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J Oncol Pract. 2014 Sep;10(5):e335-41. doi: 10.1200/JOP.2014.001457. Epub 2014 Aug 12.

Simple prognostic model for patients with advanced cancer based on performance status.

Author information

1
University of Toronto; and Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
2
University of Toronto; and Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada camilla.zimmermann@uhn.ca.

Abstract

PURPOSE:

Providing survival estimates is important for decision making in oncology care. The purpose of this study was to provide survival estimates for outpatients with advanced cancer, using the Eastern Cooperative Oncology Group (ECOG), Palliative Performance Scale (PPS), and Karnofsky Performance Status (KPS) scales, and to compare their ability to predict survival.

METHODS:

ECOG, PPS, and KPS were completed by physicians for each new patient attending the Princess Margaret Cancer Centre outpatient Oncology Palliative Care Clinic (OPCC) from April 2007 to February 2010. Survival analysis was performed using the Kaplan-Meier method. The log-rank test for trend was employed to test for differences in survival curves for each level of performance status (PS), and the concordance index (C-statistic) was used to test the predictive discriminatory ability of each PS measure.

RESULTS:

Measures were completed for 1,655 patients. PS delineated survival well for all three scales according to the log-rank test for trend (P < .001). Survival was approximately halved for each worsening performance level. Median survival times, in days, for each ECOG level were: EGOG 0, 293; ECOG 1, 197; ECOG 2, 104; ECOG 3, 55; and ECOG 4, 25.5. Median survival times, in days, for PPS (and KPS) were: PPS/KPS 80-100, 221 (215); PPS/KPS 60 to 70, 115 (119); PPS/KPS 40 to 50, 51 (49); PPS/KPS 10 to 30, 22 (29). The C-statistic was similar for all three scales and ranged from 0.63 to 0.64.

CONCLUSION:

We present a simple tool that uses PS alone to prognosticate in advanced cancer, and has similar discriminatory ability to more complex models.

PMID:
25118208
DOI:
10.1200/JOP.2014.001457
[Indexed for MEDLINE]

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