This study examines the forecasting of all-cause hospitalizations in the Greek elderly population until 2032, using historical data from 2001 to 2019. We employed two forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Prophet model. The ARIMA model demonstrated a conservative approach, generating stable forecasts with narrower confidence intervals, making it suitable for identifying gradual trends. In contrast, the Prophet model, with its flexibility in trend capture, produced forecasts with broader confidence intervals, capturing potential sharp increases but with greater uncertainty. Our findings underscore that forecasting accuracy varies across age groups, with the highest precision observed in the 80+ age cohort, reflecting the more predictable healthcare utilization patterns of older populations. These insights emphasize the value of a multi-model approach in healthcare planning, particularly for accurately predicting trends within aging populations and efficiently allocating healthcare resources.

Accurately assessing body fat percentage (BF%) is crucial for healthcare and fitness but is hindered by gold-standard methods that are costly and invasive. This study employs a dataset containing variables such as age, sex, Body Mass Index (BMI), and body circumferences, from individuals whose body fat percentage (BF%) was estimated via underwater weighing, to develop predictive machine learning models. Multiple regression techniques alongside a neural network were employed to compare model accuracies in estimating BF%. Ridge Regression emerged as the most effective model, demonstrating the highest R2 score. Notably, feature importance analysis using ElasticNet and SHAP revealed that abdominal circumference was the most significant predictor of BF%, challenging the adequacy of BMI as a measure of adiposity. These insights advocate for the broader adoption of circumference measurements in everyday practice to enhance the predictive accuracy of cost-effective and easily performed BF% estimation.

Background: As the population ages, the prevalence of surgical interventions in individuals aged 65+ continues to increase. This poses unique challenges due to the higher incidence of comorbidities, polypharmacy, and frailty in the elderly population, which result in high peri-operative risks. Traditional preoperative risk assessment tools often fail to accurately predict post-operative outcomes in the elderly, overlooking the complex interplay of factors that contribute to risk in the elderly. Methods: A literature review was conducted, focusing on the predictive value of CGA for postoperative prognosis and the implementation of perioperative interventions. Results: Evidence shows that CGA is a superior predictive tool compared to traditional models, as it more accurately identifies elderly patients at higher risk of complications such as postoperative delirium, infections, and prolonged hospital stays. CGA includes assessments of frailty, sarcopenia, nutritional status, cognitive function, mental health, and functional status, which are crucial in predicting post-operative outcomes. Studies demonstrate that CGA can also guide personalized perioperative care, including nutritional support, physical training, and mental health interventions, leading to improved surgical outcomes and reduced functional decline. Conclusions: The CGA provides a more holistic approach to perioperative risk assessment in elderly patients, addressing the limitations of traditional tools. CGA can help guide surgical decisions (e.g., curative or palliative) and select the profiles of patients that will benefit from perioperative interventions to improve their prognosis and prevent functional decline.

This study investigates the forecasting of cardiovascular mortality trends in Greece's elderly population. Utilizing mortality data from 2001 to 2020, we employ two forecasting models: the Autoregressive Integrated Moving Average (ARIMA) and Facebook's Prophet model. Our study evaluates the efficacy of these models in predicting cardiovascular mortality trends over 2020-2030. The ARIMA model showcased predictive accuracy for the general and male population within the 65-79 age group, whereas the Prophet model provided better forecasts for females in the same age bracket. Our findings emphasize the need for adaptive forecasting tools that accommodate demographic-specific characteristics and highlight the role of advanced statistical methods in health policy planning.

Antibiotic resistance presents a critical challenge in healthcare, particularly among the elderly, where multidrug-resistant organisms (MDROs) contribute to increased morbidity, mortality, and healthcare costs. This review focuses on the mechanisms underlying resistance in key bacterial pathogens and highlights how aging-related factors like immunosenescence, frailty, and multimorbidity increase the burden of infections from MDROs in this population. Novel strategies to mitigate resistance include the development of next-generation antibiotics like teixobactin and cefiderocol, innovative therapies such as bacteriophage therapy and antivirulence treatments, and the implementation of antimicrobial stewardship programs to optimize antibiotic use. Furthermore, advanced molecular diagnostic techniques, including nucleic acid amplification tests and next-generation sequencing, allow for faster and more precise identification of resistant pathogens. Vaccine development, particularly through innovative approaches like multi-epitope vaccines and nanoparticle-based platforms, holds promise in preventing MDRO infections among the elderly. The role of machine learning (ML) in predicting resistance patterns and aiding in vaccine and antibiotic development is also explored, offering promising solutions for personalized treatment and prevention strategies in the elderly. By integrating cutting-edge diagnostics, therapeutic innovations, and ML-based approaches, this review underscores the importance of multidisciplinary efforts to address the global challenge of antibiotic resistance in aging populations.

The process of aging leads to a progressive decline in the immune system function, known as immunosenescence, which compromises both innate and adaptive responses. This includes impairments in phagocytosis and decreased production, activation, and function of T- and B-lymphocytes, among other effects. Bacteria exploit immunosenescence by using various virulence factors to evade the host’s defenses, leading to severe and often life-threatening infections. This manuscript explores the complex relationship between immunosenescence and bacterial virulence, focusing on the underlying mechanisms that increase vulnerability to bacterial infections in the elderly. Additionally, it discusses how machine learning methods can provide accurate modeling of interactions between the weakened immune system and bacterial virulence mechanisms, guiding the development of personalized interventions. The development of vaccines, novel antibiotics, and antivirulence therapies for multidrug-resistant bacteria, as well as the investigation of potential immune-boosting therapies, are promising strategies in this field. Future research should focus on how machine learning approaches can be integrated with immunological, microbiological, and clinical data to develop personalized interventions that improve outcomes for bacterial infections in the growing elderly population.

OBJECTIVE:This nationwide study aims to analyze mortality trends for all individual causes in Greece from 2001 to 2020, with a specific focus on 2020, a year influenced by the COVID-19 pandemic. As Greece is the fastest-aging country in Europe, the study's findings can be generalized to other aging societies, guiding the reevaluation of global health policies. METHODS:Data on the population and the number of deaths were retrieved from the Hellenic Statistical Authority. We calculated age-standardized mortality rates (ASMR) and cause-specific mortality rates by sex in three age groups (0-64, 65-79, and 80+ years) from 2001 to 2020. Proportional mortality rates for 2020 were determined. Statistical analysis used generalized linear models with Python Programming Language. RESULTS:From 2001 to 2020, the ASMR of cardiovascular diseases (CVD) decreased by 42.7% (p < 0.0001), with declines in most sub-causes, except for hypertensive diseases, which increased by 2.8-fold (p < 0.0001). In 2020, the proportional mortality rates of the three leading causes were 34.9% for CVD, 23.5% for neoplasms, and 9.6% for respiratory diseases (RD). In 2020, CVD were the leading cause of death among individuals aged 80+ years (39.3%), while neoplasms were the leading cause among those aged 0-79 years (37.7%). Among cardiovascular sub-causes, cerebrovascular diseases were predominant in the 80+ year age group (30.3%), while ischemic heart diseases were most prevalent among those aged 0-79 years (up to 60.0%). CONCLUSIONS:The global phenomenon of population aging necessitates a reframing of health policies in our aging societies, focusing on diseases with either a high mortality burden, such as CVD, neoplasms, and RD, or those experiencing increasing trends, such as hypertensive diseases.