Long-term Implications of Tracheostomy in Cardiac Surgery Patients: Decannulation and Mortality

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The mutational landscape of melanoma brain metastases presenting as the first visceral site of recurrence

Brain metastases are a major cause of melanoma-related mortality and morbidity. We undertook whole-exome sequencing of 50 tumours from patients undergoing surgical resection of brain metastases presenting as the first site of visceral disease spread and validated our findings in an independent dataset of 18 patients. Brain metastases had a similar driver mutational landscape to cutaneous melanomas in TCGA. However, KRAS was the most significantly enriched driver gene, with 4/50 (8%) of brain metastases harbouring non-synonymous mutations. Hotspot KRAS mutations were mutually exclusive from BRAF^V600, NRAS and HRAS mutations and were associated with a reduced overall survival from the resection of brain metastases (HR 10.01, p = 0.001). Mutations in KRAS were clonal and concordant with extracranial disease, suggesting that these mutations are likely present within the primary. Our analyses suggest that KRAS mutations could help identify patients with primary melanoma at higher risk of brain metastases who may benefit from more intensive, protracted surveillance.Subject terms: CNS cancer, Metastasis, Melanoma, Tumour biomarkers, Cancer     

Anatomic Snuffbox (Distal Radial Artery) and Radial Artery Access for Treatment of Intracranial Aneurysms with FDA-Approved Flow Diverters

BACKGROUND AND PURPOSE:Transradial access for neurointerventional procedures has been proved a safer and more comfortable alternative to femoral artery access. We present our experience with transradial (distal radial/anatomic snuffbox and radial artery) access for treatment of intracranial aneurysms using all 3 FDA-approved flow diverters.MATERIALS AND METHODS:This was a high-volume, dual-center, retrospective analysis of each institution’s data base between June 2018 and June 2020 and a collection of all patients treated with flow diversion via transradial access. Patient demographic information and procedural and radiographic data were obtained.RESULTS:Seventy-four patients were identified (64 female patients) with a mean age of 57.5 years with a total of 86 aneurysms. Most aneurysms were located in the anterior circulation (93%) and within the intracranial ICA (67.4%). The mean aneurysm size was 5.5 mm. Flow diverters placed included the Pipeline Embolization Device (Flex) (PED, n = 65), the Surpass Streamline Flow Diverter (n = 8), and the Flow-Redirection Endoluminal Device (FRED, n = 1). Transradial access was successful in all cases, but femoral crossover was required in 3 cases (4.1%) due to tortuous anatomy and inadequate support of the catheters in 2 cases and an inability to navigate to the target vessel in a patient with an aberrant right subclavian artery. All 71 other interventions were successfully performed via the transradial approach (95.9%). No access site complications were encountered. Asymptomatic radial artery occlusion was encountered in 1 case (3.7%).CONCLUSIONS:Flow diverters can be successfully placed via the transradial approach with high technical success, low access site complications, and a low femoral crossover rate.

The transradial access (TRA), including distal radial artery (dRA) access in the anatomic snuffbox and radial artery (RA) access at the palmar surface of the wrist, is being increasingly used as primary vascular access for neurointerventional procedures. In prior years, large randomized trials in the field of interventional cardiology and more recent articles in neurointerventional surgery have shown higher patient preference for the TRA, cost reduction, as well as lower morbidity and mortality compared with the traditional transfemoral access (TFA).^1-11 Reduction in access site complications has been a particular advantage of wrist over femoral access and is an important consideration for vascular access choice in the treatment of intracranial aneurysms using flow diversion. Patients undergoing flow diversion are required to take dual-antiplatelet agents and receive heparin during the procedure, all of which increase the risk of bleeding from the access site.¹² Also, flow diverters (FDs) may require large-bore catheter assemblies for delivery and deployment, which may increase the risk of radial artery occlusion, access site bleeding, or vascular injury.^13,14To date, only a limited number of case reports and case series have described the safety and feasibility of TRA for the treatment of intracranial aneurysms using flow diverters.^15-22Recently, a large, retrospective multicenter study reported the safety of TRA for flow diversion, showing a lower access site (P = .039) and overall complication rate (P = .035).¹² This study, however, did not cover catheter systems, patient functional outcome, and aneurysm occlusion. Here, we report our experience with TRA (dRA [anatomic snuffbox] and RA) for the treatment of intracranial aneurysms using all 3 FDA-approved flow diverters, including technical feasibility, procedural safety, patient outcome, and aneurysm occlusion on follow-up. Additionally, we reviewed the current literature on use of flow diverters via TRA.     

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports

BACKGROUND AND PURPOSE:The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports.MATERIALS AND METHODS:We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset.RESULTS:The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (−5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017–2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017–2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data.CONCLUSIONS:Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.

There is much concern regarding the impact of the coronavirus disease 2019 (COVID-19) pandemic on the quality of stroke care, including issues with hospital capacity, clinical resource re-allocation, and the safety of patients and clinicians.^1,2 Previous reports have shown that there have been substantial decreases in stroke neuroimaging volume during the pandemic.^3,4 In addition, acute ischemic infarcts have been found on neuroimaging studies in many hospitalized patients with COVID-19, though the causal relationship is unclear.^5,6 Studies like these and other epidemiologic analyses usually rely on the creation of manually curated databases, in which identification of cases can be time-consuming and difficult to update in real-time. One way to facilitate such research is to use natural language processing (NLP), which has shown utility for automated analysis of radiology report data.⁷ NLP algorithms have been developed previously for the classification of neuroradiology reports for the presence of ischemic stroke findings and acute ischemic stroke subtypes.^8,9 Thus, NLP has the potential to facilitate COVID-19 research.In this study, we developed an NLP machine learning model that classifies radiology reports for the presence or absence of acute or subacute ischemic stroke (ASIS), as opposed to chronic stroke. We used this model to quantify the change in ASIS detected on all CT or MR imaging studies performed at a large comprehensive stroke center during the COVID-19 pandemic in the United States. We also evaluated NLP model generalizability and different training strategies using a sample of radiology reports from a second stroke center.     

Mind the gap: Teachers’ conceptions of student-staff partnership and its potential to enhance educational quality

Abstract

Introduction: Student-staff partnerships as a concept to improve medical education have received a growing amount of attention. Such partnerships are collaborations in which students and teachers seek to improve education by each adding their unique contribution to decision-making and implementation processes. Although previous research has demonstrated that students are favourable to this concept, teachers remain hesitant. The present study investigated teachers’ conceptions of student-staff partnerships and of the prerequisites that are necessary to render such partnerships successful and enhance educational quality.

Method: We conducted semi-structured interviews with 14 course coordinators who lead course design teams and also teach in 4 bachelor health programmes, using Bovill and Bulley’s levels of student participation as sensitising concepts during data analysis.

Results: The results pointed to three different conceptions of student-staff partnerships existing among teachers: Teachers teach and students study; teachers teach and value students’ feedback; and teachers and students co-create. The prerequisites for effective co-creation teachers identified were: Teachers must be open to involve students and create dialogues; students must be motivated and have good communication skills; the organisation must be supportive; and teachers should have the final say.

Conclusion: We conclude that teachers’ conceptions are consistent with Bovill and Bulley’s levels of student participation. Under certain conditions, teachers are willing to co-create and reach the highest levels of student participation.     

Twelve tips for developing and implementing a medical education Twitter chat

Abstract

Live discussions on the social media site Twitter or Twitter chats are gaining popularity as powerful tools for engaging a broad audience in an interactive discussion. Medical education, in particular, is experiencing an increase in the use of this modality to support informal learning, as a means to encourage collaboration and share best practices, and as a platform for large-scale mentorship. Despite this growth in popularity, there are limited data to guide medical educators on the fundamentals of organizing a Twitter chat. In this Twelve Tips article, we discuss strategies relevant to potential Twitter chat organizers. We have arranged the tips chronologically, beginning with a discussion of initial considerations when planning and formulating a chat topic and publicizing the chat to potentially interested people and groups, followed by practical considerations while hosting the chat, and finally strategies for evaluating and extending a Twitter chat’s impact.     

Longitudinal blood pressure patterns and cardiovascular disease risk

Abstract

Observational and interventional studies have unequivocally demonstrated that “present”, i.e. single-occasion, blood pressure is one of the key determinants of cardiovascular disease risk. Over the past two decades, however, numerous publications have suggested that longitudinal blood pressure data and assessment of long-term blood pressure exposure provide incremental prognostic value over present blood pressure. These studies have used several different indices to quantify the overall exposure to blood pressure, such as time-averaged blood pressure, cumulative blood pressure, blood pressure trajectory patterns, and age of hypertension onset. This review summarises existing research on the association between these indices and hard cardiovascular outcomes, outlines the strengths and weaknesses of these indices, and provides an overview of how longitudinal blood pressure changes can be measured and used to improve cardiovascular disease risk prediction.

• KEY MESSAGES

• Numerous recent publications have examined the relation between cardiovascular disease and long-term blood pressure (BP) exposure, quantified using indices such as time-averaged BP, cumulative BP, BP trajectory patterns, and age of hypertension onset.

• This review summarises existing research on the association between these indices and hard cardiovascular outcomes, outlines the strengths and weaknesses of these indices, and provides an overview of how longitudinal BP changes can be measured and used to improve cardiovascular disease risk prediction.

• Although longitudinal BP indices seem to predict cardiovascular outcomes better than present BP, there are considerable differences in the clinical feasibility of these indices along with a limited number of prospective data.

     

Humans and machines: Moving towards a more symbiotic approach to learning clinical reasoning

Abstract

Artificial intelligence is a growing phenomenon that is driving major changes to how we deliver healthcare. One of its most significant and challenging contributions is likely to be in diagnosis. Artificial intelligence is challenging the physician’s exclusive role in diagnosis and in some areas, its diagnostic accuracy exceeds that of humans. We argue that we urgently need to consider how we will incorporate AI into our teaching of clinical reasoning in the undergraduate curriculum; students need to successfully navigate the benefits and potential issues of new and developing approaches to AI in clinical diagnosis. We offer a pedagogical framework for this challenging change to our curriculum.