Cancer patients and their loved ones often confront their health practitioners with the question, How long have I got left? Is there really an answer?
Sniffer dogs detecting lung cancer and modified ecstasy, seen as a potential treatment for myeloma, lymphoma and leukaemia, might be ground-breaking discoveries, but the disease remains a physical, mental and financial challenge for patients.
With the rising cost of cancer treatment, it could greatly help patients and healthcare providers to detect the length of survival in advanced cancer patients. Because much of clinical practice in the 21st century revolves around caring for patients with advanced, progressive, life-limiting illness, such prognostic tools are seen as the new path ahead.
A step forward has now come from a new scoring system, published online in the British Medical Journal, that may predict whether patients with advanced cancer are likely to survive for days, weeks or months, its authors say.
This information becomes important for clinicians to help plan appropriate care for their patients. Although clinical predictions of survival remain the mainstay of current practice, they are still unreliable and subjective. The new model, it is hoped, will bring a scientific and methodical perspective to cancer survival rates.
Dr. Patrick Stone, a professor of palliative care at the St. George's, University of London, says that under the Prognosis in Palliative care Study (PiPS) predictor model, researchers estimate how much a patient with advanced cancer has left after treatment is stopped. The researchers say the model's estimate of survival is clinically meaningful, reproducible and comparable across settings. But further validation is needed before recommending its routine use.
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 the extent of disease, were taken into account.
The study was conducted in 18 palliative care centres across the UK, involving 1,018 patients with locally advanced or metastatic cancer who had been recently referred to the centres. The median survival of the group was 34 days.
The research team got survival prognoses from both a doctor and a nurse. When there was disagreement, they were asked to discuss the case and arrive at an explicitly agreed-upon estimate.
Researchers found that the PiPS-A model (without blood test) performed as well as the clinicians (PiPS-A correct on 59.6 percent of cases and multi-professional predictions correct on 57.5 percent) while the PiPS-B model (with the blood test) performed significantly better than the doctors (61 vs. 52.6 percent P = .0135) and the nurses (61.5 vs. 52.3 percent P = .012) but not significantly better than the muti-professional estimate (61.5 vs. 53.7 percent; P = .188).
Study authors state, The PiPS models offer some definite advantages over existing methods of predicting survival in this population of patients. According to the authors, both the PiPS-A and the PiPS-B models have the advantage of being independent of the clinician's opinion and of being reproducible and comparable across settings.
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.
The authors say realistic survival estimates inform decisions about appropriateness of treatment and timing of referral to palliative care or hospice. In addition, patients with advanced cancer and their caregivers often wish to know how long they have left to live. That information can help them prepare for their impending death.
In an accompanying editorial, Dr. Paul Glare, chief of pain and palliative care at Memorial Sloan-Kettering Cancer Center in New York City, was not too optimistic about the model, however. He wrote, Ultimately, even a state-of-the-art prognostic tool like the PiPS predictor model will often be inaccurate. This is not surprising, as previous work has indicated that the kind of variables evaluated in this model fail to explain a large proportion of the variance seen in actual survival. Until this inherent inaccuracy is mitigated, doctors will continue to resist prognostication.
The study authors and their colleagues have noted that previous studies have found that clinician's predictions of survival are inaccurate and overly optimistic. They added that clinician's predictions are widely used and therefore any new prediction model that attempts to answer the question of survival could perhaps show that the PiPS model might be a reliable approach. But Glare disputes this, saying, Doctors are rarely trained in formulating prognoses, nor do they like doing it, so they try to avoid it.
Caution is, therefore, seen as the keyword while communicating results of the predication especially in this case where the forecast could sound the death knell for the patient.