Eric K. Oermann

An update to the Raymond–Roy Occlusion Classification of intracranial aneurysms treated with coil embolization

Background The Raymond–Roy Occlusion Classification (RROC) is the standard for evaluating coiled aneurysms (Class I: complete obliteration; Class II: residual neck; Class III: residual aneurysm), but not all Class III aneurysms behave the same over time. Methods This is a retrospective review of 370 patients with 390 intracranial aneurysms treated with coil embolization. A Modified Raymond–Roy Classification (MRRC), in which Class IIIa designates contrast within the coil interstices and Class IIIb contrast along the aneurysm wall, was applied retrospectively. Results Class IIIa aneurysms were more likely to improve to Class I or II than Class IIIb aneurysms (83.34% vs 14.89%, p<0.001) and were also more likely than Class II to improve to Class I (52.78% vs 16.90%, p<0.001). Class IIIb aneurysms were more likely to remain incompletely occluded than Class IIIa aneurysms (85.11% vs 16.67%, p<0.001). Class IIIb aneurysms were larger with wider necks while Class IIIa aneurysms had higher packing density. Class IIIb aneurysms had a higher retreatment rate (33.87% vs 6.54%, p<0.001) and a trend toward higher subsequent rupture rate (3.23% vs 0.00%, p=0.068). Conclusions We propose the MRRC to further differentiate Class III aneurysms into those likely to progress to complete occlusion and those likely to remain incompletely occluded or to worsen. The MRRC has the potential to expand the definition of adequate coil embolization, possibly decrease procedural risk, and help endovascular neurosurgeons predict which patients need closer angiographic follow-up. These findings need to be validated in a prospective study with independent blinded angiographic grading.

Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations

Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using three prospective databases, a machine learning approach of feature engineering and model optimization was implemented to create the most accurate predictor of AVM outcomes. Existing prognostic systems were scored for purposes of comparison. The final predictor was secondarily validated on an independent site’s dataset not utilized for initial construction. Out of 1,810 patients, 1,674 to 1,291 patients depending upon time threshold, with 23 features were included for analysis and divided into training and validation sets. The best predictor had an average area under the curve (AUC) of 0.71 compared to existing clinical systems of 0.63 across all time points. On the heldout dataset, the predictor had an accuracy of around 0.74 at across all time thresholds with a specificity and sensitivity of 62% and 85% respectively. This machine learning approach was able to provide the best possible predictions of AVM radiosurgery outcomes of any method to date, identify a novel radiobiological feature (3D surface dose), and demonstrate a paradigm for further development of prognostic tools in medical care.

Predictors of treatment failure following coil embolization of intracranial aneurysms

We present a retrospective review of 357 consecutive patients with 419 aneurysms treated with coil embolization. Although incomplete occlusion and recurrence of intracranial aneurysms following coil embolization is a well-known problem, the factors that influence and predict treatment failure are still debated. For this study, we excluded non-coiling endovascular techniques (flow diversion) and non-saccular aneurysms (fusiform). The modified Raymond-Roy occlusion classification (MRRC) was used to grade the aneurysms. Treatment failure was defined as filling of the aneurysm dome (MRRC Class IIIa or IIIb) at the first angiographic follow-up (average 8 months). Univariate statistical tests were employed to select variables for incorporation into a multivariable logistic regression model. Multivariate analysis identified greater aneurysm volume (p<0.001), packing density (PD) less than 31% (p=0.007) and initial MRRC Class IIIb (p<0.001) as predictors of treatment failure. Incomplete neck coverage with coils was associated with treatment failure in univariate but not multivariate analysis. Class IIIb status was more predictive of treatment failure compared to all Class III (odds ratio 168 versus 14.4). Clinical outcomes were similar in both groups except that there were more retreatments in the treatment failure group (p<0.001). Aneurysm volume, PD and initial occlusion class are associated with angiographic outcome, consistent with prior literature. The MRRC is a powerful predictor of treatment failure. These results will be useful in the effort to both prevent and predict treatment failure after coil embolization, however, they should be verified in a prospective study.

Angiographic outcome of intracranial aneurysms with neck remnant following coil embolization.

Background The degree of aneurysm occlusion following coil embolization has an impact on aneurysm recanalization. Objective To explain the natural history of intracranial aneurysms with neck remnant, Raymond–Roy Occlusion Classification (RROC) class II. Methods A single-center, retrospective study of 198 patients with 209 aneurysms treated with coil embolization that were initially either RROC class I or II. The angiographic outcomes at short- and long-term follow-up were compared as well as the complication/re-treatment rates. Atypical aneurysms and those that had been previously treated were excluded. Results Ninety-nine class I aneurysms were compared with 110 class II aneurysms. There was no difference in recanalization rate between the groups (class I 3.3% vs class II 8.5%, p=0.478) at short-term follow-up (8.2 months) and at subsequent follow-ups (21.7 and 52.1 months). There was also no difference in re-treatment rates (class I 3.3% vs class II 8.5%, p=0.196) or complication rates (class I 9.1% vs class II 4.6%, p=0.12). There were no aneurysm ruptures after treatment in either group. Conclusions The angiographic outcome of aneurysms with neck remnant following coil embolization is similar to that of completely occluded aneurysms in that most remain stable and few recanalize. This understanding could potentially help the interventional neurosurgeon avoid complications such as coil herniation, vessel compromise, and stroke in selected cases. Further investigation with a larger patient population is warranted.

Factors associated with successful revascularization using the aspiration component of ADAPT in the treatment of acute ischemic stroke

Introduction ADAPT (a direct aspiration first pass technique) has been shown to be fast, cost-effective, and associated with excellent angiographic and clinical outcomes in the treatment of acute ischemic stroke (AIS). Objective To identify any and all preoperative factors that are associated with successful revascularization using aspiration alone. Methods A retrospective review of 76 patients with AIS treated with thrombectomy was carried out. Cohort 1 included cases in which aspiration alone was successful (Thrombolysis in Cerebral Infarction 2b or 3). Cohort 2 included cases in which aspiration was unsuccessful or could not be performed despite an attempt. Results There was no difference between cohorts in gender, race, medications, National Institute of Health Stroke Scale score, IV tissue plasminogen activator, site or side of the occlusion, dense vessel sign, aortic arch type, severe stenosis, clot length, operator years of experience, and guide/aspiration catheters used. Patients in cohort 1 were on average younger (66.5 vs 74.1 years, p=0.025). There was a trend for more patients in cohort 2 to have atrial fibrillation/arrhythmias (62.5% vs 45.5%, p=0.168) and have a cardiogenic stroke etiology (78.1% vs 56.8%, p=0.086). There was also a trend for more reverse curves (2.3 vs 1.7, p=0.107), larger vessel diameter (3.26 mm vs 2.88 mm, p=0.184), larger vessel-to-catheter ratio (2.09 vs 1.87, p=0.192), and worse clot burden score (5.38 vs 6.68, p=0.104) in cohort 2. Conclusions Aspiration success was associated with younger age. Our findings suggest that ADAPT can be used for the vast majority of patients but it may be beneficial to use a different method first in the elderly.

Coagulation Profile as a Risk Factor for 30- Day Morbidity and Mortality Following Posterior Lumbar Fusion.

Study Design. A retrospective cohort study. Objective. The aim of this study was to identify associations between abnormal coagulation profile and postoperative morbidity and mortality in patients undergoing posterior lumbar fusion (PLF). Summary of Background Data. The literature suggests that abnormal coagulation profile is associated with postoperative complications, notably the need for blood transfusion. However, there is little research that directly addresses the influence of coagulation profile on postoperative complications following PLF. Methods. The American College of Surgeons National Surgical Quality Improvement Program database (ACS-NSQIP) was utilized to identify patients undergoing PLF between 2006 and 2013. Nine thousand two hundred ninety-five patients met inclusion criteria. Multivariate analysis was utilized to identify associations between abnormal coagulation profile and postoperative complications. Results. Low platelet count was an independent risk factor for organ space surgical site infections (SSIs) [odds ratio (OR) = 6.0, P < 0.001], ventilation >48 hours (OR = 4.5, P = 0.002), Acute renal failure (OR = 5.8, P = 0.007), transfusion (OR = 1.6, P < 0.001), sepsis (OR = 2.2, P = 0.037), reoperation (OR = 2.5, P = 0.001), and death (OR = 3.7, P = 0.049). High partial thromboplastin time (PTT) was an independent risk factor for ventilation >48 hours (OR = 5.6, P = 0.002), cerebrovascular accident (CVA)/stroke with neurological deficit (OR = 5.1, P = 0.011), cardiac arrest (OR = 5.4, P = 0.030), transfusion (OR = 1.5, P = 0.020), and death (OR = 4.5, P = 0.050). High International Normalized Ration (INR) was an independent risk factor for pneumonia (OR = 8.7, P = 0.001), pulmonary embolism (OR = 5.6, P = 0.021), deep venous thrombosis/Thrombophlebitis (OR = 4.8, P = 0.011), septic shock (OR = 8.4, P = 0.048), and death (OR = 9.8, P = 0.034). Bleeding disorder was an independent risk factor for organ space SSI (OR = 5.4, P = 0.01), pneumonia (OR = 3.0, P = 0.023), and sepsis (OR = 4.4, P < 0.001). Conclusion. Abnormal coagulation profile was an independent predictor of morbidity and mortality in patients undergoing PLF. As such, it should be considered in preoperative optimization and risk stratification. Level of Evidence: 3

Cervical-petrous internal carotid artery pseudoaneurysm presenting with otorrhagia treated with endovascular techniques

Cervical–petrous internal carotid artery (CP-ICA) pseudoaneurysms are rare and have different etiologies, presentations, and treatment options. A middle-aged patient with a history of chronic otitis media presented with acute otorrhagia and was found to have a left-sided CP-ICA pseudoaneurysm. The patient was a poor surgical candidate with difficult arterial access. The pseudoaneurysm was treated with stand-alone coiling via a left brachial approach with persistent contrast filling seen only in the aneurysm neck at the end of the procedure. The patient re-presented 12 days later with repeat hemorrhage and rapid enlargement of the neck remnant, and was treated with a covered stent via a transcervical common carotid artery cut-down. A covered stent may provide a more definitive treatment for CP-ICA pseudoaneurysms compared with standalone coiling.

The impact of evidence: evolving therapy for acute ischemic stroke in a large healthcare system.

Background With a recent surge of clinical trials, the treatment of ischemic stroke has undergone dramatic changes. Objective To evaluate the impact of evidence and a revamped stroke protocol on a large healthcare system. Methods A retrospective review of 69 patients with ischemic stroke treated with intra-arterial therapy was carried out. Cohort 1 included patients treated before implementation of a new stroke protocol, and cohort 2 after implementation. Angiographic outcome was graded using the Thrombolysis in Cerebral Infarction (TICI) score. Clinical outcomes were assessed using the National Institute of Health Stroke Scale (NIHSS) and modified Rankin Scale (mRS). Results Primary outcomes comparing cohorts demonstrated decreased arrival-to-puncture time (cohort 2: 104 vs cohort 1: 181 min, p<0.001), similar TICI 2b/3 rates (86.5% vs 81.3%, p=0.5530), and similar percentage of patients with discharge mRS 0–2 (18.9% vs 21.9%, p=0.7740). Notable secondary outcomes for cohort 2 included decreased puncture-to-first pass time (34 vs 53 min, p <0.001), increased TICI 3 rates (37.8% vs 18.8%, p=0.0290), a trend toward greater improvements in NIHSS on postoperative day 1 (6.8 vs 2.6, p=0.0980) and discharge (9.5 vs 6.7, p=0.1130), and a trend toward increased percentage of patients discharged with mRS 0–3 (48.6% vs 34.4%, p=0.3280 NS). There were similar rates of symptomatic intracerebral hemorrhage (10.8% vs 9.4%, p=0.9570) and death (10.8% vs 15.6%, p=0.5530). Conclusions An interdisciplinary and rapid response to the emergence of strong clinical evidence can result in dramatic changes in a large healthcare system.

Automated deep-neural-network surveillance of cranial images for acute neurologic events

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function—‘time is brain’1–5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6–10. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data11–15. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework16. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment.A deep-learning algorithm is developed to provide rapid and accurate diagnosis of clinical 3D head CT-scan images to triage and prioritize urgent neurological events, thus potentially accelerating time to diagnosis and care in clinical settings.

Deep learning guided stroke management: a review of clinical applications

Stroke is a leading cause of long-term disability, and outcome is directly related to timely intervention. Not all patients benefit from rapid intervention, however. Thus a significant amount of attention has been paid to using neuroimaging to assess potential benefit by identifying areas of ischemia that have not yet experienced cellular death. The perfusion–diffusion mismatch, is used as a simple metric for potential benefit with timely intervention, yet penumbral patterns provide an inaccurate predictor of clinical outcome. Machine learning research in the form of deep learning (artificial intelligence) techniques using deep neural networks (DNNs) excel at working with complex inputs. The key areas where deep learning may be imminently applied to stroke management are image segmentation, automated featurization (radiomics), and multimodal prognostication. The application of convolutional neural networks, the family of DNN architectures designed to work with images, to stroke imaging data is a perfect match between a mature deep learning technique and a data type that is naturally suited to benefit from deep learning’s strengths. These powerful tools have opened up exciting opportunities for data-driven stroke management for acute intervention and for guiding prognosis. Deep learning techniques are useful for the speed and power of results they can deliver and will become an increasingly standard tool in the modern stroke specialist’s arsenal for delivering personalized medicine to patients with ischemic stroke.