Background: Patients with spinal epidural metastases (SEM) often experience a reduction in ambulatory status and, thus, the quality of life. Predicting which patients will benefit from a surgical intervention remains a challenge. Life expectancy is an essential factor to be considered in surgical decision-making, although not the only one. Prediction models can add value in surgical decision-making. The goal of this study was to develop and internally validate a novel model (Limburg spinal metastases score [LSMS]) and compare the predictive value with 2 commonly used models: modified Bauer score and Oswestry Spinal Risk Index (OSRI).
Methods: We retrospectively analyzed 144 consecutive patients who underwent surgical decompression for SEM in our centers between November 2006 and December 2020. Clinical and surgical parameters were evaluated. The novel prediction model was based on multivariate analysis and was internally validated. External validation of the 2 most commonly used prediction models was performed.
Results: The median survival was 17 months, 55.7% of the immobile patients regained ambulation postoperatively. In 50 patients (34.7%), at least 1 complication occurred within 30 days after surgery. The LSMS consists of 4 parameters: primary tumor type, Karnofsky performance score, presence of visceral metastases, and presence of multiple spinal metastases. Bootstrap internal validation of the model developed on this cohort yielded an optimism-corrected c-statistic of 0.75 (95% CI: 0.71-0.80). The c-statistic of the OSRI score and the Bauer score was 0.69 (95% CI: 0.64-0.74) and 0.67 (95% CI: 0.62-0.72), respectively.
Conclusion: The LSMS consists of 4 parameters to assist surgical decision-making for patients with SEM. The score is easy to use and appears more accurate in our population in comparison with previous existing models.
Clinical relevance: A novel prediction model was developed to aid in surgical decision-making for patients with spinal epidural metastases.
Keywords: epidural spinal metastases; prediction models; survival.
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