Intradialytic hypotension (IDH) is a critical complication in patients with chronic kidney disease undergoing dialysis, affecting both patient safety and treatment efficacy. This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. Given the critical nature of IDH, which can lead to significant complications during dialysis, the development of effective predictive tools is vital for improving patient safety and outcomes. Dialysis session data from 758 patients collected between January 2016 and October 2019 were analyzed. Particle Swarm Optimization, Grey Wolf Optimizer, Pendulum Search Algorithm, and Whale Optimization Algorithm were employed to reduce the feature space, removing approximately 45% of clinical and analytical variables while maintaining high recall for the minority class of patients experiencing hypotension. Among the evaluated models, the XGBoost classifier showed superior performance, achieving a macro F-score of 0.745 with a recall of 0.756 and a precision of 0.718. These results highlight the effectiveness of the combined approach for early identification of patients at risk for IDH, minimizing false negatives, and improving clinical decision-making in nephrology.

Background and Objectives: Fibromyalgia (FM) is a chronic and complex condition characterized by widespread pain, fatigue, psychological burden, and cognitive impairment, posing significant challenges for treatment. Immersive virtual reality exercise (iVRE) has been proposed as an innovative therapeutic approach to increase adherence, motivation, and multidimensional benefits, but evidence in FM remains limited. This study aimed to evaluate the effects of a six-week iVRE program on cortisol levels, quality of life, cognitive function, and psychological symptoms in women with FM. Materials and Methods: A quasi-experimental pre–post design was conducted with 21 women (mean age 48.1 ± 10.7 years) diagnosed with FM, who completed twelve 30 min sessions of iVRE using Oculus Quest 2™ and the FitXR platform. Outcomes assessed pre- and post-intervention included salivary cortisol (ELISA), quality of life (FIQR), emotional status (DASS-21), and cognitive function (MoCA). Adherence and safety were monitored throughout. Results: The intervention was well tolerated, with no adverse events and 100% adherence. Statistically significant improvements were observed in FIQR scores (p < 0.001, d = 3.54), depression (p < 0.001, d = 1.19), anxiety (p < 0.001, d = 1.39), and stress (p < 0.001, d = 2.28). Cognitive performance improved significantly, with higher MoCA total scores (p < 0.001, d = 1.52) and better outcomes in visuospatial ability, language, and delayed recall domains. No significant changes were detected in salivary cortisol levels (p = 1.000). Conclusions: A six-week iVRE program is safe and feasible, promoting clinically relevant improvements in quality of life, emotional well-being, and cognitive function in women with FM, despite the absence of changes in cortisol. These findings highlight iVRE as a promising complementary therapeutic strategy within multidisciplinary FM management, warranting further controlled studies with larger samples and long-term follow-up to confirm its efficacy and explore underlying mechanisms.

Environmental exposures are recognized as determinants of health, particularly in vulnerable populations such as hemodialysis patients, whose impaired kidney function may heighten susceptibility. This study assesses the association between meteorological and air-pollution factors and the risk of hospital admission and mortality in end-stage kidney disease patients, considering both acute peaks and sustained exposure through weekly and monthly averages. A retrospective cohort of 336 hemodialysis patients treated between 2016 and 2024 at Hospital Universitario Príncipe de Asturias (Madrid, Spain) was analyzed, including 563 admissions and 90 deaths. Time-to-event analyses were conducted separately for mortality and admissions. Cox proportional hazards and parametric survival models (Weibull, log-normal, log-logistic) were applied for mortality, while recurrent-event models (Andersen–Gill, Prentice–Williams–Peterson, frailty) used for admissions. Sulfur dioxide (SO ₂ ) was consistently associated with increased risk in both acute and cumulative exposure models for mortality and admissions. Nitrogen dioxide (NO ₂ ) predicted mortality only in long-term analyses, suggesting a cumulative effect. Solar radiation was linked to accelerated mortality in non-linear models, whereas atmospheric pressure appeared protective for admissions. These findings highlight the influence of environmental exposures, particularly SO ₂ , on adverse outcomes in hemodialysis patients and support incorporating environmental indicators into clinical risk prediction and public health strategies.

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities.