Simple Tool Predicts Survival in Advanced Cancer

August 28, 2014
Janet Colwell
Janet Colwell

Using a simple tool to measure performance status can help physicians predict survival and facilitate end-of-life planning in outpatients with advanced cancer.

Using a simple tool to measure performance status can help physicians accurately predict survival and facilitate end-of-life planning in outpatients with advanced cancer, a recent study reported.

Researchers asked oncologists at a large outpatient palliative care clinic to complete three commonly used performance status scales for more than 1,600 patients who visited the clinic during a 3-year period. An analysis showed that for all three scales-the Eastern Cooperative Oncology Group Scale (ECOG), the Palliative Performance Scale (PPS), and the Karnofsky Performance Status Scale (KPS)-survival was approximately cut in half for each worsening performance level. The results are published in the Journal of Oncology Practice.

“Our study demonstrates that performance status alone is a good prognostic factor in outpatients with advanced cancer,” the authors wrote. “It also provides a simple tool that can be used in clinics to assist clinicians in prognostication for outpatients with advanced cancer, enabling more proactive advance care planning.”

Each scale showed an inverse relationship between performance status score and estimated survival. For example, the estimated median survival was about 25 days for patients scoring a 4 on the ECOG and 55 days for those scoring a 3. Thus, knowing the median survival for one level (eg, ECOG 2 = 104 days) makes it easy for clinicians to calculate it for other levels, which increase or decrease in 50% increments.

Although various other models are used to predict survival in advanced cancer patients, most require more complex calculations of multiple variables, the authors said. For example, the Palliative Prognostic Index uses the PPS along with oral intake, edema, dyspnea at rest, and delirium to predict 3- and 6-week survival.

“The number of variables involved, the necessity of blood work, and the complexity of partial score assignment make these models difficult to use in a fast-paced outpatient setting,” the authors noted. “Although the models in our study contain only performance status, their predictive abilities were comparable to those of more complex models.”

A simple prognostication tool is especially useful for clinicians, patients, and families faced with planning for the future and determining eligibility for health care resources during the last year of life, noted the authors.

“Our findings best apply to ambulatory patients with advanced cancer whose clinical prognosis is a year or less,” the authors wrote. “However, this is also the population for which prognostication is the most uncertain and of greatest importance, particularly in terms of end-of-life planning.”