Age Is a Risk Factor for Postoperative Complications Following Excisional Laminectomy for Intradural Extramedullary Spinal Tumors

What Factors Predict the Development of Neurologic Deficits Following Resection of Intramedullary Spinal Cord Tumors: A Multi-Center Study

BACKGROUND

Intramedullary spinal cord tumors are challenging to resect, and their postoperative neurological outcomes are often difficult to predict, with few studies assessing this outcome.

METHODS

We reviewed the medical records of all patients surgically treated for Intramedullary spinal cord tumors at our multisite tertiary care institution (Mayo Clinic Arizona, Mayo Clinic Florida, Mayo Clinic Rochester) between June 2002 and May 2020. Variables that were significant in the univariate analyses were included in a multivariate logistic regression. "MissForest" operating on the Random Forest algorithm, was used for data imputation, and K-prototype was used for data clustering. Heatmaps were added to show correlations between postoperative neurological deficit and all other included variables. Shapley Additive exPlanations were implemented to understand each feature's importance.

RESULTS

Our query resulted in 315 patients, with 160 meeting the inclusion criteria. There were 53 patients with astrocytoma, 66 with ependymoma, and 41 with hemangioblastoma. The mean age (standard deviation) was 42.3 (17.5), and 48.1% of patients were women (n = 77/160). Multivariate analysis revealed that pathologic grade >3 (OR = 1.55; CI = [0.67, 3.58], P = 0.046 predicted a new neurological deficit. Random Forest algorithm (supervised machine learning) found age, use of neuromonitoring, histology of the tumor, performing a midline myelotomy, and tumor location to be the most important predictors of new postoperative neurological deficits.

CONCLUSIONS

Tumor grade/histology, age, use of neuromonitoring, and myelotomy type appeared to be most predictive of postoperative neurological deficits. These results can be used to better inform patients of perioperative risk.

Development and External Validation of the Spinal Tumor Surgery Risk Index

BACKGROUND

Patients undergoing surgical procedures for spinal tumors are vulnerable to major adverse events (AEs) and death in the postoperative period. Shared decision making and preoperative optimization of outcomes require accurate risk estimation.

OBJECTIVE

To develop and validate a risk index to predict short-term major AEs after spinal tumor surgery.

METHODS

Prospectively collected data from multiple medical centers affiliated with the American College of Surgeons National Surgical Quality Improvement Program from 2006 to 2020 were reviewed. Multiple logistic regression was used to assess sociodemographic, tumor-related, and surgery-related factors in the derivation cohort. The spinal tumor surgery risk index (STSRI) was built based on the resulting scores. The STSRI was internally validated using a subgroup of patients from the American College of Surgeons National Surgical Quality Improvement Program database and externally validated using a cohort from a single tertiary center.

RESULTS

In total, 14 982 operations were reviewed and 4556 (16.5%) major AEs occurred within 30 days after surgery, including 209 (4.5%) deaths. 22 factors were independently associated with major AEs or death and were included in the STSRI. Using the internal and external validation cohorts, the STSRI produced an area under the curve of 0.86 and 0.82, sensitivity of 80.1% and 79.7%, and specificity of 74.3% and 73.7%, respectively. The STSRI, which is freely available, outperformed the modified frailty indices, the American Society of Anesthesiologists classification, and the American College of Surgeons risk calculator.

CONCLUSION

In patients undergoing surgery for spinal tumors, the STSRI showed the highest predictive accuracy for major postoperative AEs and death compared with other current risk predictors.

Laminectomy Versus Laminectomy with Fusion for Intradural Extramedullary Tumors: A Systematic Review and Meta-Analysis

OBJECTIVE

The primary objective of our systematic review and meta-analysis was to systematically compare the reported outcomes between laminectomy and laminectomy with fixation/fusion (LF) for the treatment of intradural extramedullary tumors (IDEMTs). Our secondary objective was to compare the outcomes between different laminectomy exposure techniques.

METHODS

PubMed and Embase were queried for literature on laminectomy and LF for IDEMTs. Reports of transforaminal approaches, interlaminar approaches, corpectomy, pediatrics patients, intramedullary tumors, technical studies, animal or cadaver studies, and literature reviews were excluded. The outcome measures recorded were pain, neurologic function, functional independence, cerebrospinal fluid leak, and wound infection. Where possible, the laminectomy technique (partial laminectomy [PL] vs. total laminectomy [TL]) was specified. Stata, version 17, was used for the fixed effects inverse variance meta-analysis.

RESULTS

Of 1849 reports assessed, 17 were included. The meta-analysis revealed that laminectomy (PL or TL) resulted in higher rates of postoperative sagittal instability compared with LF (odds ratio, 1.81; P < 0.001). No differences in any other postoperative outcome were observed between laminectomy and LF (P = 0.44). The systematic review also revealed no differences in postoperative pain, neurologic function, or functional independence or disability between PL and TL. Some evidence suggested that TL might result in greater rates of sagittal instability compared with PL.

CONCLUSIONS

No differences between LF, PL, or TL in pain, neurologic deficit, functional independence, cerebrospinal fluid leak, or wound infection were reported. Laminectomy had greater odds of sagittal instability compared with LF. Patients with preoperative sagittal instability requiring extensive removal of the posterior spinal column to achieve adequate resection of large tumors might benefit from LF.

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma

Background

The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA.

Methods

Patients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability.

Results

A total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent.

Conclusions

Nomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.

Dural Tear Does not Increase the Rate of Venous Thromboembolic Disease in Patients Undergoing Elective Lumbar Decompression with Instrumented Fusion

OBJECTIVE

Evaluate if dural tears (DTs) are an indirect risk factor for venous thromboembolic disease through increased recumbency in patients undergoing elective lumbar decompression and instrumented fusion.

METHODS

This was a retrospective cohort study of consecutive patients undergoing elective lumbar decompression and instrumented fusion at a single institution between 2016 and 2019. Patients were divided into cohorts: those who sustained a dural tear and those who did not. The cohorts were compared using Student's t-test or Wilcoxon Rank Sum for continuous variables and Fisher exact or chi-squared test for nominal variables.

RESULTS

Six-hundred and eleven patients met inclusion criteria, among which 144 patients (23.6%) sustained a DT. The DT cohort tended to be older (63.6 vs. 60.6 years, P = 0.0052) and have more comorbidities (Charlson Comorbidity Index 2.75 vs. 2.35, P = 0.0056). There was no significant difference in the rate of symptomatic deep vein thrombosis (2.1% vs. 2.6%, P = 1.0) or pulmonary embolus (1.4% vs. 1.50%, P = 1.0). Intraoperatively, DT was associated with increased blood loss (754 mL vs. 512 mL, P < 0.0001), operative time (224 vs. 195 minutes, P < 0.0001), and rate of transfusion (19.4% vs. 9.4%, P = 0.0018). Postoperatively, DT was associated with increased time to ambulation (2.6 vs. 1.4 days, P < 0.0001), length of stay (5.8 vs. 4.0 days, P < 0.0001), and rate of discharge to rehab (38.9 vs. 25.3%, P = 0.0021).

CONCLUSIONS

While DTs during elective lumbar decompression and instrumentation led to later ambulation and longer hospital stays, the increased recumbency did not significantly increase the rate of symptomatic venous thromboembolic disease.

Tumor Occupancy Ratio-An Imaging Characteristic Prognosticating the Surgical Outcome of Benign Intradural Extramedullary Spinal Cord Tumors

BACKGROUND

Intradural extramedullary (IDEM) spinal cord tumors are two thirds of all spinal tumors. We have prospectively analyzed the importance of the tumor occupancy ratio as a factor for predicting the course of the disease and in prognosticating the surgical outcome in patients with IDEM tumors.

METHODS

We prospectively analyzed 44 consecutive cases of IDEM tumors, diagnosed as cervical, thoracic, and lumbar IDEM tumors (excluding conus/cauda equina lesion) by magnetic resonance imaging (MRI), that were operated on at our institution between 2014 and 2016. We measured the tumor occupancy ratio and noted the sagittal and axial location of the tumor in the preoperative MRI and performed the laminectomy and unilateral medial facetectomy. A primary outcome has been noted according to the gait disability score in the preoperative period and in the follow-up period of 1 year. In the statistical analysis, categorical variables were compared using a chi-square test, and an analysis of variance and student t tests were used for the continuous variables. P < .05 was considered statistically significant.

RESULTS

The tumor occupancy ratio showed a significant association to the preoperative gait disability score (P < .001) and also predicted that the surgical outcome is much better in patients with tumors with a low tumor occupancy ratio than in patients with tumors with a high occupancy ratio (P < .001). No difference in the tumor occupancy ratio was noted in 2 different pathological tumors, and there was also no difference in the tumor occupancy ratio at different sagittal and axial locations of the tumor.

CONCLUSION

Tumor occupancy ratio has shown a significant impact on the preoperative course and also predicts the surgical outcome in patients with IDEM tumors. Hence, it is an important imaging characteristic to prognosticate the outcome in IDEM tumors and should be noted in each case.

Predicting Postoperative Mortality After Metastatic Intraspinal Neoplasm Excision: Development of a Machine-Learning Approach

OBJECTIVE

Mortality following surgical resection of spinal tumors is a devastating outcome. Naïve Bayes machine learning algorithms may be leveraged in surgical planning to predict mortality. In this investigation, we use a Naïve Bayes classification algorithm to predict mortality following spinal tumor excision within 30 days of surgery.

METHODS

Patients who underwent laminectomies between 2006 and 2018 for excisions of intraspinal neoplasms were selected from the National Surgical Quality Initiative Program. Naïve Bayes classifier analysis was conducted in Python. The area under the receiver operating curve (AUC) was calculated to evaluate the classifier's ability to predict mortality within 30 days of surgery. Multivariable logistic regression analysis was performed in R to identify risk factors for 30-day postoperative mortality.

RESULTS

In total, 2094 spine tumor surgery patients were included in the study. The 30-day mortality rate was 5.16%. The classifier yielded an AUC of 0.898, which exceeds the predictive capacity of the National Surgical Quality Initiative Program mortality probability calculator's AUC of 0.722 (P < 0.0001). The multivariable regression indicated that smoking history, chronic obstructive pulmonary disease, disseminated cancer, bleeding disorder history, dyspnea, and low albumin levels were strongly associated with 30-day mortality.

CONCLUSIONS

The Naïve Bayes classifier may be used to predict 30-day mortality for patients undergoing spine tumor excisions, with an increasing degree of accuracy as the model better performs by learning continuously from the input patient data. Patient outcomes can be improved by identifying high-risk populations early using the algorithm and applying that data to inform preoperative decision making, as well as patient selection and education.