Browse

You are looking at 101 - 110 of 7,837 items for

  • User-accessible content x
Clear All
Free access

Anthony L. Asher, Clinton J. Devin, Robert E. Harbaugh and Mohamad Bydon

Free access

Lateral lumbar interbody fusion in the elderly: a 10-year experience

Presented at the 2018 AANS/CNS Joint Section on Disorders of the Spine and Peripheral Nerves

Nitin Agarwal, Andrew Faramand, Nima Alan, Zachary J. Tempel, D. Kojo Hamilton, David O. Okonkwo and Adam S. Kanter

OBJECTIVE

Elderly patients, often presenting with multiple medical comorbidities, are touted to be at an increased risk of peri- and postoperative complications following spine surgery. Various minimally invasive surgical techniques have been developed and employed to treat an array of spinal conditions while minimizing complications. Lateral lumbar interbody fusion (LLIF) is one such approach. The authors describe clinical outcomes in patients over the age of 70 years following stand-alone LLIF.

METHODS

A retrospective query of a prospectively maintained database was performed for patients over the age of 70 years who underwent stand-alone LLIF. Patients with posterior segmental fixation and/or fusion were excluded. The preoperative and postoperative values for the Oswestry Disability Index (ODI) were analyzed to compare outcomes after intervention. Femoral neck t-scores were acquired from bone density scans and correlated with the incidence of graft subsidence.

RESULTS

Among the study cohort of 55 patients, the median age at the time of surgery was 74 years (range 70–87 years). Seventeen patients had at least 3 medical comorbidities at surgery. Twenty-three patients underwent a 1-level, 14 a 2-level, and 18 patients a 3-level or greater stand-alone lateral fusion. The median estimated blood loss was 25 ml (range 5–280 ml). No statistically significant relationship was detected between volume of blood loss and the number of operative levels. The median length of hospital stay was 2 days (range 1–4 days). No statistically significant relationship was observed between the length of hospital stay and age at the time of surgery. There was one intraoperative death secondary to cardiac arrest, with a mortality rate of 1.8%. One patient developed a transient femoral nerve injury. Five patients with symptomatic graft subsidence subsequently underwent posterior instrumentation. A lower femoral neck t-score < −1.0 correlated with a higher incidence of graft subsidence (p = 0.006). The mean ODI score 1 year postoperatively of 31.1 was significantly (p = 0.003) less than the mean preoperative ODI score of 46.2.

CONCLUSIONS

Stand-alone LLIF can be safely and effectively performed in the elderly population. Careful evaluation of preoperative bone density parameters should be employed to minimize risk of subsidence and need for additional surgery. Despite an association with increased comorbidities, age alone should not be a deterrent when considering stand-alone LLIF in the elderly population.

Free access

Andrew T. Hale, David P. Stonko, Li Wang, Megan K. Strother and Lola B. Chambless

OBJECTIVE

Prognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. The goal of this study was to compare the performance of ML algorithms to standard statistical methods when predicting meningioma grade.

METHODS

The cohort included patients aged 18–65 years with WHO grade I (n = 94) and II (n = 34) meningioma in whom preoperative MRI was obtained between 1998 and 2010. A board-certified neuroradiologist, blinded to histological grade, interpreted all MR images for tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, presence of a draining vein, and patient sex. The authors trained and validated several binary classifiers: k-nearest neighbors models, support vector machines, naïve Bayes classifiers, and artificial neural networks as well as logistic regression models to predict tumor grade. The area under the curve–receiver operating characteristic curve was used for comparison across and within model classes. All analyses were performed in MATLAB using a MacBook Pro.

RESULTS

The authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895).

CONCLUSIONS

ML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.

Free access

Andrew T. Hale, David P. Stonko, Amber Brown, Jaims Lim, David J. Voce, Stephen R. Gannon, Truc M. Le and Chevis N. Shannon

OBJECTIVE

Modern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.

METHODS

The clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.

RESULTS

“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).

CONCLUSIONS

This study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.

Free access

Anthony V. Nguyen, Elizabeth E. Blears, Evan Ross, Rishi R. Lall and Juan Ortega-Barnett

OBJECTIVE

Glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.

METHODS

The authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS

Eight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.

CONCLUSIONS

Few studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.

Free access

Todd C. Hollon, Adish Parikh, Balaji Pandian, Jamaal Tarpeh, Daniel A. Orringer, Ariel L. Barkan, Erin L. McKean and Stephen E. Sullivan

OBJECTIVE

Pituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.

METHODS

A retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.

RESULTS

The study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.

CONCLUSIONS

Early postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

Free access

Jan-Karl Burkhardt, George F. Lasker, Ethan A. Winkler, Helen Kim and Michael T. Lawton

As the population ages, the question of how to manage brain arteriovenous malformations in the elderly becomes increasingly relevant. Is resection a reasonable option for these patients? In this study, the authors examined the outcomes of surgical patients 60 years or older and found that favorable outcomes were achieved with careful patient selection. Preoperative grading scales were more predictive of outcomes in patients older than 65 years than in those 60–65 years of age.

Free access

Aiko Terada, Masaki Komiyama, Tomoya Ishiguro, Yasunari Niimi and Hidenori Oishi

The authors performed a nationwide study in Japan to evaluate the annual detected rate of pediatric intracranial arteriovenous (AV) shunts such as brain AV malformations (BAVMs), pial AV fistulas (PAVFs), vein of Galen aneurysmal malformations (VGAMs), and dural AV fistulas (DAVFs). These rates were revealed for the first time and showed that VGAM, DAVF, and PAVF were relatively common but that BAVMs were extremely rare in neonates and infants.