Routine Blood Test May Predict Cancer Survival

A novel test uses results from a routine blood test to accurately determine cancer survival.

A routine blood test could predict how long a patient with cancer may survive in palliative care, which allows patients to receive the proper care, a recent study suggests.

“Patients with advanced cancer and their families have to make decisions about treatment, where to spend the end-of-life, and when to discontinue palliative chemotherapy,” said lead author Yu Uneno, MD. “Continuing ineffective therapy increases life-threatening adverse events, reduces quality of life, delays hospice referral, and deprives patients of the chance to die in their preferred place. Accurately predicting prognosis improves end-of-life care for cancer patients and their caregivers.”

Survival evaluations can help determine whether a patient should receive cancer treatment. Those with a short expected survival likely will not receive treatment with cytotoxic chemotherapy due to significant adverse events, according to a study presented at the ESMO Asia 2016 Congress in Singapore.

Treatment with midazolam can be used to relieve pain and other symptoms for patients receiving palliative care. However, extended treatment with the sedative can cause resistance, so the drug is only recommended for patients with limited survival. By gaining a better understanding of prognosis, patients will likely receive the appropriate treatment option, the authors noted.

Previous methods employed to predict prognosis used conditions such as dyspnea and delirium, and were only analyzed once. This method leaves significant variability between physicians, which may not be the most accurate method, according to the study.

The new method, the Six Adaptable Prognostic (SAP) models, uses measurements of albumin, neutrophil, and lactate dehydrogenase to determine cancer prognosis. These 3 measurements are typically monitored in standard blood tests.

Importantly, the SAP models can be used at any time during treatment, which is important since the patient’s condition may change over time, according to the study.

The models were created using more than 5000 patients with cancer receiving chemotherapy treatment. The researchers found that they were able to predict death within 6 months, and also allows physicians to re-evaluate prognosis at any point.

The test was developed from a sub-analysis of the J-ProVal study, which compared the ability of 4 models to predict cancer survival in real-world settings. Included in the study were 1015 patients, of whom 385 were based with palliative care teams in hospital, 464 were in palliative care units, and 166 received palliative care at home.

To determine the ability of the SAP models to predict survival in patients, the researchers conducted receiver operating characteristic analyses. They discovered that the area under the curve for predicting death within 1 to 3 months was 0.75 to 0.80.

"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,” Dr Uneno said. “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."