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Brain arteriovenous malformations (AVMs) are complex and heterogeneous lesions that can rupture, causing significant morbidity and mortality. While ruptured lesions are usually treated, the management of unruptured AVMs remains unclear. A Randomized trial of Unruptured Brain Arteriovenous Malformations (ARUBA) was the first trial conducted to compare the effects of medical and interventional therapy. Although it concluded that medical therapy was superior in preventing stroke and death over a follow-up period of 33 months, the findings were met with intense criticism regarding several aspects of study design, progression, and analysis/conclusion. Namely, the increased use of stand-alone embolisation relative to microsurgery in a cohort with predominantly low-grade lesions combined with a short follow-up period amplified treatment risk. Subsequently, several observational studies were conducted on ARUBA-eligible patients to investigate the safety and efficacy of microsurgery, radiosurgery, and endovascular embolisation over longer follow-up periods. These reports showed that favourable safety profiles and cure rates can be achieved with appropriate patient selection and judicious use of different treatment modalities in multidisciplinary centres. Since large prospective randomised trials on AVMs may not be feasible, it is important to make use of practice-based data beyond the flawed ARUBA study to optimise patients’ lifetime outcomes.

BACKGROUND Within the literature, there has been limited research tracking the career trajectories of international medical graduates (IMGs) following residency training. OBJECTIVE To compare the characteristics of IMG and US medical school graduate (USMG) neurosurgeons holding academic positions in the United States and also analyze factors that influence IMG career trajectories following US-based residency training. METHODS We collected data on 243 IMGs and 2506 USMGs who graduated from Accreditation Council for Graduate Medical Education (ACGME)-accredited neurosurgery residency programs. We assessed for significant differences between cohorts, and a logistic regression model was used for the outcome of academic career trajectory. RESULTS Among the 2749 neurosurgeons in our study, IMGs were more likely to pursue academic neurosurgery careers relative to USMGs (59.7% vs 51.1%; P = .011) and were also more likely to complete a research fellowship before beginning residency (odds ratio [OR] = 9.19; P < .0001). Among current US academic neurosurgeons, USMGs had significantly higher pre-residency h-indices relative to IMGs (1.23 vs 1.01; P < .0001) with no significant differences between cohorts when comparing h-indices during (USMG = 5.02, IMG = 4.80; P = .67) or after (USMG = 14.05, IMG = 13.90; P = .72) residency. Completion of a post-residency clinical fellowship was the only factor independently associated with an academic career trajectory among IMGs (OR = 1.73, P = .046). CONCLUSION Our study suggests that while IMGs begin their US residency training with different research backgrounds and achievements relative to USMG counterparts, they attain similar levels of academic productivity following residency. Furthermore, IMGs are more likely to pursue academic careers relative to USMGs. Our work may be useful for better understanding IMG career trajectories following US-based neurosurgery residency training.

Background and Purpose— The management of unruptured brain arteriovenous malformations remains unclear. Using a large cohort to determine risk factors predictive of hemorrhagic presentation of arteriovenous malformations, this study aims to develop a predictive tool that could guide hemorrhage risk stratification. Methods— A database of 789 arteriovenous malformation patients presenting to our institution between 1990 and 2017 was used. A hold-out method of model validation was used, whereby the data was randomly split in half into training and validation data sets. Factors significant at the univariable level in the training data set were used to construct a model based on multivariable logistic regression. Model performance was assessed using receiver operating curves on the training, validation, and complete data sets. The predictors and the complete data set were then used to derive a risk prediction formula and a practical scoring system, where every risk factor was worth 1 point except race, which was worth 2 points (total score varies from 0 to 6). The factors are summarized by R 2 eD arteriovenous malformation (acronym: R 2 eD AVM). Results— In 755 patients with complete data, 272 (36%) presented with hemorrhage. From the training data set, a model was derived containing the following risk factors: nonwhite race (odds ratio [OR]=1.8; P =0.02), small nidus size (OR=1.47; P =0.14), deep location (OR=2.3; P< 0.01), single arterial feeder (OR=2.24; P< 0.01), and exclusive deep venous drainage (OR=2.07; P =0.02). Area under the curve from receiver operating curve analysis was 0.702, 0.698, and 0.685 for the training, validation, and complete data sets, respectively. In the entire study population, the predicted probability of hemorrhagic presentation increased in a stepwise manner from 16% for patients with no risk factors (score of 0) to 78% for patients having all the risk factors (score of 6). Conclusions— The final model derived from this study can be used as a predictive tool that supplements clinical judgment and aids in patient counseling.

OBJECTIVEIncidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP).METHODSThe authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online.RESULTSOf the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97).CONCLUSIONSUsing multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.

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