Search

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article discusses the opportunities, challenges, and recommendations for the use of artificial intelligence (AI) in surgical services in Australia and New Zealand. The implementation of AI must be evidence-based, safe, and ethically cautious, and should serve as an adjunct to surgical staff rather than a replacement. The article highlights the need for greater engagement with non-surgical healthcare communities and the wider society, as well as the importance of developing guidelines and infrastructure for monitoring and auditing AI tools. Ethical considerations, such as bias in training data and the lack of transparency in AI decision-making processes, must also be addressed. The article concludes with recommendations for the future use of AI in surgical services, emphasizing the need for an evidence-based approach, close engagement with regulatory bodies, and education for surgical staff and patients. [Extracted from the article]

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. [ABSTRACT FROM AUTHOR]

Regulation, particularly relating to intellectual property and commercial considerations are relatively unexplored, particularly when artificial intelligence algorithms are being implemented within public healthcare systems. Accordingly, a stepwise and rigorous process is required to demonstrate that artificial intelligence algorithms improve patient outcomes. As more artificial intelligence algorithms near clinical implementation, regulatory authorities and professional colleges and societies must carefully consider how these algorithms are evaluated. However, the regulation of the use of clinical artificial intelligence algorithms will be inherently related to evidenced generated through artificial intelligence clinical trials. [Extracted from the article]

Background Technical advancements in spine surgery havemade 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. [ABSTRACT FROM AUTHOR]

Abstract: With increasing application of endovascular therapies worldwide, the management of procedure-related complications has become increasingly important. Of particular interest is the surgical management of complications refractory to endovascular recanalization. Here, we present the unique surgical management of an inadvertent intracranial glue embolus following urgent glue embolization of a maxillofacial arteriovenous malformation. This is the first report to document management of this complication. An arteriotomy was performed and the glue, despite being adhered to the vessel intima, was retrieved in its entirety from the middle cerebral artery (MCA). Following the procedure, the patient developed a new thrombus at the previous glue site, likely related to endothelial injury. With post-operative heparin therapy, the patient recovered to baseline neurological state. This report demonstrates that microsurgical arteriotomy and glue embolectomy of the MCA is feasible, safe and may be useful in the management of complications of inadvertent intracranial glue embolization. [Copyright &y& Elsevier]

As a result of the increasingly popularity of vagal nerve stimulation (VNS) for intractable seizures, neurosurgeons not uncommonly encounter cases which require electrode revision. We examine our experience of VNS revision and reports the use of the ultra-sharp monopolar tip for safe dissection and removal of the electrode from the vagus nerve. A retrospective review was performed from January 2000 to Dec 2009 reviewed eight cases of VNS revision. The indications for VNS revision were device malfunction manifesting with increased seizures or increased impedance of the device and infection. The time from initial VNS implantation to revision ranged from 6 to 108 months (mean: 38 months). The entire VNS electrode system, was removed in seven cases and the helical coils were left in-situ in one case who did not derive any benefit from VNS and it was deemed unnecessary to subject the patient to the additional risk of vagal nerve injury. One case had dislodgement of the lower two coils and three cases had dense scarring to the vagus nerve causing high impedance and malfunction. The other three cases demonstrated no fibrotic scar tissue between the helical coils and the vagus nerve. Four cases had replacement of new VNS system but the case of infected VNS stimulator was not replaced as there was no benefit from the device. VNS revision is normally performed in cases of device malfunction or infection and can be safely performed using a combination of ultra-sharp monopolar coagulation and sharp dissection. [ABSTRACT FROM AUTHOR]

Paediatric facial traumas are often accompanied by other intracranial and non-head injuries. Use of Computed Tomography (CT) has increased diagnostic accuracy when compared to plain radiographs alone. Coexisting anomalies can sometimes be a chance finding from the imagings and this highlights the need of a formal reporting by a radiologist. We report a case of a traumatic unilateral condylar fracture with a coexisting life-threatening abnormality detected from careful assessment of the imagings. [ABSTRACT FROM AUTHOR]

Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract Purpose of the article Material and methods Results Conclusions 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.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.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).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. [ABSTRACT FROM AUTHOR]

Abstract Introduction Method Results Conclusions 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.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.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.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. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, decreased volumes of stroke admissions and mechanical thrombectomy were reported. The study's objective was to examine whether subarachnoid haemorrhage (SAH) hospitalisations and ruptured aneurysm coiling interventions demonstrated similar declines., Methods: We conducted a cross-sectional, retrospective, observational study across 6 continents, 37 countries and 140 comprehensive stroke centres. Patients with the diagnosis of SAH, aneurysmal SAH, ruptured aneurysm coiling interventions and COVID-19 were identified by prospective aneurysm databases or by International Classification of Diseases, 10th Revision, codes. The 3-month cumulative volume, monthly volumes for SAH hospitalisations and ruptured aneurysm coiling procedures were compared for the period before (1 year and immediately before) and during the pandemic, defined as 1 March-31 May 2020. The prior 1-year control period (1 March-31 May 2019) was obtained to account for seasonal variation., Findings: There was a significant decline in SAH hospitalisations, with 2044 admissions in the 3 months immediately before and 1585 admissions during the pandemic, representing a relative decline of 22.5% (95% CI -24.3% to -20.7%, p<0.0001). Embolisation of ruptured aneurysms declined with 1170-1035 procedures, respectively, representing an 11.5% (95%CI -13.5% to -9.8%, p=0.002) relative drop. Subgroup analysis was noted for aneurysmal SAH hospitalisation decline from 834 to 626 hospitalisations, a 24.9% relative decline (95% CI -28.0% to -22.1%, p<0.0001). A relative increase in ruptured aneurysm coiling was noted in low coiling volume hospitals of 41.1% (95% CI 32.3% to 50.6%, p=0.008) despite a decrease in SAH admissions in this tertile., Interpretation: There was a relative decrease in the volume of SAH hospitalisations, aneurysmal SAH hospitalisations and ruptured aneurysm embolisations during the COVID-19 pandemic. These findings in SAH are consistent with a decrease in other emergencies, such as stroke and myocardial infarction., Competing Interests: Competing interests: TNN: PI CLEAR study (Medtronic). DCH: Stryker, Vesalio, Cerenovus consultant. AEH: consultant and speaker for Medtronic, Stryker, Microvention, Penumbra, Balt, Scientia, Genentech and GE Healthcare. PJ: Medtronic, Microvention, Balt, Cerenovus consultant. SO-G: Medtronic, Stryker consultant. DSL: Cerenovus, Genentech, Stryker, Medtronic consultant. TGJ: advisor/investor for Anaconda, Route92, FreeOx, and Blockade Medical; Medtronic grants, DAWN, AURORA PI (Stryker). WJM: consultant: Rebound Therapeutics, Viseon Imperative Care, Q’Apel, Stryker, Stream Biomedical, Spartan Micro; Investor: Cerebrotech, Endostream, Q’Apel, Viseon, Rebound, and Spartan Micro. RGN: Stryker; Cerenovus/Neuravi; Anaconda, Cerebrotech, Ceretrieve, Vesalio (Advisory Board); Imperative Care., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)