Static v. Expandable TLIF Cage Outcomes

Static cages were introduced in the 1990s as a solution to degenerative spondylolisthesis, recurrent disc herniation and spinal stenosis. As this procedure was popularized, a new class of expandable Transforaminal Lumbar Interbody Fusion devices was introduced to further improve outcomes that will be studied in this project. It will be explored how expandable cages compare to static cages in TLIF procedures in patient-reported outcomes, complications and restoration of appropriate lumbar lordosis. We conducted a retrospective cohort review comparing those who received expandable and static cages. Eligible patients received TLIF procedure at the Rothman Institute, were ≥18 years of age and had radiographic follow-up at 3 months and 1 year postoperatively. Outcomes were measured in lumbar lordosis via calculating angles via radiographic images preoperatively and 3 month and 1 year postoperatively as well as pre- and post-operative SF-12 surveys. At this time, data acquisition is ongoing and no preliminary data has been generated. However, we anticipate better patient reported outcomes and greater and sustained restoration of Lumbar Lordosis in patients who received expandable cages. Data collection is scheduled to be completed shortly. Once completed, this will be a study of greater magnitude and will address the shortage of investigations into the surgical outcomes of static and expandable cages and clarify the theorized benefits of expandable cages. Recent emphasis has been placed on restoring appropriate lumbar lordosis in fusion surgeries and this project was designed to investigate lordosis at different time posts as compared to patient-reported outcomes

Machine Learning Models for 6-Month Survival Prediction after Surgical Resection of Glioblastoma

Introduction: The role of surgical resection for the treatment of glioblastoma multiforme is well established. Survival analysis after resective surgery in the literature comprises mostly of traditional statistical models. Machine learning models offer powerful predictive and analytical capability for varied datasets and offer improved generalizability and scalability. We analyzed survival data of patients with glioblastoma with various machine learning algorithms and compared it to binary logistic regression. Methods: We retrospectively identified cases of glioblastoma treated with surgical resection at our institution from 2012-2018. Feature scaling and one-hot encoding was used to better fit the models to the data and used the formula X’ = (X – Xmin)/(Xmax – Xmin). Feature selection was performed using chi-squared analysis (features with p Results: 582 patients fit the inclusion criteria and were used to build these models. 6-month mortality was 43.13%. Accuracy scores (AUC) for models used were 0.670 (logistic regression), 0.704 (Random Forest), 0.585 (Support Vector Machine), 0.560 (Naïve Bayes), 0.650 (XG Boost), 0.585 (Stochastic Gradient Descent Classifier), and 0.740 (Neural Network). 5-fold cross validation was used to ensure generalizability to an independent dataset. Conclusion: Machine learning methods for prediction of six-month survival for glioblastoma are promising analytical tools that we show can approach or exceed the accuracy of traditional logistic regression, particularly neural networks and the random forest algorithm. Improved prediction of 6-month survival using machine learning offers increased capabilities for patient education, adjuvant chemotherapy or radiation planning, and post-operative counseling, while maintaining increased adaptability and generalizability compared to regression models

Ossification of the Posterior Longitudinal Ligament: Surgical Approaches and Associated Complications.

Ossification of the posterior longitudinal ligament (OPLL) is a rare but potentially devastating cause of degenerative cervical myelopathy (DCM). Decompressive surgery is the standard of care for OPLL and can be achieved through anterior, posterior, or combined approaches to the cervical spine. Surgical correction of OPLL via any approach is associated with higher rates of complications and the presence of OPLL is considered a significant risk factor for perioperative complications in DCM surgeries. Potential complications include dural tear (DT) and subsequent cerebrospinal fluid leak, C5 palsy, hematoma, hardware failure, surgical site infections, and other neurological deficits. Anterior approaches are technically more demanding and associated with higher rates of DT but offer greater access to ventral OPLL pathology. Posterior approaches are associated with lower rates of complications but may allow for continued disease progression. Therefore, the decision to pursue either an anterior or posterior approach to surgical decompression may be critically influenced by complications associated with each procedure. The authors critically review anterior and posterior approaches to surgical decompression of OPLL with particular focus on the complications associated with each approach. We also review the recent work in developing new surgical treatments for OPLL that aim to reduce complication incidence

Determining the Role of Surgery in Diagnosis and Treatment of Primary CNS Lymphoma

Introduction: Primary central nervous system lymphoma (PCNSL) is a rare entity typically treated with a combination of chemotherapy and radiation. The role of surgery is controversial, and biopsy may be non-definitive or injurious. We review our series of stereotactic and excisional biopsy as well as surgical debulking of PCNSL to quantify overall risk and benefits. Methods: Patients with biopsy-confirmed intracranial PCNSL were identified from a large singlecenter academic institution between 2012-2018. Preoperative factors and perioperative outcomes were retrospectively reviewed. Results: A total of 61 cases of PCNSL were identified. Most patients presented with confusion (23.0%), weakness/paralysis (19.7%), and gait disturbance (18.0%). 1.6% were incidentally identified. HIV status was positive in 8.2% of cases. CSF cytology was positive for malignancy in 33.3% of applicable cases. Of all procedures, 44.3% were needle biopsy, 27.9% were open excisional biopsies, and 27.9% were surgical debulking procedures. Prior biopsy had been performed in 9.8%, of which 83.3% (5/6) were positive for PCNSL. Intraoperative frozen pathology failed to illicit a definitive diagnosis in 39.3% of cases despite adequate sampling. Stereotactic biopsies did not demonstrate an increased risk of non-diagnostic frozen pathology compared to open excisional biopsy. Intraoperative complications, 30-day mortality, and long-term survival was not associated with open vs. stereotactic biopsy. Discussion: Biopsy of PCNSL carries a moderate surgical risk that should not be discounted, particularly in the setting of previously diagnosed PCNSL or with evidence of malignancy in CSF cytology. Early initiation of chemotherapy continues to be the mainstay of long-term response and control

Characterizing the Anesthetic Management of Patients Undergoing Transradial Cardiac and Cerebrovascular Interventions: A Single-Institution Study

Introduction: Transradial approaches are shown to reduce mortality, morbidity, access-site complications, hospital stay and costs when compared to the transfemoral approach in multiple cardiac trials. Use of the antispasmotic cocktail for these interventions poses challenges for anesthetic management. Differing practices between fields may need to be consolidated. This study aims to characterize the safety profile of cardiac and neuroanesthetic management protocols for transradial interventions, hypothesizing that the cocktail is safe from an anesthetic perspective. Methods: We performed a retrospective chart review of patients undergoing transradial diagnostic cardiac and cerebrovascular interventions. Data collected included age, sex, comorbidities, prior antihypertensive medications, dose of radial antispasmotic cocktail, sedation dose, and periprocedural blood pressure changes. Results: Within 25 cardiac patients the average total cardene dose was 643.75 mcg, given over 3.2 injections on average. For 15 cerebrovascular patients, the average cardene dose was 458 mcg over 0.8 injections on average. Eleven cardiac patients received an average of 160 mcg of nitroglycerin, while 11 cerebrovascular patients received an average of 200 mcg of nitroglycerin. Average periprocedural MAP drop from induction was 29.72 in cardiac patients and 26.60 in cerebrovascular patients. Discussion: Our data demonstrate that anesthetic management has a favorable safety profile for both our cardiac and cerebrovascular patients. The drop in MAP between the two cohorts demonstrates no appreciable difference on descriptive analysis. Initial data demonstrate that, on average, cardiac patients may be receiving higher doses of cardene and lower doses of nitroglycerin. Further data may guide protocol consolidation

Capturing Initial Understanding and Impressions of Surgical Therapy for Parkinson\u27s Disease

Background: Deep Brain Stimulation (DBS) is an underutilized surgical therapy for Parkinson\u27s Disease (PD). Both physician and patient hesitancies have been described as potential barriers to DBS, but the specifics of patient perceptions of DBS have not been well-characterized in the general PD population. Objective: To characterize the understanding and impressions of surgical therapy in PD patients prior to formal surgical evaluation. Methods: A 30-question survey assessing impressions of surgical therapy for PD and understanding of DBS for PD was administered to PD patients seen at an urban movement disorders clinic. Results: One hundred and two patients completed the survey. When asked if they would undergo a hypothetical risk-free, curative brain surgery for PD, 98 patients responded “yes.” Patients were more agreeable to “reversible,” “minimally-invasive,” and “incisionless” surgery. 51.2% thought DBS is an “effective” treatment for PD, 76.6% thought it was “invasive,” and 18.3% thought it was “reversible.” 45.2% reported fear of being awake during DBS surgery. Regarding costs, 52.4% were concerned that DBS was “very expensive” or “not covered by insurance.” Initial source of information and perceived treatment effectiveness were not associated with concerns about DBS effectiveness or threats to normality. Negative perceptions of past surgery were associated with concerns about DBS altering mood and personality. Conclusion: Overall, patients expressed concerns regarding procedural efficacy, invasiveness, cost, and irreversibility—independent of the original source of information. Future studies are required to allow us to better understand the impact of these initial findings on DBS hesitancy and underutilization

Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke.

BACKGROUND: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities. AIMS: 582 studies were identified on initial searching of the PubMed database. Of these studies, 106 full texts were assessed after title and abstract screening which resulted in 489 papers excluded. Of these 106 studies, 79 were excluded due to using cohorts from outside the United States or being review articles or editorials. 27 studies were thus included in this analysis. SUMMARY OF REVIEW: Of the 27 studies included, 7 (25.9%) used patient data from California, 6 (22.2%) were multicenter, 3 (11.1%) were in Massachusetts, 2 (7.4%) each in Illinois, Missouri, and New York, and 1 (3.7%) each from South Carolina, Washington, West Virginia, and Wisconsin. 1 (3.7%) study used data from Utah and Texas. These were qualitatively compared to a CDC study showing the highest distribution of stroke in Mississippi (4.3%) followed by Oklahoma (3.4%), Washington D.C. (3.4%), Louisiana (3.3%), and Alabama (3.2%) while the prevalence in California was 2.6%. CONCLUSIONS: It is clear that a strong disconnect exists between the datasets and patient cohorts used in training machine learning algorithms in clinical research and the stroke distribution in which clinical tools using these algorithms will be implemented. In order to ensure a lack of bias and increase generalizability and accuracy in future machine learning studies, datasets using a varied patient population that reflects the unequal distribution of stroke risk factors would greatly benefit the usability of these tools and ensure accuracy on a nationwide scale

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis.

INTRODUCTION: Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning for feature importance analysis and prediction in first-pass reperfusion. METHODS: We retrospectively identified cases of ischemic stroke treated with mechanical thrombectomy (MT) at our institution from 2012-2018. Significant variables used in predictive modeling were demographic characteristics, medical history, admission NIHSS, and stroke characteristics. Outcome was binarized TICI on first pass (0-2a vs 2b-3). Shapley feature importance plots were used to identify variables that strongly affected outcomes. RESULTS: Accuracy for the Random Forest and SVM models were 67.1% compared to 65.8% for the logistic regression model. Brier score was lower for the Random Forest model (0.329 vs 0.342) indicating better predictive capability. Other supervised learning models performed worse than the logistic regression model, with accuracy of 56.2% for Naïve Bayes and 61.6% for XGBoost. Shapley plots for the Random Forest model showed use of aspiration, hyperlipidemia, hypertension, use of stent retriever, and time between symptom onset and catheterization as the top five predictors of first pass reperfusion. CONCLUSION: Use of machine learning models, such as Random Forest, for the study of MT outcomes, is more accurate than logistic regression for our dataset, and identifies new factors that contribute to achieving first pass reperfusion. The benefits of machine learning, such as improved predictive capabilities, integration of new data, and generalizability, establish ML as the preferred model for studying outcomes in stroke

Transradial approach for the treatment of brain aneurysms using flow diversion: feasibility, safety, and outcomes

The aim of this paper is to describe our experience with the transradial approach (TRA) for intracranial aneurysms treatment using the Pipeline Embolization device (PED). We also performed a logistic regression comparing outcomes between TR and transfemoral approach (TFA). The clinical and imaging characteristics as well as periprocedural outcomes of patients treated for aneurysms using a TRA with PED were analyzed retrospectively. A total of 598 aneurysms were treated with pipeline during a period extending from 2010-2019 (N.=580 [97%] using TFA vs. N.=18 [3%] using TRA). About 84.28% (N.=504) were females, and the average age was 55.5 years. There was no significant difference between both approaches in procedural duration, complication rate, morbidity, and aneurysms obliteration. We did not encounter any access site complication in the TR group compared to 2% in the TF; however, this did not reach statistical significance. One case was converted to TFA due to the need for more support. Multivariate analysis did not show that the access site was an independent predictor of the complications, morbidity, and aneurysms obliteration. The results of this study illustrate that the transradial approach is a safe and effective means of treating intracranial aneurysms with PED. The TRA in selected patients offers better safety with high rates of procedural success