A predictive model for survival in metastatic cancer patients attending an outpatient palliative radiotherapy clinic

Abstract

Purpose: To develop a predictive model for survival from the time of presentation in an outpatient palliative radiotherapy clinic.

Methods and materials: Sixteen factors were analyzed prospectively in 395 patients seen in a dedicated palliative radiotherapy clinic in a large tertiary cancer center using Cox's proportional hazards regression model.

Results: Six prognostic factors had a statistically significant impact on survival, as follows: primary cancer site, site of metastases, Karnofsky performance score (KPS), and fatigue, appetite, and shortness of breath scores from the modified Edmonton Symptom Assessment Scale. Risk group stratification was performed (1) by assigning weights to the prognostic factors based on their levels of significance, and (2) by the number of risk factors present. The weighting method provided a Survival Prediction Score (SPS), ranging from 0 to 32. The survival probability at 3, 6, and 12 months was 83%, 70%, and 51%, respectively, for patients with SPS <or=13 (n = 133); 67%, 41%, and 20% for patients with SPS 14-19 (n = 129); and 36%, 18%, and 4% for patients with SPS >or=20 (n = 133) (p < 0.0001). Corresponding survival probabilities based on number of risk factors were as follows: 85%, 72%, and 52% (<or=3 risk factors)(n = 98); 68%, 47%, and 24% (4 risk factors)(n = 117); and 46%, 24%, and 11% (>or=5 factors)(n = 180)(p < 0.0001).

Conclusion: Clinical prognostic factors can be used to predict prognosis among patients attending a palliative radiotherapy clinic. If validated in an independent series of patients, the model can be used to guide clinical decisions, plan supportive services, and allocate resource use.