UK researchers have developed a new scoring system that could accurately predict the remaining life expectancy of patients with advanced cancer in terms of "days", "weeks" or "months".
Patients and their carers often wish to know how long they
have left to live. This information is also important for clinicians to help
them plan appropriate care. Clinician predictions of survival are the mainstay
of current practice, but are unreliable, over-optimistic and subjective.
So a team of researchers, led by Dr Paddy Stone at St George's, University
of London, set out to
develop a scoring system for use in patients with advanced cancer in different
care settings that was as good, or better, than clinicians' best predictions.
The study involved 1,018 patients with advanced incurable
cancer, no longer receiving treatment, and recently referred to palliative care
services across the UK.
Using a combination of clinical and laboratory variables
known to predict survival, the team created two prognostic scores (PiPS-A and
PiPS-B) to predict whether patients were likely to survive for "days" (0-13
days), "weeks" (14-55 days) or "months" (more than 55 days) compared with
actual survival and clinicians' predictions.
Factors that could have affected the results, such as age,
gender, ethnicity, diagnosis, and extent of disease, were taken into account.
Both scores were at least as accurate as a clinician's
estimate. PiPS-B (which required a blood test) was significantly better than an
individual doctor's or nurse's prediction, but neither scale was significantly
more accurate than a multi-professional estimate of survival.
This is the first study to benchmark a prognostic scoring
system against current best practice, say the authors. However, further
validation work is needed before the scales can be recommended for use in
routine clinical practice, they conclude.
In an accompanying editorial, Paul Glare from the Memorial Sloan-Kettering
in New York
believes that prognosis "needs to be restored as a core clinical skill, to
optimise the patient's treatment and planning."
He says that prognostic tools can help, but should not
be applied blindly, and he points out that "communicating the prediction to the
patient is as important as forecasting it."