New models based on test results may help doctors and patients decide whether and when to discontinue aggressive treatment.
A routine blood test can predict how long cancer patients in palliative care will survive, researchers reported at an oncology conference in Singapore this week.
Researchers at Kyoto University Hospital in Japan developed six adaptable prognostic (SAP) models that use laboratory measurements for albumin, neutrophil, and lactate dehydrogenase, which are routinely monitored with a blood test. The six models were developed in approximately 5,000 cancer patients receiving chemotherapy at Kyoto University Hospital in Japan, and can be used at any time point after the initiation of treatment.
The models predicted death within one to six months, allowing physicians to re-estimate prognosis at any point after beginning chemotherapy, according to a statement released at the ESMO Asia 2016 Congress.
The study tested the predictive value of the models in cancer patients receiving palliative care. It was designed as a sub-analysis of the Japan–prognostic assessment tools validation (J-ProVal) study, which compared the ability of four models to predict survival of advanced cancer patients in the real world.
This sub-analysis included 1015 patients, of whom 385 were based with palliative care teams in hospital, 464 were in palliative care units, and 166 were receiving palliative care services at home. The investigators performed receiver operating characteristic (ROC) analysis to calculate the ability of the SAP models to predict death in cancer patients in the palliative care setting. The area under the curve (AUC) for predicting death within 1–3 months ranged from 0.75 to 0.80.
Previous models for predicting prognosis used subjective conditions such as dyspnoea and delirium, which may be scored differently by clinicians. These conditions were assessed once (at the start of treatment, for example), limiting the use of the models to that time point.
“We found that the SAP models had a good ability to predict that a patient would die in one to three months. The prediction was accurate in 75–80% of cases,” said Yu Uneno, M.D., an oncologist at Kyoto University and lead author of the study, in the statement. “The SAP models could be a promising decision aid for healthcare professionals and patients. Accurate prediction of survival allows patients adequate time to prepare for their impending death and is vital for planning effective palliative care.”
Knowing the patient’s prognosis will help physicians, patients and families evaluate the need for anti-cancer therapy and treatments for relieving symptoms, added Grace Yang, M.D., a consultant in the division of palliative medicine at the National Cancer Centre Singapore.
“For example, in deciding between pain-relieving treatments with different time horizons for onset and duration of effect, with different side effects/ risk profiles and different financial costs,” Yang said in the statement. “As we get closer to knowing how long a cancer patient has to live, further studies exploring the ethical and psychological implications will also be worthwhile.”
Nancy Crotti is a contributor to Qmed.
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