The role of decompressive craniectomy following microsurgical repair of a ruptured aneurysm: Analysis of a South Australian cerebrovascular registry.

OBJECTIVE: Decompressive craniectomy (DC) remains a controversial intervention for intracranial hypertension among patients with aneurysmal subarachnoid haemorrhage (aSAH). METHODS: We identified aSAH patients who underwent DC following microsurgical aneurysm repair from a prospectively maintained registry and compared their outcomes with a propensity-matched cohort who did not. Logistic regression was used to identify predictors of undergoing decompressive surgery and post-operative outcome. Outcomes of interest were inpatient mortality, unfavourable outcome, NIS-Subarachnoid Hemorrhage Outcome Measure and modified Rankin Score (mRS). RESULTS: A total of 246 patients with aSAH underwent clipping of the culprit aneurysm between 01/09/2011 and 20/07/2020. Of these, 46 underwent DC and were included in the final analysis. Unsurprisingly, DC patients had a greater chance of unfavourable outcome (p < 0.001) and higher median mRS (p < 0.001) at final follow-up. Despite this, almost two-thirds (64.1 %) of DC patients had a favourable outcome at this time-point. When compared with a propensity-matched cohort who did not, patients treated with DC fared worse at all endpoints. Multivariable logistic regression revealed that the presence of intracerebral haemorrhage and increased pre-operative mid-line shift were predictive of undergoing DC, and WFNS grade ≥ 4 and a delayed ischaemic neurological deficit requiring endovascular angioplasty were associated with an unfavourable outcome. CONCLUSIONS: Our data suggest that DC can be performed with acceptable rates of morbidity and mortality. Further research is required to determine the superiority, or otherwise, of DC compared with structured medical management of intracranial hypertension in this context, and to identify predictors of requiring decompressive surgery and patient outcome.

Identifying epilepsy surgery referral candidates with natural language processing in an Australian context.

OBJECTIVE: Epilepsy surgery is known to be underutilized. Machine learning-natural language processing (ML-NLP) may be able to assist with identifying patients suitable for referral for epilepsy surgery evaluation. METHODS: Data were collected from two tertiary hospitals for patients seen in neurology outpatients for whom the diagnosis of "epilepsy" was mentioned. Individual case note review was undertaken to characterize the nature of the diagnoses discussed in these notes, and whether those with epilepsy fulfilled prespecified criteria for epilepsy surgery workup (namely focal drug refractory epilepsy without contraindications). ML-NLP algorithms were then developed using fivefold cross-validation on the first free-text clinic note for each patient to identify these criteria. RESULTS: There were 457 notes included in the study, of which 250 patients had epilepsy. There were 37 (14.8%) individuals who fulfilled the prespecified criteria for epilepsy surgery referral without described contraindications, 32 (12.8%) of whom were not referred for epilepsy surgical evaluation in the given clinic visit. In the prediction of suitability for epilepsy surgery workup using the prespecified criteria, the tested models performed similarly. For example, the random forest model returned an area under the receiver operator characteristic curve of 0.97 (95% confidence interval 0.93-1.0) for this task, sensitivity of 1.0, and specificity of 0.93. SIGNIFICANCE: This study has shown that there are patients in tertiary hospitals in South Australia who fulfill prespecified criteria for epilepsy surgery evaluation who may not have been referred for such evaluation. ML-NLP may assist with the identification of patients suitable for such referral. PLAIN LANGUAGE SUMMARY: Epilepsy surgery is a beneficial treatment for selected individuals with drug-resistant epilepsy. However, it is vastly underutilized. One reason for this underutilization is a lack of prompt referral of possible epilepsy surgery candidates to comprehensive epilepsy centers. Natural language processing, coupled with machine learning, may be able to identify possible epilepsy surgery candidates through the analysis of unstructured clinic notes. This study, conducted in two tertiary hospitals in South Australia, demonstrated that there are individuals who fulfill criteria for epilepsy surgery evaluation referral but have not yet been referred. Machine learning-natural language processing demonstrates promising results in assisting with the identification of such suitable candidates in Australia.

Peri-medullary anterior spinal artery aneurysm associated with cervical pial arterio-venous fistula.

Spinal arteriovenous fistulas represent a heterogenous group of pathologies and are divided into four categories. Type IV fistulas are further stratified into three groups (IVa, IVb and IVc) according to the number and dimensions of the fistulous vessels. Approximately 10% of these fistulas are associated with aneurysms. However, we are not aware of a previously reported case of an aneurysm associated with a Type IVa fistula with an anterior spinal artery (ASA) feeder at the cervico-medullary junction. We therefore describe our experience with a patient presenting with a ruptured aneurysm associated with a fistula in this location.

Early minimally invasive intracerebral hemorrhage evacuation: a phase 2a feasibility, safety, and promise of surgical efficacy study.

BACKGROUND: Surgical treatment of intracerebral hemorrhage (ICH) is unproven, although meta-analyses suggest that both early conventional surgery with craniotomy and minimally invasive surgery (MIS) may be beneficial. We aimed to demonstrate the safety, feasibility, and promise of efficacy of early MIS for ICH using the Aurora Surgiscope and Evacuator. METHODS: We performed a prospective, single arm, phase IIa Simon's two stage design study at two stroke centers (10 patients with supratentorial ICH volumes ≥20 mL and National Institutes of Health Stroke Scale (NIHSS) score of ≥6, and surgery commencing <12 hours after onset). Positive outcome was defined as ≥50% 24 hour ICH volume reduction, with the safety outcome lack of significant ICH reaccumulation. RESULTS: From December 2019 to July 2020, we enrolled 10 patients at two Australian Comprehensive Stroke Centers, median age 70 years (IQR 65-74), NIHSS score 19 (IQR 19-29), ICH volume 59 mL (IQR 25-77), at a median of 227 min (IQR 175-377) post-onset. MIS was commenced at a median time of 531 min (IQR 437-628) post-onset, had a median duration of 98 min (IQR 77-110), with a median immediate postoperative hematoma evacuation of 70% (IQR 67-80%). A positive outcome was achieved in 5/5 first stage patients and in 4/5 second stage patients. One patient developed significant 24 hour ICH reaccumulation; otherwise, 24 hour stability was observed (median reduction 71% (IQR 61-80), 5/9 patients <15 mL residual). Three patients died, unrelated to surgery. There were no surgical safety concerns. At 6 months, the median modified Rankin Scale score was 4 (IQR 3-6) with 30% achieving a score of 0-3. CONCLUSION: In this study, early ICH MIS using the Aurora Surgiscope and Evacuator appeared to be feasible and safe, warranting further exploration. TRIAL REGISTRATION NUMBER: ACTRN12619001748101.

Identifying epilepsy surgery candidates with natural language processing: A systematic review.

INTRODUCTION: Epilepsy surgery is an underutilised, efficacious management strategy for selected individuals with drug-resistant epilepsy. Natural language processing (NLP) may aid in the identification of patients who are suitable to undergo evaluation for epilepsy surgery. The feasibility of this approach is yet to be determined. METHOD: In accordance with the PRISMA guidelines, a systematic review of the databases PubMed, EMBASE and Cochrane library was performed. This systematic review was prospectively registered on PROSPERO. RESULTS: 6 studies fulfilled inclusion criteria. The majority of included studies reported on datasets from only a single centre, with one study utilising data from two centres and one study six centres. The most commonly employed algorithms were support vector machines (5/6), with only one study utilising NLP strategies such as random forest models and gradient boosted machines. However, the results are promising, with all studies demonstrating moderate to high levels of performance in the identification of patients who may be suitable to undergo epilepsy surgery evaluation. Furthermore, multiple studies demonstrated that NLP could identify such patients 1-2 years prior to the treating clinicians instigating referral. However, no studies were identified that have evaluated the influence of implementing such algorithms on healthcare systems or patient outcomes. CONCLUSIONS: NLP is a promising approach to aid in the identification of patients that may be suitable to undergo epilepsy surgery evaluation. Further studies are required examining diverse datasets with additional analytical methodologies. Studies evaluating the impact of implementation of such algorithms would be beneficial.

Artificial intelligence and the potential for perioperative delabeling of penicillin allergies for neurosurgery inpatients.

PURPOSE OF THE ARTICLE: Patients with penicillin allergy labels are more likely to have postoperative wound infections. When penicillin allergy labels are interrogated, a significant number of individuals do not have penicillin allergies and may be delabeled. This study was conducted to gain preliminary evidence into the potential role of artificial intelligence in assisting with perioperative penicillin adverse reaction (AR) evaluation. MATERIAL AND METHODS: A single-centre retrospective cohort study of consecutive emergency and elective neurosurgery admissions was conducted over a two-year period. Previously derived artificial intelligence algorithms for the classification of penicillin AR were applied to the data. RESULTS: There were 2063 individual admissions included in the study. The number of individuals with penicillin allergy labels was 124; one patient had a penicillin intolerance label. Of these labels, 22.4% were not consistent with classifications using expert criteria. When the artificial intelligence algorithm was applied to the cohort, the algorithm maintained a high level of classification performance (classification accuracy 98.1% for allergy versus intolerance classification). CONCLUSIONS: Penicillin allergy labels are common among neurosurgery inpatients. Artificial intelligence can accurately classify penicillin AR in this cohort, and may assist in identifying patients suitable for delabeling.

Neurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learning.

INTRODUCTION: Deep learning may be able to assist with the prediction of neurosurgical inpatient outcomes. The aims of this study were to investigate deep learning and transfer learning in the prediction of several inpatient outcomes including timing of discharge and discharge destination. METHOD: Data were collected on consecutive neurosurgical admissions from existing databases over a 15-month period. Following pre-processing artificial neural networks were applied to admission notes and ward round notes to predict four inpatient outcomes. Models were developed on the training dataset, before being tested on a hold-out test dataset and a validation dataset. RESULTS: 1341 individual admissions were included in the study. Using transfer learning and an artificial neural network an area under the receiver operator curve (AUC) of 0.81 and 0.80 on the derivation and validation datasets was able to be achieved for the prediction of discharge within the next 48 hours using daily ward round notes. This result is in comparison to an AUC of 0.71 and 0.68 using an artificial neural network without transfer learning for the same outcome. When the artificial neural network with transfer learning was applied to the other outcomes AUC of 0.72, 0.93 and 0.83 was achieved on the validation datasets for predicting discharge within the next 7 days, survival to discharge and discharge to home as a destination. CONCLUSIONS: Deep learning may predict inpatient neurosurgery outcomes from free-text medical data. Recurrent predictions with ward round notes enable the use of information obtained throughout hospital admissions in these estimates.

Time to hospital presentation following intracerebral haemorrhage: Proportion of patients presenting within eight hours and factors associated with delayed presentation.

PURPOSE: Prolonged time to diagnosis of primary intracerebral haemorrhage (ICH) can result in delays in obtaining appropriate blood pressure control, reversal of coagulopathy or surgical intervention in select cases. We sought to characterise the time to diagnosis in a cohort of patients with ICH and identify factors associated with delayed diagnosis. METHODOLOGY: The stroke database of our hospital was retrospectively reviewed to identify patients presenting to our hospitals emergency department with ICH over two years (January 2017-December 2018.) Data collected included demographics (age and sex), comorbidities, anticoagulation status, clinical scores (NIHSS, GCS, ICH score), and imaging (anatomical site, haematoma size). Time from symptom onset to diagnosis and hospital presentation were recorded. Factors associated with diagnosis >8 h post ictus were assessed using a univariate and then multivariable analysis. RESULTS: 235 patients were identified with 125 males (53%) and a median age of 76 (range 40-98). For the 200 patients that initially presented to our hospital, median time to presentation was 179 min (IQR 77-584 min), and median time from ictus to imaging diagnosis was 268 min (IQR 114-717 min). 139 (70%) presented within 8 h of symptom onset, and 129 (65%) patients had imaging of the brain performed within 8 h of symptom onset. Factors associated with presentation >8 h post symptom onset included wake up stroke (OR 5.31, 95% confidence interval (CI) 2.36-11.96, p < 0.0001) and age (OR 1.04, 95% CI 1.01-1.08, p = 0.01). Patients with hemiplegia were less likely to present >8 h following ictus (OR 0.41, 95% CI 0.21-0.84, p = 0.01). CONCLUSIONS: The majority of patients with ICH presented within 8 h of ictus. Cases of delayed diagnosis involved patients who had not incurred hemiplegia.

LINAC stereotactic radiosurgery for brain arteriovenous malformations: An updated single centre analysis of outcomes.

Arteriovenous malformations (AVM) of the brain are congenital, high pressure vascular malformations, which are at risk of haemorrhage. Stereotactic radiosurgery (SRS) can obliterate the nidus by delivering a precise high dose of ionising radiation in a single fraction. This paper updates long term AVM obliteration rates, time to obliteration and retreatment outcomes in LINAC delivered SRS treatment at the Royal Adelaide Hospital. A retrospective review of a prospectively maintained AVM SRS database supplemented by clinical case notes, patient correspondence and electronic medical records was performed. 89 AVMs received primary SRS treatment for which the crude obliteration rate was 61% (68% for 79 patients with adequate follow up). Higher marginal dose, smaller nidus size and lower Pollock-Flickinger (PF) score were significantly associated with AVM obliteration. The crude obliteration rates for patients with adequate follow-up and AVM diameter < 3 cm vs ≥ 3 cm were 76% vs 48%, respectively, and 93% with PF score < 1.0. Median time to obliteration was 36 months. Higher dose and lower PF score were associated with earlier obliteration. The crude obliteration rate after second SRS was 56% (9/16 patients) and no significant associations were found. These obliteration rates after primary and retreatment LINAC SRS are comparable to other studies. Marginal dose and PF score were the main predictors of obliteration overall as well as early (<36 months) obliteration.

Adult medulloblastoma in an Australian population.

Medulloblastoma in adult patients is a rare condition with limited contemporary demographic and treatment outcome data available in an Australian population. We conducted a retrospective review of patterns of care and outcomes of adult patients diagnosed with medulloblastoma treated at major neuro-oncology centres across Australia between January 2010 and December 2019. A total of 80 patients were identified and the median follow-up after diagnosis was 59.2 (range 0.5-204) months. A variety of chemotherapy regimens were used in the adjuvant and recurrent settings. The median overall survival (mOS) was 78 months (IQR 17.5-94.8). Patients who had no residual disease post-resection or with SHH-subtype tumours had a numerically longer 5-year survival rate than those with residual disease post resection or non-SHH subtypes respectively. The median time to recurrence from diagnosis was 18.4 months. The median OS from 1st relapse was 22.1 months (95% CI 11.7-31.4) and mOS from second relapse was 10.2 months (95% CI 6.6 - NR). This is the largest dataset examining patterns of care of adult patients with medulloblastoma in an Australian population. Substantial variation existed in the chemotherapy agents used in the adjuvant and recurrent setting. As has been demonstrated in a paediatric population, trials such as the upcoming EORTC 1634-BTG/NOA-23 trial (PersoMed-1 study) which are tailoring treatments to molecular profiles are likely to improve outcome in adult medulloblastoma.

The Predictive Accuracy of the Delayed Spot Sign for Haematoma Expansion in Spontaneous Supratentorial Intracerebral Haemorrhage: A Systematic Review and Meta-Analysis.

Previous studies have shown that spot signs on imaging modalities such as CT perfusion, delayed phase CTA or post contrast CT imaging reportedly have greater ability to predict haematoma expansion (HE) than the traditional CT angiography spot sign. We performed a systematic review and meta-analysis of the diagnostic accuracy of the spot sign on delayed imaging modalities in predicting haematoma expansion. Pubmed, Excerpta Medica Database, and the Cochrane library were searched on the 11 November 2019. The search strategy utilised the following terms: CT angiography OR post contrast CT OR CT perfusion OR CT AND intracerebral haemorrhage (or synonyms) AND spot sign OR delayed spot sign OR dynamic spot sign. The area under the summary of receiver operating curves for diagnostic accuracy of delayed spot sign in predicting HE was calculated using bivariate random effects meta-analysis. 501 articles were identified, with 10 meeting inclusion criteria. The studies included 711 patients overall, with 272 (38%) demonstrating a spot sign. The presence of a delayed spot sign was associated with HE with a diagnostic odds ratio of 25.4 (12.7-50.9). Pooled sensitivity was 0.81 (0.72-0.88), with a pooled specificity of 0.82 (0.76-0.88). Pooled positive likelihood ratio was 4.30, with a pooled negative likelihood ratio of 0.26. The area under the receiver operating curve (AUC) was 0.88. The delayed spot sign has greater diagnostic accuracy in predicting haematoma expansion than the traditional CT angiography spot sign. Further research could determine the delayed imaging technique that has the greatest diagnostic accuracy.

Progress in neurosurgery: Contributions of women neurosurgeons in the Middle East.

The Middle East is known for its complex history and rich environment and culture. The region is home to a wide variety of traditions, cultures and religions, which have made the area vulnerable to political conflicts. Despite these difficulties, science and medicine have always thrived in the region, with many medical practices and principles established by physicians and scholars living in the Middle East. The first academic neurosurgical activity in the region started in the 1950s. The first women neurosurgeons in the Middle East started training in the 1970s, and were from Iran, Palestine, followed by Saudi Arabia in the 1970s. These pioneers have encountered serious challenges, yet have become role models for the next generation. These women have paved the way and facilitated neurosurgical training and practice for more women surgeons. The gradual increase in the number of women neurosurgical residents in the region leads to the expectation that women will play a more prominent role in the future as leaders in neurosurgery in the Middle East. This collaborative study, which identifies the known women neurosurgeons in the Middle East for the first time, may serve to provide background and context for further contributions of women neurosurgeons for our profession and our patients.

Outcomes of surgical resection of sphenoid-orbital meningiomas with Sonopet ultrasonic aspirator.

Purpose: Surgical resection is the mainstay of treatment for spheno-orbital meningiomas. The Sonopet® is an ultrasonic aspirator device that provides several advantages over the traditional standard suction techniques and bone drill, including decreased collateral soft tissue damage, more precise bone removal and a clearer operative field. The purpose of the study was to examine the treatment outcomes of Sonopet®-assisted resection of spheno-orbital meningiomas.Methods: A retrospective chart review was conducted in seven patients with spheno-orbital meningioma in a single institution who underwent surgical resection with the Sonopet®. Pre-operative and post-operative data included the assessment of visual acuity, relative afferent pupillary defect (RAPD), Ishihara score, proptosis, fundus examination, computerised visual fields and the presence or absence of diplopia, headache, and other neuro-ophthalmic complications.Results: Nine Sonopet®-assisted procedures were performed on seven patients. Post-operatively, 89% of cases had stabilization or improvement of visual acuity and colour vision, whilst 29% had improved visual fields with the remainder being stable. Proptosis improved in all patients. Five of nine cases (44%) had new post-operative cranial nerve palsies, of which 75% were transient. One patient had tumour recurrence after 14 months, requiring further tumour resection and radiotherapy.Conclusion: Sonopet®-assisted resection of spheno-orbital meningiomas has comparable outcomes of visual improvement and complication rates to traditional resection techniques. Longer periods of post-operative observation and follow-up are recommended to observe long-term benefits.

Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.

The identification of high-grade glioma (HGG) progression may pose a diagnostic dilemma due to similar appearances of treatment-related changes (TRC) (e.g. pseudoprogression or radionecrosis). Deep learning (DL) may be able to assist with this task. MRI scans from consecutive patients with histologically confirmed HGG (grade 3 or 4) were reviewed. Scans for which recurrence or TRC was queried were followed up to determine whether the cases indicated recurrence/progression or TRC. Identified cases were randomly split into training and testing sets (80%/20%). Following development on the training set, classification experiments using convolutional neural networks (CNN) were then conducted using models based on each of diffusion weighted imaging (DWI - isotropic diffusion map), apparent diffusion coefficient (ADC), FLAIR and post-contrast T1 sequences. The sequence that achieved the highest accuracy on the test set was then used to develop DL models in which multiple sequences were combined. MRI scans from 55 patients were included in the study (70.1% progression/recurrence). 54.5% of the randomly allocated test set had progression/recurrence. Based upon DWI sequences the CNN achieved an accuracy of 0.73 (F1 score = 0.67). The model based on the DWI+FLAIR sequences in combination achieved an accuracy of 0.82 (F1 score = 0.86). The results of this study support similar studies that have shown that machine learning, in particular DL, may be useful in distinguishing progression/recurrence from TRC. Further studies examining the accuracy of DL models, including magnetic resonance perfusion (MRP) and magnetic resonance spectroscopy (MRS), with larger sample sizes may be beneficial.

Complex Spine Pathology Simulator: An Innovative Tool for Advanced Spine Surgery Training.

Background Technical advancements in spine surgery have made possible the treatment of increasingly complex pathologies with less morbidity. Time constraints in surgeons' training have made it necessary to develop new training models for spine pathology. Objective To describe the application of a novel compound, Stratathane resin ST-504 derived polymer (SRSDP), that can be injected at different spinal target locations to mimic spinal epidural, subdural extra-axial, and intra-axial pathologies for the use in advanced surgical training. Material and Methods Fresh-frozen thoracolumbar and cervical spine segments of human and sheep cadavers were used to study the model. SRSDP is initially liquid after mixing, allowing it to be injected into target areas where it expands and solidifies, mimicking the entire spectrum of spinal pathologies. Results Different polymer concentrations have been codified to vary adhesiveness, texture, spread capability, deformability, and radiologic visibility. Polymer injection was performed under fluoroscopic guidance through pathology-specific injection sites that avoided compromising the surgical approach for subsequent excision of the artificial lesion. Inflation of a balloon catheter of the desired size was used to displace stiff cadaveric neurovascular structures to mimic pathology-related mass effect. Conclusion The traditional cadaveric training models principally only allow surgeons to practice the surgical approach. The complex spine pathology simulator is a novel educational tool that in a user-friendly, low-cost fashion allows trainees to practice advanced technical skills in the removal of complex spine pathology, potentially shortening some of the aspects of the learning curve of operative skills that may otherwise take many years to acquire.

Venous sinus thrombosis secondary to tuberculous meningitis: a novel cause of trigeminal neuralgia.

A 33-year-old Vietnamese man with a history of superior sagittal sinus thrombosis secondary to tuberculous meningitis presented with severe recurrent trigeminal neuralgia. A complex compensatory cerebral venous drainage system developed, and a varix, in direct contact with the root entry zone of the trigeminal nerve, was the trigger for his pain. The patient's symptoms are presently controlled with a combination of medications for neuropathic pain, however he continues to experience frequent severe exacerbations and may very well require more invasive means to manage his condition in future.