Myth: We can tailor palliative care to match life expectancy. Reality: No we can't

The goal of palliative treatment is to return the patient to an optimal functional level in the shortest period of time with the fewest side effects. As Nora Janjan wrote, palliative therapy should ‘‘efficiently provide comfort using antineoplastic therapies or supportive care approaches to the patient with the fewest treatment-related side effects, recognizing that the patient will die of the disease. ’’ For patients with terminal cancer, quality of life is more important than tumor control. Treatment courses given at the end of life are frequently too protracted, keeping the patients on treatment and in the clinic rather than at home with friends and family. That is one conclusion that Gripp et al have drawn from their series of patients, reported in this issue of Cancer. Unfortunately, they seem to have drawn their conclusions first and then tried to find the data to match those conclusions. Although it is an admirable (and, in my view, appropriate) conclusion, it cannot be justified from their data. Gripp et al prospectively evaluated 216 patients referred to the University Hospital Dusseldorf Department of Radiation Oncology for palliative radiation therapy. They used 3 methods to estimate survival time for these patients and have previously reported those results. The further analysis in the current issue of Cancer reviewed the 33 patients who survived for 30 days or less. Not surprisingly, the median survival in this group was only 15 days. Because the typical prescribed radiation therapy treatment course was for 2 or more weeks, a substantial number of these patients were not able to complete treatment. Gripp et al conclude that palliative radiation therapy is not appropriately customized for patients at the end of life, especially not for those patients who have prolonged irradiation schedules. What is wrong with this conclusion? In Gripp et al’s previous study, they showed that there is a poor correlation between predicted survival and actual survival for individual patients. There is a correlation between the predicted and actual survivals for the entire cohort of patients, but this correlation is rather weak. There have been multiple other attempts to correlate some factor (physician prediction, performance status, symptoms, biochemical markers such as albumin levels, quality of life scores, etc) with survival times. All of these attempts have come to the same conclusion we can predict the general survival trend, but we are lousy at predicting the survival time for any individual patient. In addition, physician predictions of survival tend to be overly optimistic, overestimating survival time for the majority of patients. The problem from the Gripp et al study is not that the therapy was not appropriately customized; instead, it is the implication that we can appropriately customize the treatment. If we can’t accurately predict survival times, how can we attempt to fit shorteror longer-course treatments based on predicted survival times? In the current study, Gripp et al collected these survival predictions in a prospective fashion, but no apparent attempt was made to tailor the treatment prospectively based on those predictions. They then retrospectively took the poorest-performing group of patients (those who died in the first 30 days) to tell us that treatment was not effective.Well, taken in retrospect, this seems obvious but is this analysis of any use to us? Not particularly. This bit of information would be much more useful if we knew it before treatment, not after the fact. Unfortunately, they have already told us that they were not