Assessment of Spinal Metastases Surgery Risk Stratification Tools in Breast Cancer by Molecular Subtype.

BACKGROUND: Breast cancer molecular features and modern therapies are not included in spine metastasis prediction algorithms. OBJECTIVE: To examine molecular differences and the impact of postoperative systemic therapy to improve prognosis prediction for spinal metastases surgery and aid surgical decision making. METHODS: This is a retrospective multi-institutional study of patients who underwent spine surgery for symptomatic breast cancer spine metastases from 2008 to 2021 at the Massachusetts General Hospital and Brigham and Women's Hospital. We studied overall survival, stratified by breast cancer molecular subtype, and calculated hazard ratios (HRs) adjusting for demographics, tumor characteristics, treatments, and laboratory values. We tested the performance of established models (Tokuhashi, Bauer, Skeletal Oncology Research Group, New England Spinal Metastases Score) to predict and compare all-cause. RESULTS: A total of 98 patients surgically treated for breast cancer spine metastases were identified (100% female sex; median age, 56 years [IQR, 36-84 years]). The 1-year probabilities of survival for hormone receptor positive, hormone receptor positive/human epidermal growth factor receptor 2+, human epidermal growth factor receptor 2+, and triple-negative breast cancer were 63% (45 of 71), 83% (10 of 12), 0% (0 of 3), and 12% (1 of 8), respectively ( P < .001). Patients with triple-negative breast cancer had a higher proportion of visceral metastases, brain metastases, and poor physical activity at baseline. Postoperative chemotherapy and endocrine therapy were associated with prolonged survival. The Skeletal Oncology Research Group prognostic model had the highest discrimination (area under the receiver operating characteristic, 0.77 [95% CI, 0.73-0.81]). The performance of all prognostic scores improved when preoperative molecular data and postoperative systemic treatment plans was considered. CONCLUSION: Spine metastases risk tools were able to predict prognosis at a significantly higher degree after accounting for molecular features which guide treatment response.

Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions.

OBJECTIVE: Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes. METHODS: Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation. RESULTS: Of 479 patients (median age 64 years [IQR 55-71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50-0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54-0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56-0.68 for random forest vs AUROC 0.56, 95% CI 0.50-0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43-0.64) and the highest negative predictive value (0.77, 95% CI 0.72-0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications. CONCLUSIONS: This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort.

Assessment of the efficacy of teriparatide treatment for osteoporosis on lumbar fusion surgery outcomes: a systematic review and meta-analysis.

Treatment of osteoporosis with medications like teriparatide, a parathyroid hormone, is known to improve bone density and reduce the risk of osteoporotic vertebral fractures. Anecdotal and limited surgical series have described the utility of this treatment for osteoporotic patients prior to spinal fusion surgery, but there is variability in adoption of this strategy as well as consensus regarding optimal treatment duration before and after surgery. In this study, the clinical results of the use of teriparatide for this application are reviewed and critically examined. We conducted a systematic review of electronic databases using different MeSH terms from 1980 to 2020. Pooled and subgroup analyses were performed using fixed and random effect models based upon the heterogeneity (I2). The results were reported as either mean difference (MD) or odds ratio (OR) with 95% confidence interval (CI). A total of 771 patients from 12 studies were identified. Three hundred seventy-seven patients (90.8% females) were treated with teriparatide. Lumbar spinal fusion rates were significantly higher among patients who received teriparatide compared to the non-teriparatide group (OR 2.15, 95%CI 1.56-2.97, p < 0.00001). Subgroup analysis revealed that patients receiving teriparatide demonstrated 2.12-fold and 2.23-fold higher likelihood of fusion compared to those in the bisphosphonate (OR 2.12, 95%CI 1.45-3.11, p = 0.0001) and placebo (OR 2.23, 95%CI 1.22-4.08, p = 0.009) cohorts, respectively. The treatment effect of teriparatide was associated with significantly reduced subsequent vertebral fractures (OR 0.16, 95%CI 0.06-0.41, p = 0.0002), sagittal malalignment (MD - 3.85, 95%CI: -6.49 to - 1.21, p = 0.004), limb visual analogue score (VAS) (MD - 0.36, 95%CI - 0.64 to - 0.09, p = 0.008), and spinal VAS (MD - 0.24, 95%CI - 0.44 to - 0.04, p = 0.02) compared to the non-teriparatide group. Patients using teriparatide had 30% less likelihood of screw loosening at last follow-up compared to the non-teriparatide group; however, this was not statistically significant (OR 0.70, 95%CI 0.43-1.14, p = 0.15). There did not exist any statistically significant difference between the two comparative groups in terms of pseudoarthrosis (OR 0.54, 95%CI 0.24-1.21, p = 0.13), cage subsidence (OR 1.30, 95%CI 0.38-4.52, p = 0.68), and bone mineral density (MD 0.04, 95%CI - 0.19-0.29, p = 0.74) at last follow-up examination. This meta-analysis corroborates the effectiveness of teriparatide resulting in higher fusion rates. Further study is required to determine the optimal duration of treatment and timing of surgery.