Originally published as JCO Early Release 10.1200/JCO.2008.17.1363 on November 17 2008

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Predictive Model for Survival in Patients With Advanced Cancer

Edward Chow, Mohamed Abdolell, Tony Panzarella, Kristin Harris, Andrea Bezjak, Padraig Warde, Ian Tannock

From the Odette Cancer Centre, Sunnybrook Health Sciences Centre; and Princess Margaret Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada

Corresponding author: Edward Chow, MBBS, PhD, FRCPC, Department of Radiation Oncology, Odette Cancer Centre, Sunnybrook Health Sciences Centre, 2075 Bayview Ave, Toronto, Ontario, Canada M4N 3M5; e-mail: edward.chow{at}sunnybrook.ca

Purpose To derive and validate a simple predictive model for survival of patients with metastatic cancer attending a palliative radiotherapy clinic.

Patients and Methods We described previously a model predicting survival of patients referred for palliative radiotherapy using six prognostic factors: primary cancer site, site of metastases, Karnofsky performance score (KPS), and the fatigue, appetite, and shortness of breath subscales from the Edmonton Symptom Assessment Scale. Here we simplified the model to include only three factors: primary cancer site, site of metastases, and KPS. Each factor was assigned a value proportional to its prognostic weight, and the weighted scores for each patient were summed to obtain a survival prediction score (SPS). Patients were also grouped according to their number of risk factors (NRF): nonbreast cancer, metastases other than bone, and KPS 60. The three- and six- variable models were evaluated for their ability to predict survival in patients referred during a different time period and of those referred to a different cancer center.

Results A training set of 395 patients, a temporal validation set of 445 patients, and an external validation set of 467 patients were used. The ability of the three- and six-variable models to separate patients into three prognostic groups and to predict their survival was similar using both SPS and NRF methods in the training, temporal, and external validation data sets. There was no statistically significant difference in the performance of the models.

Conclusion The three-variable NRF model is preferred because of its relative simplicity.

published online ahead of print at www.jco.org on November 17, 2008

Supported by the Michael and Karyn Goldstein Cancer Research Fund, Department of Radiation Oncology, University of Toronto, and Odette Cancer Center Radiation Program Fund.

Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.

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