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Open access

J. Manuel Sarmiento, Mitchell S. Fourman, Francis Lovecchio, Keith W. Lyons, and James C. Farmer

BACKGROUND

Synovial facet cysts can sometimes develop in patients with lumbar spinal stenosis after decompressive laminectomy. The etiology of spinal lumbar synovial cysts is still unclear, but their formation is associated with underlying spinal instability, facet joint arthropathy, and degenerative spondylolisthesis.

OBSERVATIONS

A 61-year-old-male patient presented with neurogenic claudication due to lumbar spinal stenosis. Radiographic studies showed grade I spondylolisthesis and radiological predictors of delayed spinal instability. He underwent lumbar decompression and shortly thereafter developed spinal instability and recurrent symptoms, with formation of a new spinal lumbar synovial facet cyst. He required revisional decompression, cyst excision, and posterolateral spinal fusion for definitive treatment.

LESSONS

The literature reports postoperative spinal instability in up to one-third of patients with lumbar spinal stenosis and stable degenerative spondylolisthesis who undergo decompressive laminectomy. Close radiographic monitoring and early advanced imaging may be prudent in this patient population if they develop new postoperative neurological symptoms and show radiographic predictors of instability on preoperative imaging. Posterolateral spinal fusion with instrumentation should be considered in addition to lumbar decompression in this select group of patients who demonstrate radiographic predictors of delayed spinal instability if they are medically capable of tolerating a spinal fusion procedure.

Free access

Elie Massaad, Natalie Williams, Muhamed Hadzipasic, Shalin S. Patel, Mitchell S. Fourman, Ali Kiapour, Andrew J. Schoenfeld, Ganesh M. Shankar, and John H. Shin

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.

Open access

Elie Massaad, Christopher P. Bridge, Ali Kiapour, Mitchell S. Fourman, Julia B. Duvall, Ian D. Connolly, Muhamed Hadzipasic, Ganesh M. Shankar, Katherine P. Andriole, Michael Rosenthal, Andrew J. Schoenfeld, Mark H. Bilsky, and John H. Shin

OBJECTIVE

Cancer patients with spinal metastases may undergo surgery without clear assessments of prognosis, thereby impacting the optimal palliative strategy. Because the morbidity of surgery may adversely impact recovery and initiation of adjuvant therapies, evaluation of risk factors associated with mortality risk and complications is critical. Evaluation of body composition of cancer patients as a surrogate for frailty is an emerging area of study for improving preoperative risk stratification.

METHODS

To examine the associations of muscle characteristics and adiposity with postoperative complications, length of stay, and mortality in patients with spinal metastases, the authors designed an observational study of 484 cancer patients who received surgical treatment for spinal metastases between 2010 and 2019. Sarcopenia, muscle radiodensity, visceral adiposity, and subcutaneous adiposity were assessed on routinely available 3-month preoperative CT images by using a validated deep learning methodology. The authors used k-means clustering analysis to identify patients with similar body composition characteristics. Regression models were used to examine the associations of sarcopenia, frailty, and clusters with the outcomes of interest.

RESULTS

Of 484 patients enrolled, 303 had evaluable CT data on muscle and adiposity (mean age 62.00 ± 11.91 years; 57.8% male). The authors identified 2 clusters with significantly different body composition characteristics and mortality risks after spine metastases surgery. Patients in cluster 2 (high-risk cluster) had lower muscle mass index (mean ± SD 41.16 ± 7.99 vs 50.13 ± 10.45 cm2/m2), lower subcutaneous fat area (147.62 ± 57.80 vs 289.83 ± 109.31 cm2), lower visceral fat area (82.28 ± 48.96 vs 239.26 ± 98.40 cm2), higher muscle radiodensity (35.67 ± 9.94 vs 31.13 ± 9.07 Hounsfield units [HU]), and significantly higher risk of 1-year mortality (adjusted HR 1.45, 95% CI 1.05–2.01, p = 0.02) than individuals in cluster 1 (low-risk cluster). Decreased muscle mass, muscle radiodensity, and adiposity were not associated with a higher rate of complications after surgery. Prolonged length of stay (> 7 days) was associated with low muscle radiodensity (mean 30.87 vs 35.23 HU, 95% CI 1.98–6.73, p < 0.001).

CONCLUSIONS

Body composition analysis shows promise for better risk stratification of patients with spinal metastases under consideration for surgery. Those with lower muscle mass and subcutaneous and visceral adiposity are at greater risk for inferior outcomes.