Outcome of spine surgery in the context of spinal metastatic disease: The National Surgical Quality Improvement Program - Salim M. Yakdan; Maya Herrera; Nour Wehbe; Monifa Al Akoum; Muhammad Irfan Kaleem; Miguel A. Ruiz-Cardozo; Karan Joseph; Nada Assaf; Hani Dimassi (Journal of Craniovertebral Junction and Spine)
Background: Spinal metastases are the third most common site of metastasis, with around 10% of cancer patients experiencing symptomatic spine involvement. Despite poor overall survival, recent advances in targeted therapies and minimally invasive surgical techniques have made surgery a more viable option for managing metastatic spine disease. Objective: Our study aims to identify perioperative risk factors associated with poor outcomes following spine surgery in the setting of spinal cord metastasis. Methods: We used the National Surgical Quality Improvement Program data. Patients with metastatic spine disease undergoing spine surgery were identified. Our primary outcome measure was 30-day morbidity and mortality after surgery. Our secondary outcome was prolonged hospital stay. Logistic regression model for each outcome based on individual characteristics was developed. Results: A total of 2109 patients were included. The 30-day morbidity and mortality rates were 19.1%. Significant predictors included smoking, more than 10% body weight loss, surgical urgency, dependent status, and preoperative albumin levels. In addition, 28.6% of patients experienced a prolonged hospital stay, with significant predictors including chemotherapy, surgical urgency, dependent status, preoperative hematocrit, neurological deficits, preoperative albumin levels, and surgical complexity. Conclusion: Our study provides valuable insights into the risk factors associated with mortality, morbidity, and prolonged hospital stay in patients with spinal cord metastasis undergoing spine surgery. These identified factors can be instrumental in assisting clinicians with risk stratification, preoperative optimization, and postoperative care planning, ultimately improving patient outcomes. Further research and validation of these predictive models are warranted to ensure their generalizability and applicability in clinical practice.