Purpose: Cerebral vasospasm following aneurysmal subarachnoid hemorrhage (aSAH) is a significant complication associated with poor neurological outcomes. We present a novel, semi-automated pipeline, implemented in the open-source medical imaging analysis software ITK-SNAP, to segment subarachnoid blood volume from initial CT head (CTH) scans and use this to predict future radiological vasospasm. Methods: 42 patients were admitted between February 2020 and December 2021 to our tertiary neurosciences center, and whose initial referral CTH scan was used for this retrospective cohort study. Blood load was segmented using a semi-automated random forest classifier and active contour evolution implemented in ITK-SNAP. Clinical data were extracted from electronic healthcare records in order to fit models aimed at predicting radiological vasospasm risk. Results: Semi-automated segmentations demonstrated excellent agreement with manual, expert-derived volumes (mean Dice coefficient = 0.92). Total normalized blood volume, extracted from CTH images at first presentation, was significantly associated with greater odds of later radiological vasospasm, increasing by approximately 7% for each additional cm³ of blood (OR = 1.069, 95% CI: 1.021–1.120; p < .005). Greater blood volume was also significantly associated with vasospasm of a higher Lindegaard ratio, of longer duration, and a greater number of discrete episodes. Total blood volume predicted radiological vasospasm with a greater accuracy as compared to the modified Fisher scale (AUC = 0.86 vs 0.70), and was of independent predictive value. Conclusion: Semi-automated methods provide a plausible pipeline for the segmentation of blood from CT head images in aSAH, and total blood volume is a robust, extendable predictor of radiological vasospasm, outperforming the modified Fisher scale. Greater subarachnoid blood volume significantly increases the odds of subsequent vasospasm, its time course and its severity.

Objective: To identify cognitive biases and heuristics experienced by surgeons in operative settings and the impact these biases and heuristics have on patient care. Background: Cognitive biases and heuristics are systematic errors in thinking that can affect clinical decisions. Both are noted in surgical settings and are a risk to patient safety. Methods: This review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and PROSPERO registered (CRD42023432099). Five major databases were searched from inception to August 28, 2022, with an updated search on January 27, 2024. Original primary research studies in English were included, with relevant risk of bias tools employed for each study. Results: Twenty-one papers were included. Thirty-eight biases were identified across 6 experiments, 5 analyses, and 10 survey studies. Confirmation bias, anchoring, risk aversion, and overconfidence bias were the most represented. Risk of bias was moderate across most studies. Cognitive biases and heuristics were found to influence surgical outcomes and 6 studies cited a negative impact on patient care, with one associating biases with fatal outcomes. Conclusions: Biases and heuristics contribute to surgical errors and never events, and will continue to do so until they are recognised and addressed. Implementing debiasing strategies, such as mindfulness training and deliberate reflection, was found to reduce surgical errors in 2 studies. This review highlights the need for experimental studies, which are essential for understanding how and why biases lead to negative outcomes and for evaluating further debiasing interventions. We propose directions for future research and system changes.

The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.

After craniectomy, patients are generally advised to wear a helmet when mobilising to protect the unshielded brain from damage. However, there exists limited guidance regarding head protection for patients at rest and when being transferred or turned. Here, we emphasise the need for such protocols and utilise evidence from several sources to affirm our viewpoint. A literature search was first performed using MEDLINE and EMBASE, looking for published material relating to head protection for patients post-craniectomy during rest, transfer or turning. No articles were identified using a wide-ranging search strategy. Next, we surveyed and interviewed staff and patients from our neurosurgical centre to ascertain how often their craniectomy site was exposed to external pressure and the precautions taken to prevent this. 59% of patients admitted resting in contact with the craniectomy site, in agreement with the observations of 67% of staff. In 63% of these patients, this occurred on a daily basis and for some, was associated with symptoms suggestive of raised intracranial pressure. 44% of staff did not use a method to prevent craniectomy site contact while 65% utilised no additional precautions during transfer or turning. 63% of patients received no information about avoiding craniectomy site contact upon discharge, and almost all surveyed wished for resting head protection if it were available. We argue that pragmatic guidelines are needed and that our results support this perspective. As such, we offer a simple, practical protocol which can be adopted and iteratively improved as further evidence becomes available in this area.