Performance Status Alone Found Effective Tool for Prognostication in Advanced Cancer


Physician looking at brain scans

Predictive Ability

A simple prognostic model based on performance status has predictive ability that is similar to more complex models in patients with advanced cancer, and can thus be a useful prognostic tool for busy clinicians in outpatient settings, according to a report published in the Journal of Oncology Practice, a journal of the American Society of Clinical Oncology.

“In our study, we found that performance status alone ... was effective in delineating the survival of outpatients with advanced cancer, as seen by the Kaplan-Meier survival curves and the highly significant log-rank test for trend,” write the authors, led by Raymond Jang, MD, MSc, a medical oncologist at the Princess Margaret Cancer Centre within the University Health Network, a research hospital affiliated with the University of Toronto.

“As for any model, this estimate requires adjustment by the clinician, based on clinical impression and experience,” add the authors. “However, unlike for other models, there is no need to collect and enter other clinical or laboratory information into an algorithm.”

A Simpler Model

Various models have been developed to assist clinicians in assessing survival, but because they incorporate multiple variables related to prognosis, they are often complex, requiring input of laboratory values or conversion calculations that are “time consuming and impractical for rapid outpatient assessment,” note the authors. In addition, these more complicated models are often designed to determine shorter survival probability for use in patients closer to death.

“Our findings best apply to ambulatory patients with advanced cancer whose clinical prognosis is a year or less,” the authors point out. “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.”

Performance status, or assessment of the patient’s level of function, is already routinely employed in oncology outpatient settings, note the authors. The most commonly used model is the Eastern Cooperative Oncology Group (ECOG) scale, followed in popularity by the Karnofsky Performance Status (KPS) scale. In palliative care settings, the Palliative Performance Scale (PPS), which is based on the KPS, tends to be the most utilized tool.

“Although these three measures are known to be correlated with survival, there has been no study (to our knowledge) assessing and comparing their predictive validity in relation to actual survival time,” observe the authors.

Investigators compared actual survival with survival estimates based on three performance status scales completed by physicians for each of their adult patients with cancer (n = 1655; median age, 65 years; female, 51%) newly attending an outpatient oncology palliative care clinic in Toronto between 2007 and 2010. At the time of analysis, 91% of patients had died. Overall, median survival for all patients was 135 days (95% confidence interval [CI], 123 to 144 days).

Key Findings

  • C-statistic, which measures predictive ability, was 0.64 for the ECOG model—indicating modest predictive performance—and 0.63 for the PPS and KPS models.
  • Similarly, the C-statistic for the more complex PiPS-A and PiPS-B (Prognosis in Palliative Care Study) models, which use 10 to 12 variables and require blood work, was between 0.67 and 0.69.

“Performance scores delineated survival well using any of the performance measures,” note the authors, and “the ECOG, PPS, and KPS all have similar predictive ability.” Study physicians completed the ECOG, KPS and PPS for each of their respective patients at the end of the initial consultation. Separate survival analyses were performed for the scales, using the Kaplan-Meier method.

The study also provided survival estimates for each performance level. For the ECOG, for example, the estimated survival for each improved performance level was approximately twice that of the performance level below it. A similar pattern was also found for KPS and PPS. For the ECOG measure (scored from 0 [normal activity] to 5 [death]), median survival was:

  • 293 days (95% CI, 242 to 403) days for patients with a score of ECOG 0
  • 197 days (95% CI, 183 to 219 days) for ECOG 1
  • 104 days (95% CI, 90 to 118 days) for ECOG 2
  • 55 days (95% CI, 46 to 66 days) for ECOG 3
  • 25.5 days (95% CI, 17 to 51 days) for ECOG 4

“A clinician need only remember that an ECOG of 4 corresponds to a median survival of approximately 25 days in order to easily calculate the median survivals for the other ECOG levels,” suggest the authors. “Survival was approximately halved for each worsening performance level.”

Patients with advanced cancer and their family members often ask questions related to prognosis, and although clinical prognostication can be difficult, “open, empathic discussions about this topic may improve satisfaction with care,” note the authors, who hope use of this simple tool can enable more proactive advance care planning.

“Accurate prognostic information can help physicians decide whether to initiate or continue anticancer therapy, facilitate transitions to hospice care, and enable appropriate advance care planning and end-of-life decision making.”

Source: “Simple Prognostic Model for Patients with Advanced Cancer Based on Performance Status,” Journal of Oncology Practice; September 2014; 10(5):e335-341. Jang RW, Caraiscos VB, Swami N, Banerjee S, Mak E, Kava E, Rodin G, Bryson J, Ridley JZ, Le LW, Zimmermann C; University of Toronto and Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.

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