Development of a model with which to predict the life expectancy of patients with spinal epidural metastasis

Cancer. 2007 Nov 1;110(9):2042-9. doi: 10.1002/cncr.23002.


Background: The surgical treatment of spinal epidural metastasis is evolving. To be a surgical candidate, a patient should have a life expectancy of at least 3 months. Estimation of survival by experienced specialists has proven to be unreliable.

Methods: The Cox proportional hazards model was used to make a prediction model. To validate the model, Efron optimism correction by bootstrapping was performed. Retrospective data of patients treated for a spinal metastasis were used. Possible predictive factors were defined based on clinical experience and the literature. Statistical methods and clinical knowledge were also used to reveal an optimal set of predictors of survival. Data from patients treated at the Department of Radiation Oncology for spinal metastasis between 1998 and 2005 were evaluated.

Results: The case notes of 219 patients form the base of this study. In the final model, only 5 variables were required to predict the survival of a patient with spinal metastasis: sex, location of the primary lesion, intentional curative treatment of the primary tumor, cervical location of the spinal metastasis, and Karnofsky performance score. Examples with different predictors are given. The R(2) (N) index of Nagelkerke was 0.36 (95% confidence interval [95% CI], 0.28-0.48) and the c-index 0.72 (95% CI, 0.68-0.77).

Conclusions: A reliable and simple model with which to predict the survival of a patient with spinal epidural metastasis is presented. Without the need for extensive investigations, survival can be predicted and only 5 easily obtainable parameters are required.

MeSH terms

  • Female
  • Humans
  • Life Expectancy*
  • Male
  • Models, Statistical*
  • Neoplasms / mortality
  • Neoplasms / pathology
  • Predictive Value of Tests
  • Prognosis
  • Proportional Hazards Models
  • Sex Factors
  • Spinal Neoplasms / mortality*
  • Spinal Neoplasms / secondary*