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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]

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]