifty-eight percent accuracy is in line with prior literature. Routinely incorporating survival prediction into palliative care consultation raised a number of questions. What decisions were made based on the 42% incorrect prognoses? Did these decisions negatively affect care? Survival prediction accuracy has potential as a quality measure for hospital-based palliative care programs, however to be truly useful it needs to be shown to be "improveable" and the downstream effects of predictions need to be better understood.