Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is particularly impactful for pregnant women who experience increased risk of severe disease upon infection. 1,2 This may be due to physiologic changes in pregnancy including reduced lung capacity, increased metabolic and cardiovascular demands, and immune-mediated changes 3,4 that can predispose patients

Objectives Our aim was to validate the online Brain Health Assessment (BHA) for detection of amnestic mild cognitive impairment (aMCI) compared to gold-standard neuropsychological assessment. We compared the diagnostic accuracy of the BHA to the Montreal Cognitive Assessment (MoCA). Methods Using a cross-sectional design, community-dwelling older adults completed a neuropsychological assessment, were diagnosed as normal cognition (NC) or aMCI, and completed the BHA and MoCA. Both logistic regression (LR) and penalized logistic regression (PLR) analyses determined BHA and demographic variables predicting aMCI; MoCA variables were similarly modeled. Diagnostic accuracy was compared using area under the receiver operating characteristic curve (ROC AUC) analyses. Results Ninety-one participants met inclusion criteria (51 aMCI, 40 NC). PLR modeling for the BHA indicated Face–Name Association, Spatial Working Memory, and age-predicted aMCI (ROC AUC = 0.76; 95% confidence interval [CI]: 0.66–0.86). Optimal cut-points resulted in 21% classified as aMCI (positive), 23% negative, and 56% inconclusive. For the MoCA, digits, abstraction, delayed recall, orientation, and age predicted aMCI (ROC AUC = 0.71; 95% CI: 0.61–0.82). Optimal cut-points resulted in 22% classified positive, 8% negative, and 70% inconclusive (LR results presented within). The BHA model classified fewer participants into the inconclusive category and more as negative for aMCI, compared to the MoCA model (Stuart–Maxwell p = .004). Discussion The self-administered BHA provides similar detection of aMCI as a clinician-administered screener (MoCA), with fewer participants classified inconclusively. The BHA has the potential to save practitioners time and decrease unnecessary referrals for a comprehensive assessment to determine the presence of aMCI.

Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

Background and Objectives Despite the well-recognized difficulty that persons with dementia and family carers experience in the decision making and transition to nondriving, there are few interventions and resources to support them. As part of our ongoing research to develop a driving cessation toolkit that addresses this gap, we sought to examine the context-specific factors relevant to its effective implementation in settings that support older adults with dementia. Research Design and Methods A qualitative descriptive approach was used to explore the perspectives of Alzheimer Society (AS) staff in their work of supporting people with dementia and family carers within the context of driving cessation. Individual in-depth interviews were conducted with 15 AS staff members in 4 Canadian provinces. Data were examined using interpretative thematic analysis. Results The study results revealed an overarching paradox that despite the importance of driving cessation in people with dementia, it continues to be largely avoided at the individual and system levels. This is explored via the themes of (a) paradox of importance and avoidance identified in AS settings; (b) lack of awareness and understanding about dementia and driving among people with dementia and family carers; (c) distress and avoidance rooted in ongoing system issues; and (d) moving driving cessation to the “front burner.” Discussion and Implications Viewed through the emerging social health paradigm, which focuses on the social and emotional consequences of dementia, our results highlight the urgent need to mobilize our communities, medical education systems, and transportation authorities to finally resolve the dementia and driving cessation paradox.

Background Our need for easily administered online assessments sensitive to mild cognitive difficulties is increasing as our population ages. Our team has recently presented data indicating the accuracy of an online, publicly available, self‐administered screening measure, Cogniciti’s Brain Health Assessment (BHA), in the detection of amnestic mild cognitive impairment (aMCI) in a sample of community dwelling older adults. This current work extends those findings by further examining diagnostic accuracy of this measure. Method Using a cross‐sectional design, community‐dwelling older adults aged 60‐89 completed a gold standard neuropsychological assessment to determine a diagnosis of normal cognition (NC) or aMCI (by consensus of 3 staff neuropsychologists). Each participant also completed the BHA. Penalized logistic regression (PLR) analyses were used to examine which specific BHA tasks and measured demographic variables contributed to this test’s predictive utility in detecting aMCI. Diagnostic accuracy of the PLR model was compared with a logistic regression (LR) model examining BHA total score accuracy. Result 91 participants met inclusion criteria (51 aMCI, 40 NC). PLR modelling for the BHA indicated that of the tasks and variables measured by the BHA, the Face‐Name Association and Spatial Working Memory tasks predicted aMCI, with age also accounted for in the model (ROC‐AUC = 0.76; 95%CI: 0.66, 0.86). Based on this model, optimal performance cut‐points were determined, which resulted in 21% of the sample being classified as aMCI (positive), 23% as negative, and 56% as inconclusive. Projected general population classification rates are also presented to provide an estimate of how the BHA may perform in the broader population. Conclusion Results support that validity of the BHA as a screening measure for aMCI, and provide indication of the tasks within this measure that contribute to its utility in screening for this specific type of cognitive decline. Given the BHA is an online, self‐administered task, this measure has the potential to not only decrease unnecessary referrals for comprehensive assessment to determine presence of aMCI, but also to save practitioners time over commonly used paper and pencil screeners.

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Dr. Robert Arntfield is a critical care intensivist and traumatologist at London Health Sciences Center where he also acts as the medical director of the Critical Care Trauma Unit. Originally interested in emergency medicine, he then carved his pathway to enter the realm of critical care. Dr. Arntfield is a world-renowned expert in critical care ultrasonography and lectures globally on the topic. He is currently working in collaboration with multiple artificial intelligence and technology companies to advance the applications of Point-of-Care Ultrasound (POCUS). We had the opportunity to talk to Dr. Arntfield about the field of critical care medicine at LHSC, in Canadian healthcare, and the significance of the POCUS within the field.

Dr. Sanatani is a medical oncologist at the London Regional Cancer Program at Victoria Hospital. Originally interested in general medicine, he then changed routes to medical oncology. We had the opportunity to talk to Dr. Sanatani about the field of medical oncology at LHSC, in Canadian healthcare, and the significance of the patient-physician relationship within the field.