Primary Spinal Tumor Mortality Score (PSTMS): a novel scoring system for predicting poor survival.

ABSTRACT

BACKGROUND CONTEXT Although the surgical and oncological therapies of primary spinal tumors (PSTs) have changed significantly over the last few decades, the prognosis of this rare disease is still poor. The decision-making process in the multidisciplinary management is handicapped by the lack of large-scale population-based prognostic studies. PURPOSE: The objective of the present study was to investigate preoperative factors associated with PST mortality and to develop a predictive scoring system of poor survival. STUDY DESIGN: This is a large-scale ambispective cohort study. PATIENT SAMPLE The study included 323 consecutive patients with PSTs, treated surgically over an 18-year period at a tertiary care spine referral center for a population of 10 million. OUTCOME MEASURE: Survival was the outcome measure. METHODS: Patients were randomly divided into a training cohort (n=273) and a validation cohort (n=50). In the training cohort, 12 preoperative factors were investigated using Cox proportional hazard models. Based on the mortality-related variables, a simple scoring system of mortality was created, and three groups of patients were identified. Kaplan-Meier and log-rank analyses were used to compare the survival in the three groups. The model performance was assessed by measuring the discriminative ability (c-index) of the model and by applying a pseudo-R(2) goodness-of-fit test (Nagelkerke R(2), RN(2)). Internal validation was performed using bootstrapping in the training cohort and assessing the discrimination and explained variation of the model in the validation cohort. RESULTS: Patient age, spinal region, tumor grade, spinal pain, motor deficit, and myelopathy/cauda equina syndrome were significantly associated with poor survival in the multivariate analysis (p<.001, RN(2)=0.799). Based on these variables, we developed the Primary Spinal Tumor Mortality Score (PSTMS), where an eight-point scale was divided into three categories (low, medium, and high mortality). The three PSTMS categories were significantly associated with the overall survival (p<.001, RN(2)=0.811, c=0.82). The model performance remained similarly high in the validation cohort (RN(2)=0.831, c=0.81). CONCLUSIONS: The present study identifies six predictive variables for mortality in PSTs. Using these six variables, an easy-to-use scoring system was developed that can be applied to the estimation of postoperative survival in all types of PST patients.