A novel scoring system incorporating sarcopenia to predict postoperative survival in spinal metastasis

• Published in: The spine journal 2023-09, Vol.23 (9), p.1270-1275
• Main Authors: McCabe, Fergus J., McCabe, John P., Murray, Odhrán
• Format: Article
• Language: English
• Subjects: Cancer; Metastasis; Prediction; Prognosis; Scoring system; Spine
• ISSN: 1529-9430; 1878-1632; 1878-1632
• DOI: 10.1016/j.spinee.2023.04.010

As the prevalence of spinal metastasis rises, methods to predict survival will become increasingly important for clinical decision-making. Sarcopenia may be used to predict survival in these patients. The purpose of this study to develop a prediction model incorporating sarcopenia for postoperative survival in patients with spinal metastasis. Retrospective cohort study This included 200 patients who underwent operative intervention for spinal metastasis in our institution, a tertiary, academic spine center. The primary outcome measure was 1-year postoperative survival. The secondary outcome measures were 3 month and 6 month postoperative survival. Clinicopathological and survivorship data was collated. Sarcopenia was defined using the L3 Psoas/Vertebral Body Ratio on cross-sectional CT. Independent predictors of postoperative survival were assessed by multiple logistic regression. Overall 1-year postoperative survival was 50%. L3/Psoas ratio ≥1.5 (OR 6.2), albumin ≥35g/l (OR 3.0) and primary tumor type were found to be independent predictors of 3 month, 6 month and 1 year postoperative survival on multivariable analysis. Age at surgery, ambulatory status and mode of presentation were not independent predictors of survival. Variables were used to generate a new scoring system, ProgMets, to predict postoperative survival. The ProgMets system had greater correlation and higher area under the curve (AUC, 0.80) for 1-year survival than other scoring systems. This is the first model to incorporate sarcopenia to predict survival in spinal metastasis patients and has good prediction of survival compared to previous models. This tool may be increasingly useful for informed decision making for patients and surgeons.