Machine learning–based detection of sarcopenic obesity and association with adverse outcomes in patients undergoing surgical treatment for spinal metastases

Author:

Khalid Syed I.12,Massaad Elie1,Kiapour Ali1,Bridge Christopher P.3,Rigney Grant1,Burrows Akeive1,Shim Jane1,De la Garza Ramos Rafael4,Tobert Daniel G.5,Schoenfeld Andrew J.6,Williamson Theresa1,Shankar Ganesh M.1,Shin John H.1

Affiliation:

1. Departments of Neurosurgery and

2. Department of Neurosurgery, University of Illinois at Chicago, Chicago, Illinois;

3. Massachusetts General Hospital and Brigham and Women’s Hospital Center for Clinical Data Science, Boston, Massachusetts;

4. Department of Neurosurgery, The Warren Alpert Medical School of Brown University, Providence, Rhode Island; and

5. Orthopedic Surgery, Massachusetts General Hospital, Boston, Massachusetts;

6. Department of Orthopedic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts

Abstract

OBJECTIVE The distributions and proportions of lean and fat tissues may help better assess the prognosis and outcomes of patients with spinal metastases. Specifically, in obese patients, sarcopenia may be easily overlooked as a poor prognostic indicator. The role of this body phenotype, sarcopenic obesity (SO), has not been adequately studied among patients undergoing surgical treatment for spinal metastases. To this end, here the authors investigated the role of SO as a potential prognostic factor in patients undergoing surgical treatment for spinal metastases. METHODS The authors identified patients who underwent surgical treatment for spinal metastases between 2010 and 2020. A validated deep learning approach evaluated sarcopenia and adiposity on routine preoperative CT images. Based on composition analyses, patients were classified with SO or nonsarcopenic obesity. After nearest-neighbor propensity matching that accounted for confounders, the authors compared the rates and odds of postoperative complications, length of stay, 30-day readmission, and all-cause mortality at 90 days and 1 year between the SO and nonsarcopenic obesity groups. RESULTS A total of 62 patients with obesity underwent surgical treatment for spinal metastases during the study period. Of these, 37 patients had nonsarcopenic obesity and 25 had SO. After propensity matching, 50 records were evaluated that were equally composed of patients with nonsarcopenic obesity and SO (25 patients each). Patients with SO were noted to have increased odds of nonhome discharge (OR 6.0, 95% CI 1.69–21.26), 30-day readmission (OR 3.27, 95% CI 1.01–10.62), and 90-day (OR 4.85, 95% CI 1.29–18.26) and 1-year (OR 3.78, 95% CI 1.17–12.19) mortality, as well as increased time to mortality after surgery (12.60 ± 19.84 months vs 37.16 ± 35.19 months, p = 0.002; standardized mean difference 0.86). No significant differences were noted in terms of length of stay or postoperative complications when comparing the two groups (p > 0.05). CONCLUSIONS The SO phenotype was associated with increased odds of nonhome discharge, readmission, and postoperative mortality. This study suggests that SO may be an important prognostic factor to consider when developing care plans for patients with spinal metastases.

Publisher

Journal of Neurosurgery Publishing Group (JNSPG)

Subject

General Medicine

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