Innominate artery occlusion: a case study

Aim of the study

The aim of this case report is to evaluate carotid duplex and hemodynamic patterns in an asymptomatic male patient with innominate artery occlusion. Innominate artery occlusion is a rare clinical entity that can lead to a range of cerebrovascular symptoms, including arm claudication, subclavian steal syndrome, and stroke. The case report emphasizes key learning points in diagnosing innominate artery occlusion using imaging and physiological methods.

Case description

A 64-year-old asymptomatic male patient with a history of carotid bruit, hypertension, coronary artery bypass grafting, aortic aneurysm, hyperlipidemia, mild aortic stenosis, long-term tobacco use, and a body mass index of 24 was referred for a carotid ultrasound.

Conclusions

Innominate artery occlusion is a rare condition requiring a comprehensive assessment of collateralization before any intervention is attempted. Considering waveform features such as transient end-diastolic flow reversal and tardus parvus, along with brachial pressures and transcranial Doppler, can assist in evaluating the extent of disease.

A Systematic Review and Quality Assessment of Breast Cancer Clinical Practice Guidelines Providing Complementary and Alternative Medicine Recommendations

PURPOSE OF REVIEW

Up to 80% of patients with breast cancer are reported to use complementary and alternative medicine (CAM). Despite this high prevalence, many healthcare providers have little knowledge and education surrounding this topic and may be inadequately prepared to discuss such therapies with their patients. Given this knowledge gap, the purpose of this study was to systematically identify the quantity and assess the quality of CAM recommendations in clinical practice guidelines (CPGs) for the treatment and/or management of breast cancer.

RECENT FINDINGS

Thirty-four CPGs were deemed eligible, 5 of which mentioned CAM, and 4 of which made CAM recommendations. Eligible CPGs containing CAM recommendations were assessed with the AGREE II instrument. Scaled domain percentages from highest to lowest were (% overall, % CAM) as follows: scope and purpose (100.0%, 100.0%), editorial independence (100.0%, 100.0%), clarity of presentation (97.2%, 80.6%), rigour of development (80.2%, 80.2%), stakeholder involvement (88.9%, 77.8%), and applicability (58.3%, 58.3%). CPGs with favourable scores may provide practitioners with guidance on safe and effective use of CAM therapies. A need exists to improve the quality of CAM recommendations in CPGs.

Virtual reality as a learning tool in spinal anatomy and surgical techniques

Background

Surgical simulation is a valuable educational tool for trainees to practice in a safe, standardized, and controlled environment. Interactive feedback-based virtual reality (VR) has recently moved to the forefront of spine surgery training, with most commercial products focusing on instrumentation. There is a paucity of learning tools directed at decompression principles. The purpose of this study was to evaluate the efficacy of VR simulation and its educational role in learning spinal anatomy and decompressive techniques.

Methods

A VR simulation module was created with custom-developed software. Orthopaedic and neurosurgical trainees were prospectively enrolled and interacted with patient-specific 3D models of lumbar spinal stenosis while wearing a headset. A surgical toolkit allowed users to perform surgical decompression, specifically removing soft tissues and bone. The module allowed users to perform various techniques in posterior decompressions and comprehend anatomic areas of stenosis. Pre- and post-module testing, and utility questionnaires were administered to provide both quantitative and qualitative evaluation of the module as a learning device.

Results

28 trainees were enrolled (20-orthopaedic, 8-neurosurgery) in the study. Pre-test scores on anatomic knowledge progressively improved and showed strong positive correlation with year-in-training (Pearson's r = 0.79). Following simulation, the average improvement in post-test scores was 11.4% in junior trainees (PGYI-III), and 1.0% in senior trainees (PGYIII-Fellows). Knowledge improvement approached statistical significance amongst junior trainees (p = 0.0542). 89% of participants found the VR module useful in understanding and learning the pathology of spinal stenosis. 71% found it useful in comprehending decompressive techniques. 96% believed it had utility in preoperative planning with patient-specific models.

Conclusions

Our original VR spinal decompression simulation has shown to be overwhelmingly positively received amongst trainees as both a learning module of patho-anatomy and patient-specific preoperative planning, with particular benefit for junior trainees.

Diagnosis of Obstructive Sleep Apnea during Wakefulness Using Upper Airway Negative Pressure and Machine Learning

Background and Rational: Obstructive Sleep Apnea (OSA) is a common disorder, affecting almost 10% of adults, but very underdiagnosed. This is largely due to limited access to overnight sleep testing using polysomnography (PSG). Our goal was to distinguish OSA from healthy individual using a simple maneuver during wakefulness in combination with machine learning methods. Methods: Participants have undergone an overnight PSG to determine their ground truth OSA severity. Separately, they were asked to breathe through a nasal mask or a mouth piece through which negative pressure (NP) was applied, during wakefulness. Airflow waveforms were acquired and several features were extracted and used to train various classifiers to predict OSA. Results and Discussion: The performance of each classifier and experimental setup was calculated. The best results were obtained using Random Forest classifier for distinguishing OSA from healthy individuals with a very good area under the curve of 0.80. To the best of our knowledge, this is the first study to deploy machine learning and NP with promising path to diagnose OSA during wakefulness.