August 26, 2011
New Score Can Tell Doctors How Long Cancer Patients Have Left To Live
A new scoring system can more reliably predict whether patients with advanced cancer are likely to survive for "days", "weeks" or "months" finds a study published on bmj.com today.
Patients with advanced cancer 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.
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 Cancer Center 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."
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