Multiple sclerosis and hypogonadism: is there a relationship?

INTRODUCTION: Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system and is widely recognized as a disease primarily affecting women. The relationship between MS and hypogonadism is complex and not fully understood, with recent evidence showing that hypogonadism may have a significant impact on the quality of life and disease progression of patients with MS. OBJECTIVES: This review aims to provide an overview of the current knowledge regarding the relationship between MS and hypogonadism, including the mechanisms underlying this relationship; the effects of hypogonadism on patients with MS; and the potential benefits and drawbacks of testosterone replacement therapy for patients with MS and hypogonadism. METHODS: This scientific review analyzed 19 articles that investigated the potential relationship among MS, testosterone levels, and hypogonadism. The articles were published between November 2008 and March 2022 and were identified through a comprehensive search of the PubMed database. The search terms used included "multiple sclerosis," "testosterone," "hypogonadism," and "MS and testosterone levels." RESULTS: Of the 19 articles reviewed, 11 described a positive correlation between low testosterone levels and dysfunction within the hypothalamic-pituitary-gonadal axis in individuals with MS. These findings suggest that low testosterone levels may contribute to dysfunction within the hypothalamus-pituitary-gonadal axis, which plays a crucial role in regulating testosterone production. The results also showed a relationship between sexual dysfunction and low testosterone levels, as well as a positive correlative relationship between these factors. CONCLUSION: The reviewed articles indicate a complex relationship among MS, testosterone levels, and the hypothalamic-pituitary-gonadal axis, with low testosterone levels potentially contributing to dysfunction in this axis and to sexual dysfunction. Further research is needed to better understand the effects of testosterone therapy on MS and sexual dysfunction in patients with MS.

Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.

OBJECTIVE: Surveillance algorithms that predict patient decompensation are increasingly integrated with clinical workflows to help identify patients at risk of in-hospital deterioration. This scoping review aimed to identify the design features of the information displays, the types of algorithm that drive the display, and the effect of these displays on process and patient outcomes. MATERIALS AND METHODS: The scoping review followed Arksey and O'Malley's framework. Five databases were searched with dates between January 1, 2009 and January 26, 2022. Inclusion criteria were: participants-clinicians in inpatient settings; concepts-intervention as deterioration information displays that leveraged automated AI algorithms; comparison as usual care or alternative displays; outcomes as clinical, workflow process, and usability outcomes; and context as simulated or real-world in-hospital settings in any country. Screening, full-text review, and data extraction were reviewed independently by 2 researchers in each step. Display categories were identified inductively through consensus. RESULTS: Of 14 575 articles, 64 were included in the review, describing 61 unique displays. Forty-one displays were designed for specific deteriorations (eg, sepsis), 24 provided simple alerts (ie, text-based prompts without relevant patient data), 48 leveraged well-accepted score-based algorithms, and 47 included nurses as the target users. Only 1 out of the 10 randomized controlled trials reported a significant effect on the primary outcome. CONCLUSIONS: Despite significant advancements in surveillance algorithms, most information displays continue to leverage well-understood, well-accepted score-based algorithms. Users' trust, algorithmic transparency, and workflow integration are significant hurdles to adopting new algorithms into effective decision support tools.