Explore the vast range of titles available.

Metabolic alterations in the critically ill have been studied for more than a century, but the heterogeneity of the critically ill patient population, the varying duration and severity of the acute phase of illness, and the many confounding factors have hindered progress in the field. These factors may explain why management of metabolic alterations and related conditions in critically ill patients has for many years been guided by recommendations based essentially on expert opinion. Over the last decade, a number of randomized controlled trials have been conducted, providing us with important population-level evidence that refutes several longstanding paradigms. However, between-patient variation means there is still substantial uncertainty when translating population-level evidence to individuals. A cornerstone of metabolic care is nutrition, for which there is a multifold of published guidelines that agree on many issues but disagree on others. Using a series of nine questions, we provide a review of the latest data in this field and a background to promote efforts to address the need for international consistency in recommendations related to the metabolic care of the critically ill patient. Our purpose is not to replace existing guidelines, but to comment on differences and add perspective.

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

[Display omitted] •Nutrigenetics and nutrigenomics study nutrition-gene interaction and genome-based patterns of taking bioactive food components regulating the internal OMICS (transcriptomics, proteomics, and metabolomics) environment.•Personalized health care diagnosis using nutrigenetics and nutrigenomics in conjunction with omics technologies can result in optimum nutritional therapy and better individual-centric care.•Using omics data, comprising transcriptomics, proteomics, and metabolomics, can give a personalized diagnosis for a preventive rather than reactive treatment approach. Nutrigenetics and nutrigenomics, combined with the omics technologies, are a demanding and an increasingly important field in personalizing nutrition-based care to understand an individual's response to nutrition-guided therapy. Omics is defined as the analysis of the large data sets of the biological system featuring transcriptomics, proteomics, and metabolomics and providing new insights into cell regulation. The effect of combining nutrigenetics and nutrigenomics with omics will give insight into molecular analysis, as human nutrition requirements vary per individual. Omics measures modest intraindividual variability and is critical to exploit these data for use in the development of precision nutrition. Omics, combined with nutrigenetics and nutrigenomics, is instrumental in the creation of goals for improving the accuracy of nutrition evaluations. Although dietary-based therapies are provided for various clinical conditions such as inborn errors of metabolism, limited advancement has been done to expand the omics data for a more mechanistic understanding of cellular networks dependent on nutrition-based expression and overall regulation of genes. The greatest challenge remains in the clinical sector to integrate the current data available, overcome the well-established limits of self-reported methods in research, and provide omics data, combined with nutrigenetics and nutrigenomics research, for each individual. Hence, the future seems promising if a design for personalized, nutrition-based diagnosis and care can be implemented practically in the health care sector.

Purpose Despite advances in personalizing the efficacy of cancer therapy, our ability to identify patients at risk of severe treatment side effects and provide individualized supportive care is limited. This is particularly the case for mucositis (oral and gastrointestinal), with no comprehensive risk evaluation strategies to identify high-risk patients. We, the Multinational Association for Supportive Care in Cancer/International Society for Oral Oncology (MASCC/ISOO) Mucositis Study Group, therefore aimed to systematically review current evidence on that factors that influence mucositis risk to provide a foundation upon which future risk prediction studies can be based. Methods We identified 11,018 papers from PubMed and Web of Science, with 197 records extracted for full review and 113 meeting final eligibility criteria. Data were then synthesized into tables to highlight the level of evidence for each risk predictor. Results The strongest level of evidence supported dosimetric parameters as key predictors of mucositis risk. Genetic variants in drug-metabolizing pathways, immune signaling, and cell injury/repair mechanisms were also identified to impact mucositis risk. Factors relating to the individual were variably linked to mucositis outcomes, although female sex and smoking status showed some association with mucositis risk. Conclusion Mucositis risk reflects the complex interplay between the host, tumor microenvironment, and treatment specifications, yet the large majority of studies rely on hypothesis-driven, single-candidate approaches. For significant advances in the provision of personalized supportive care, coordinated research efforts with robust multiplexed approaches are strongly advised.

ABSTRACT Wearable technology in the management of chronic diseases has emerged as a significant and growing concern in healthcare. These technologies, including smartwatches, fitness trackers, and other sensor‐based devices, offer continuous monitoring and real‐time data collection for individuals with chronic conditions. The data collected can include vital signs, activity levels, sleep patterns, and more, providing valuable insights into a patient's health. This trend is particularly relevant in the context of chronic diseases, such as diabetes, cardiovascular conditions, and respiratory disorders, where continuous monitoring is crucial for effective management. Wearable devices empower patients to actively participate in their healthcare by facilitating self‐monitoring and promoting healthy behaviors. Healthcare providers can also leverage the data generated by these devices to make informed decisions, personalize treatment plans, and intervene proactively. However, challenges exist, such as data security and privacy concerns, the accuracy of the collected information, and the need for effective integration into existing healthcare systems. Despite these challenges, the increasing adoption of wearable technology in chronic disease management reflects a promising avenue for improving patient outcomes and reducing healthcare costs through preventive and personalized care. Summary Wearable technology enables continuous monitoring of vital signs, activity levels, and other health metrics for individuals with chronic conditions, empowering patients to actively participate in their healthcare. This self‐monitoring promotes healthy behaviors and enhances patient engagement, contributing to better health outcomes and more personalized treatment approaches. Despite the advantages, incorporating wearable technology into clinical care is complex due to concerns over data privacy, device accuracy, and high costs. Additionally, effective integration requires addressing standardization issues in data collection and interpretation and maintaining patient trust by securing sensitive health information.

Transitional care may play a vital role in the sustainability of Europe’s future healthcare system, offering solutions for relocating patient care from hospital to home, therefore addressing the growing demand for medical care as the population is ageing. Re-hospitalization for the same initial pathological cause is a recurrent problem among older adults with chronic conditions such as heart failure or chronic obstructive pulmonary disease. However, to be effective, it is nowadays essential to integrate into the transitional care process innovative Information and Communications Technology technologies to ensure that patients with comorbidities experience a smooth and coordinated transition from hospitals or care centers to home, thereby reducing the risk of rehospitalization. In this paper, we present an overview of the integration of Internet of Things, artificial intelligence, and digital assistance technologies with traditional care pathways to address the challenges and needs of healthcare systems in Europe. We identify the current gaps in transitional care and define the technology mapping to enhance the care pathways, aiming to improve patient outcomes, safety, and quality of life, avoiding hospital readmissions. Finally, we define the trial setup and evaluation methodology needed to provide clinical evidence that supports the positive impact of technology integration on patient care and discuss the potential effects on the healthcare system.

The dementia landscape has evolved, with earlier diagnoses, improved prevention understanding (eg, modifiable factors), and new treatments. Emerging digital technologies (eg, wearables, smart home systems, and mobile apps) offer self‑management opportunities; yet, gaps persist regarding integration into the care needs and preferences of people with dementia. Broader gaps remain concerning intervention design; adaptation; and implementation, including effectiveness, study quality, and accessibility. This systematic review aims to synthesize and critically appraise existing literature on digital self-management technologies (wearables, smart home systems, and mobile apps) intended to reduce dementia-associated behaviors, enhance self-management, and improve quality of life (QoL). It evaluates intervention characteristics, effectiveness, accessibility, study design, and methodological quality according to international standards. A systematic search across 9 databases (PubMed, Scopus, ACM Digital Library, CINAHL, PsycInfo, Web of Science, IEEE Xplore, Embase, and MEDLINE) identified relevant English‑language studies published between January 1, 2013, and September 30, 2023. Search terms covered dementia, QoL, behavioral and self‑management strategies, and digital technologies. Eligible studies involved adults with dementia using wearable, smart home, or mobile technologies targeting QoL, behavior, and autonomy. Two reviewers independently appraised study design, hardware, and intervention purpose. Outcomes were mapped to the Nursing Outcomes Classification and benchmarked against National Institute for Health and Care Excellence quality standard 184. Accessibility was evaluated by availability, cost, usability, and context. Bias mitigation included a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)‑guided strategy and PROSPERO registration. Methodological quality and bias were assessed using the Mixed Methods Appraisal Tool, the Critical Appraisal Skills Program, and a bespoke characterization framework. Twenty-four studies evaluated interventions based on wearables, smart home systems, or apps for people with dementia and carers. Outcomes centered on neurocognition (24/24, 100%), self-care (17/24, 71%), and health behavior (13/24, 54%). Identified needs included managing distress (15/24, 62%) and supporting carers (15/24, 62%). Technologies included commercial tools (activity trackers, health-based wearables, and digital prompters) but were often inaccessible due to complex setup requirements and ongoing support needs. Substantial methodological heterogeneity precluded meta-analysis, necessitating narrative synthesis. Study quality was generally good to excellent, but samples were small, reporting incomplete, and outcomes unblinded. Only 1 (17%) of 6 randomized controlled trials reported effect sizes, indicating moderate decline in QoL at 24 months; effectiveness in other studies remains uncertain. Research on digital technologies for dementia self‑management shows benefits, particularly with off‑the‑shelf devices and mobile apps supporting person‑centered outcomes. Notable limitations include inadequate participant diversity (eg, atypical dementias and minoritized populations) and insufficient high‑quality research on QoL and behavioral outcomes, such as symptom management and self‑control. Future research must prioritize innovative solutions enhancing accessibility and usability, emphasizing simplified configuration, personalized adaptability, and effective training and support structures. PROSPERO CRD42023461841; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023461841.

Background: The International Consortium for Health Outcomes Measurement has published a set of patient-centered outcome measures for pregnancy and childbirth (PCB set), including patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs). To establish value-based pregnancy and childbirth care, the PCB set was implemented in the Netherlands, using the outcomes on the patient level for shared decision-making and on an aggregated level for quality improvement. Objective: This study aims to report first outcomes, experiences, and practice insights of implementing the PCB set in clinical practice. Methods: In total, 7 obstetric care networks across the Netherlands, each consisting of 1 or 2 hospitals and multiple community midwifery practices (ranging in number from 2 to 18), implemented the PROM and PREM domains of the PCB set as part of clinical routine. This observational study included all women participating in the clinical project. PROMs and PREMs were assessed with questionnaires at 5 time points: 2 during pregnancy and 3 post partum. Clinical threshold values (alerts) supported care professionals interpreting the answers, indicating possibly alarming outcomes per domain. Data collection took place from February 2020 to September 2021. Data analysis included missing (pattern) analysis, sum scores, alert rates, and sensitivity analysis. Results: In total, 1923 questionnaires were collected across the 5 time points: 816 (42.43%) at T1 (first trimester), 793 (41.23%) at T2 (early third trimester), 125 (6.5%) at T3 (maternity week), 170 (8.84%) at T4 (6 weeks post partum), and 19 (1%) at T5 (6 months post partum). Of these, 84% (1615/1923) were filled out completely. Missing items per domain ranged from 0% to 13%, with the highest missing rates for depression, pain with intercourse, and experience with pain relief at birth. No notable missing patterns were found. For the PROM domains, relatively high alert rates were found both in pregnancy and post partum for incontinence (469/1798, 26.08%), pain with intercourse (229/1005, 22.79%), breastfeeding self-efficacy (175/765, 22.88%), and mother-child bonding (122/288, 42.36%). Regarding the PREM domains, the highest alert rates were found for birth experience (37/170, 21.76%), shared decision-making (101/982, 10.29%), and discussing pain relief ante partum (310/793, 39.09%). Some domains showed very little clinical variation; for example, role of the mother and satisfaction with care. Conclusions: The PCB set is a useful tool to assess patient-reported outcomes and experiences that need to be addressed over the whole course of pregnancy and childbirth. Our results provide opportunities to improve and personalize perinatal care. Furthermore, we could propose several recommendations regarding methods and timeline of measurements based on our findings. This study supports the implementation of the PCB set in clinical practice, thereby advancing the transformation toward patient-centered, value-based health care for pregnancy and childbirth.