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Purpose Frailty is an important predictor of adverse health events in older people, and improving quality of life (QOL) is increasingly recognised as a focus for services in this population. This systematic review synthesised evidence of the relationship between frailty and QOL in community-dwelling older people, with an emphasis on how this relationship varied across QOL domains. Methods We conducted a systematic review with meta-analysis. We searched five databases for reports of QOL in older people with frailty and included studies based on pre-defined criteria. We conducted meta-analyses comparing “frail” and “not frail” groups for each QOL scale where data were available. We compared pooled results to distribution-based and known-group differences to enhance interpretation. We summarised reported cross-sectional and longitudinal analyses. Results Twenty-two studies (24,419 participants) were included. There were medium or larger standardised mean differences for 24 of 31 QOL scales between frail and not frail groups, with worse QOL for frail groups. These scales encompassed constructs of health-related quality of life as well as psychological and subjective well-being. There were similar findings from mean difference meta-analyses and within-study analyses. Conclusions The association between frailty and lower QOL across a range of constructs is clear and often substantial. Future research should establish whether causal mechanisms link the constructs, which aspects of QOL are most important to older people with frailty, and investigate their tractability. Services focused on measuring and improving QOL for older people with frailty should be introduced.

Management of psychiatric disorders in high-risk cardiac patients often requires difficult decision making when it comes to acceptable medication side effects. We present the case report of a 28-year-old female with a history of generalized anxiety disorder (GAD), major depressive disorder (MDD), intravenous heroin use disorder, and prior tricuspid valve replacement who presented to the hospital with signs and symptoms of sepsis. She was found to have corrected QT interval (QTc) prolongation and infective endocarditis with blood cultures positive for . Due to QTc prolongation, her home medication of citalopram was discontinued in favor of escitalopram. Within 24 hours of administration, the patient experienced angioedema with periorbital swelling, lip swelling, and urticaria of the face and arms which was resolved with intravenous (IV) diphenhydramine. In this case report, we present what we believe to be the first recorded case of escitalopram-induced angioedema in a patient without a past medical history of hereditary angioedema (HAE), and how pharmacogenomic testing influenced antidepressant medication selection.

Artificial Intelligence (AI) is rapidly transforming healthcare, but also raising concerns about algorithmic biases that mostly stem from the training data. It is widely supported that transparent dataset documentation is key to enabling responsible AI development. Several standardized dataset documentation approaches have been established, such as Datasheet, Dataset Nutrition Label, Accountability Documentation, Healthsheet, and Data Card. However, their suitability and usage for health datasets remain unclear. In this Analysis, we compared all five approaches and evaluated their alignment with the STANDING Together Recommendations for Documentation of Health Datasets. We also investigated their real-world usage and gathered insights from generators and consumers of health datasets. Our findings reveal that none of these documentation approaches are used widely or fully suited for health datasets. We recommend developing a standard documentation approach for health datasets along with clear guidelines and automation tools to support adoption.

Here, we introduce Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI), a multidisciplinary data-generation project designed to create and share a multimodal dataset optimized for artificial intelligence research in type 2 diabetes mellitus.