Search Results (105)

Adapting Large Language Models (LLMs) to specialized domains like geriatric care remains a significant challenge due to the limited availability of domain-specific data and the difficulty of achieving efficient yet effective fine-tuning. Current methods often fail to effectively harness domain-specific causal insights, which are crucial for understanding and solving complex problems in low-resource domains.To address these challenges, we propose Causality-Aware, Parameter-Efficient Learning (CPEL), a novel framework that leverages domain-specific causal relationships to guide a multi-layer, parameter-efficient fine-tuning process for more effective domain adaptation. By embedding causal reasoning into the model’s adaptation pipeline, CPEL enables efficient specialization in the target domain while maintaining strong task-specific performance. Specifically, the Causal Prompt Generator of CPEL extracts and applies domain-specific causal structures, generating adaptive prompts that effectively guide the model’s learning process. Complementing this, the MPEFT module employs a dual-adapter mechanism to balance domain-level adaptation with downstream task optimization. This cohesive design ensures that CPEL achieves resource efficiency while capturing domain knowledge in a structured and interpretable manner. Based on this framework, we delved into its application in the field of geriatric care and trained a specialized large language model (Geriatric Care LLaMA) tailored for the aged-care domain, leveraging its capacity to efficiently integrate domain expertise. Experimental results from question-answering tasks demonstrate that CPEL improves ROUGE scores by 9–14% compared to mainstream LLMs and outperforms frontier models by 1–2 points in auto-scoring tasks. In summary, CPEL demonstrates robust generalization and cross-domain adaptability, highlighting its scalability and effectiveness as a transformative solution for domain adaptation in specialized, resource-constrained fields. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

Introduction: Postoperative delirium (POD) remains a major complication in geriatric surgical care, with poorly understood molecular mechanisms. Emerging evidence links cardiac surgery to elevated markers of neurologic injury, even in cognitively intact individuals. While neuroinflammation is the prevailing model, a more detailed characterization of the systemic inflammatory and metabolic response to surgery may offer deeper insights into POD pathogenesis. Methods: We used the 7K SomaLogic proteomic platform to analyze preoperative and postoperative day-one serum samples from 78 patients undergoing cardiac surgery with cardiopulmonary bypass. We compared proteomic profiles within individuals (pre- vs. post-surgery) and between those who developed POD and those who did not. Functional analyses were performed to identify relevant biological pathways. A composite metabo-inflammatory score (MIF) was derived to quantify systemic derangement. We modeled the association between POD and age, sex, baseline cognition, and MIF score. Results: Cardiac surgery with CPB was associated with marked inflammatory responses across all subjects, including increased IL-6, CRP, and serum amyloid A. Compared to controls, POD cases showed greater metabo-inflammatory shifts from baseline (average logFC = 2.56, p < 0.001). Lower baseline cognitive scores (OR = 0.74, p = 0.019) and higher MIF scores (OR = 1.03, p = 0.013) were independently associated with increased POD risk. Conclusions: Cardiac surgery with CPB elicits a significant metabo-inflammatory response in all patients. However, those who develop POD exhibit disproportionately greater dysregulation. Full article

Background: Assessing nutrition-related health risks in older individuals is often overlooked in clinical practice due to the lack of appropriate methods of evaluation. While anthropometric measurements and body composition analyses are mainly used, these tools are not standardized for the oldest old and fail to account for age-related changes. This underscores the need for improved assessment techniques that accurately capture the progressive and non-linear shifts in nutritional status throughout the aging process. Accordingly, the primary aim of our paper is to identify the most effective tools to use for evaluating nutritional status in the oldest population. Methods: To address this gap, we conducted a cross-sectional study, investigating the nutritional status of a cohort of Sicilian individuals aged between 65 and 111, using methods commonly applied to adult and older adult populations. These included the BIoimpedance Analysis (BIA), the Mini Nutritional Assessment (MNA) evaluation, and nutritional risk indices such as the COntrolling NUTritional Status (CONUT) score and Geriatric Nutritional Risk Index (GNRI). Results: Despite the oldest population being classified as “at risk” of malnutrition by the MNA or “cachetic” by BIA, our results indicated a “normal” or “low risk” of malnutrition when assessments were performed using tools (GNRI and CONUT) that were not reliant on body composition parameters. These findings align with clinical history assessments conducted during their recruitment. Conclusions: This pilot study highlights the need for future research aimed at developing standardized, multidimensional assessment models tailored to the heterogeneity of each age group, to improve risk stratification, clinical outcomes, and personalized nutritional care. Full article

Background/Objectives: Older adults (ages ≥ 65) have experienced longer recovery, decreased independence in self-care, and reduced quality of life after diagnosis of mild traumatic brain injury (mTBI). Although the sequela following mTBI has also generated higher healthcare costs in older adults, the research on associations returning to the emergency department (ED) has been limited. This study explored subsequent mild traumatic brain injury (mTBI-S) ED visit relationships among older adult populations, fall injuries, payors, and discharge outcomes. Methods: The design was a population-based cross-sectional study using data from the 2018 Nationwide Emergency Department Sample (NEDS). The study sample size was 4932. Descriptive analysis and correlation analysis described characteristics of people with subsequent mTBI visits. Logistic regressions and odds ratios ascertained the relationship between subsequent mTBI visits and the predictor variables of age, fall injury, payors (Medicare, Medicaid, Private, and other), and the outcome variable of healthcare services. Results: Falls and referrals to healthcare service associations were significant (p < 0.001, X²₁ = 123.6). The association between Medicare and referral to healthcare service visits was also significant (p < 0.001, X²₃ = 1059.9). The odds ratio in populations aged ≥65 (OR 4.172, p < 0.001, CI 95% 3.427, 5.079), falls (OR 3.847, p < 0.001, CI 95% 2.649, 5.587), and Medicare (OR 4.492, p < 0.001, CI 95% 1.273, 2.106) had an increased probability of referral to healthcare services. Conclusions: Geriatric populations, falls, and Medicare carriers had an increased probability of healthcare service referral upon readmission to the ED for persistent symptoms after mTBI. Research on geriatric populations and post-mTBI medical monitoring may inform ED discharge models. Full article

Background: The aging population is increasing rapidly, with individuals aged 65 and older now representing more than 15% of the global population. This demographic shift is associated with a rising incidence of age-related cardiovascular diseases (CVDs). Early prediction and prevention of cardiovascular aging are essential to improve health outcomes among elderly patients. Objective: This study aimed to develop and externally validate a mathematical model for predicting cardiovascular aging in individuals aged 65 and older, based on general clinical and behavioral data. Methods: The model was built using data from 800 individuals aged 65+ from Almaty, Kazakhstan. Predictors included sex, marital status, education, smoking, alcohol use, disability, physical activity, total cholesterol, hypertension, BMI, coronary artery disease (CAD), myocardial infarction, diabetes mellitus, and chronic heart failure. A system of ordinary differential equations was used to simulate the dynamic interactions of these factors. Numerical integration was performed using the Runge–Kutta, Adams–Bashforth, and backward Euler methods. The model was verified statistically using Pearson correlation analysis and externally validated on independent age cohorts. In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. These machine learning approaches were used as complementary tools to enhance the robustness and interpretability of the modeling results. Results: The model trained on the 65–74 age group achieved an external validation accuracy of 98.8% and an AUC of 0.989 when applied to the 75–89 group. Risk modeling showed that in the 65–74 group, smoking and alcohol increased the risk of myocardial infarction, hypertension, and obesity by up to 53%. In the 75–89 group, these factors increased the likelihood of hypertension by 21%, chronic heart failure by 16%, and CAD by 14%. Among individuals aged 90+, hypercholesterolemia increased the risk of chronic heart failure by 17%, while hypertension increased myocardial infarction risk by 16%. Conclusions: The proposed model demonstrated high accuracy in predicting cardiovascular aging and identifying high-risk individuals across elderly subgroups. The integration of clustering and classification methods (k-means and Random Forest) provided additional insights and confirmed the consistency of the findings. This multi-method approach may serve as a valuable tool for developing personalized prevention strategies in geriatric care and improving healthy life expectancy. Full article

Introduction: The World Health Organization has identified dementia as a growing global health concern with 10 million new cases diagnosed every year. The growing number of people living with dementia (PLWD) heightens the need for effective interventions that support PLWD and their caregivers. The most effective interventions supporting PLWD and caregivers combine education, care, and services to increase knowledge, decrease stigma, improve care, heighten empathy, and increase engagement of PLWD in their communities. Dementia Friendly America (DFA), administered by USAging, promotes a Dementia Friendly Community (DFC) initiative designed to engage multiple sectors (e.g., business, healthcare, community services) and engage PLWD in a comprehensive community change process. A center for healthy aging and wellness at a midwestern public university developed a network approach in its regional support of eight DFCs, as a part of its Geriatric Workforce Enhancement Program funded by the U.S. Health Resources and Services Administration. Objective: This article documents a mid-size university’s approach to establishing a regional DFC network of urban and rural communities surrounding the university, describing the support the university provided as well as how communities implemented the four-phase DFC process and emulated guiding principles. Results: A retrospective evaluation found engagement with the DFA guiding principles and varying levels of adherence to DFC phases. Discussion: The project team suggests that there are unique roles that universities can play in supporting the DFC movement and that developing a network of communities is a helpful strategy to use in providing this support. Additionally, the authors propose the integration of a community change model to guide future DFC work. Conclusions: This article helps to fill an existing research gap concerning DFC implementation and explores the unique role academic partners can play in cultivating regional hubs of DFC activity. Full article

Background/Objectives: Serious infections in older adults are associated with substantial mortality and morbidity. Diagnosis is challenging because of the non-specific presentation and overlap with pre-existing comorbidities. The objective of this study was to develop a clinical prediction model using clinical features and biomarkers to support emergency care physicians in diagnosing serious infections in acutely ill older adults. Methods: We conducted a prospective cross-sectional diagnostic study, consecutively including acutely ill patients (≥65 year) presenting to the emergency department. Clinical information and blood samples were collected at inclusion by a trained study nurse. A prediction model for any serious infection was developed based on ten candidate predictors that were further reduced to four ad interim using a penalized Firth multivariable logistic regression model. We assessed discrimination and calibration of the model after internal validation using bootstrapping. Results: We included 425 participants at three emergency departments, of whom 215 were diagnosed with a serious infection (51%). In the final model, we retained systolic blood pressure, oxygen saturation, and C-reactive protein as predictors. This model had good discriminatory value with an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.82 (95% CI: 0.78 to 0.86) and a calibration slope of 0.96 (95% CI: 0.76 to 1.16) after internal validation. Addition of procalcitonin did not improve the discrimination of the model. Conclusions: The ROSIE model uses three predictors that can be easily and quickly measured in the emergency department. It provides good discriminatory power after internal validation. Next steps should include external validation and an impact assessment. Full article

Background/Objectives: Obesity and depression, in conjunction with multimorbidity, are interconnected conditions increasingly managed in general practitioner (GP) settings, yet these associations remain insufficiently studied in older patients. This study investigates the prevalence of depression across different body mass index (BMI) classes and includes age and gender differences in multimorbid older patients, offering a novel perspective on subgroup-specific patterns. Further the agreement between GP depression diagnoses and the Geriatric Depression Scale (GDS) is studied and patient-specific factors that may affect the agreement are explored, aiming to improve future diagnostics for vulnerable subgroups. Methods: Data were provided by the baseline assessment of the MultiCare Study, a prospective multicenter observational cohort of multimorbid patients aged 65+ years recruited from 158 GP practices across eight study centers in Germany. Data from 2568 study participants were analyzed based on GP-coded International Classification of Diseases (ICD) diagnoses, structured GP questionnaires, and patient questionnaires. Assessments included data on the BMI and depression (15 item version of the GDS). Agreement between GP diagnoses of depression and GDS assessment was measured using Cohen’s kappa. Four logistic regression models were used to examine the effects of patient-specific factors on the agreement of depression diagnosis (match or mismatch). Results: GPs diagnosed depression in 17.3% of cases, compared to the detection of depressive symptoms in 12.4% of the patients by GDS (cut-off ≥ 6 points). The highest prevalence rates were observed in patients with obesity class III (25.0% by GP; 21.7% by GDS). Women were significantly more likely to receive a depression diagnosis by a GP across most BMI classes (except obesity classes II and III). The detection of depressive symptoms by GDS was significantly more prevalent in older multimorbid obese patients (≥75 years), except for patients with obesity class III. The overall agreement between GP diagnosis and GDS assessment was weak (κ = 0.156, p < 0.001). The highest agreement was found for people with obesity class III (κ = 0.256, p < 0.05). Factors associated with a True Positive depression diagnosis (match by both GDS and GP) were female gender (odds ratio (OR) = 1.83, p < 0.05), widowhood (OR = 2.43, p < 0.01), limited daily living skills (OR = 3.14, p < 0.001), and a higher level of education (OR = 2.48, p < 0.01). A significantly lower likelihood of a False Negative depression diagnosis was found for patients with obesity class III. Conclusions: This study highlights the significant prevalence of depression among multimorbid older adults across different BMI classes, particularly in those with obesity class III. The weak diagnostic agreement between GP diagnosis and GDS assessment suggests a need for improved diagnostic practices in primary care. Implementing standardized screening tools and fostering collaboration with mental health specialists could enhance the identification and management of depression in this vulnerable population. Full article

Background: It is a well-established fact that late-life depression represents a significant public health issue, particularly in low- and middle-income countries experiencing rapid demographic aging. Although its clinical and societal impacts are well-recognized, data on the interplay between depressive symptoms and functional status in older populations remain limited for Türkiye. This study aimed to estimate the prevalence of depression among individuals aged 65 years or older, examine its associations with instrumental and basic activities of daily living, and identify key sociodemographic and behavioral correlates. Methods: In this study, data obtained from a population-based survey in 2264 clusters by the Turkish Statistical Institute (TUIK) were used, and weighted data were yielded from 6,036,396 adults aged 65 and over. Depression was measured using the Geriatric Depression Scale (GDS), categorizing participants as “not depressed”, “mildly depressed”, or “severely depressed”. Functional status was evaluated using the Lawton–Brody Instrumental Activities of Daily Living (IADL) Scale and the Katz Activities of Daily Living (ADL) Scale. Logistic regression models, adjusted for age and body mass index (BMI), were used to determine the associations of depression with functional impairment and various covariates, including gender, education, marital status, chronic disease, physical activity, smoking, and alcohol use. Results: Overall, the prevalence of depression in this cohort was 49.9% [95% CI = 48.7–51%], with 36.0% [95% CI = 34.8–37.0%] classified as mild and 13.9% [95% CI = 13.1–14.7%] as severe depression. IADL and ADL scores were negatively correlated with GDS scores (r = −0.416 and r = −0.321, respectively; p < 0.001). In logistic models, lower IADL scores were linked to higher odds of mild (OR = 0.797, 95% CI = [0.796–0.798], p < 0.001) and severe depression (OR = 0.689, 95% CI = [0.688–0.690], p < 0.001). Being semi-dependent or dependent in ADL further escalated depression risk. Female gender, lower education, single/divorced status, chronic disease, and inactivity also emerged as strong predictors. Conclusions: The findings of this study suggest that depression is highly prevalent among older adults in Türkiye, with functional impairment, unfavorable health behaviors, and sociodemographic vulnerabilities heightening risk. Integrating depression screening into geriatric care—alongside interventions to maintain functional independence—may help mitigate the burden of late-life depression in similar contexts. Full article

Background: Aging is a global phenomenon closely associated with changes in cognitive function and mental health. These conditions substantially burden public health systems and adversely affect the quality of life of older adults. This study aimed to examine changes in depressive symptoms and cognitive function over a 12-month follow-up period in a cohort of Brazilian older adults attending primary care. Methods: This observational longitudinal study included a randomized sample of individuals aged ≥60 years residing in São Paulo, Brazil, and registered at a Primary Healthcare Unit (PHU). Data collection involved administering a sociodemographic and health questionnaire along with two validated instruments: the Geriatric Depression Scale-15 (GDS-15) and the Mini-Mental State Examination (MMSE). Linear regression models were used for the analyses. Results: A total of 368 older adults were included, with 63% being men and a mean age of 74.65 years. After one year, depressive symptoms showed a notable increase, with the mean GDS-15 score rising from 5.97 to 7.48 (Cohen-d = 0.542). Likewise, there was a decrease in the mean MMSE score ranging from 19.11 to 18.88 (Cohen-d = 0.216). Adjusted regression analyses revealed that depressive symptoms at baseline (B = 0.696; p = 0.048; R² = 0.19) and cognitive function at baseline (B = 0.444; p < 0.001; R² = 0.26) were predictive of their respective deteriorations over the follow-up period. Conclusions: Depressive symptoms and cognitive decline place a significant burden on public health systems in aging societies. These findings underscore the importance of continuous monitoring and early intervention strategies to mitigate their impact and enhance the quality of life for older adults. Full article

Background/Objectives: The current study aimed to evaluate the oral health self-perception on quality of life in the elderly using the Geriatric Oral Health Assessment Index (GOHAI) to assess the impact of demographic and oral health factors on oral health-related quality of life. Methods: An observational prospective cross-sectional study for the entire six-month period was conducted in a dental health care center in the United Arab Emirates. The principal inclusion criterion is being aged 60 and over. The GOHAI questionnaire is composed of 4 domains of 12 items that address functional limitation, pain and discomfort, psychological impacts, and behavioral impacts. Patients were questioned about the frequency at which they experience any of the 12 listed problems. Results: A total of 318 respondents participated in the study and completed the whole questionnaire. Among these participants, 63.5% (n = 202) were male and 86.8% (n = 276) were ≤70 years. The average GOHAI score was 13.25, with a 95% confidence interval (CI) [12.4%, 14%], indicating a low self-perception of oral health by the allocated sample. Statistical modeling identified dry mouth (OR = 2.21, 95% CI 1.40–3.48) and chewing problems (OR = 1.87, 95% CI 1.09–3.20) as the strongest determinants of poor oral health-related quality of life (OHRQoL) in the elderly population. Conclusions: Healthcare professionals should develop targeted strategies to address the specific needs of this population, ensuring sustained improvements in their quality of life. Full article

Frailty is a key concept in geriatric care; yet its definition and assessment remain debated. Since the early 2000s, two main models have emerged: the Fried frailty phenotype, focusing on physical deficits, and the Mitnitski frailty index, which incorporates broader health factors. These divergent approaches have led to over 50 frailty instruments, reflecting the absence of a unified framework. This review explores the content, weighting, and scoring methods of frailty instruments, identifying potential concerns derived from this. This review exposes the overlap of frailty with other constructs including function, disability, morbidity, and sarcopenia. Many instruments lack content validity, and detect highly heterogeneous samples within and between scales, all labeled under the “frail” tag. This poses challenges to interpreting instrument responsiveness. In addition, frailty should not be considered a clinical entity with a unique etiology. This review discusses how the broad nature of frailty conflicts with modern paradigms of individualization and precision. They may be useful in primary care, but lack the specificity for secondary care evaluations. This article also discusses how the predictive validity of frailty should be interpreted with caution. Finally, we summarize our findings and propose a new definition of frailty, highlighting the strengths and weaknesses of the construct. The identified inconsistencies should serve as a guide for refining the concept of frailty, both in research and in its application to geriatric care. Full article

As the global population ages, so too does the prevalence of older people with chronic kidney disease (CKD). Helping people age well with CKD and supporting older people with end-stage kidney disease (ESKD) to make personalized decisions regarding kidney replacement therapy (KRT) or conservative care (CC) are an essential component of care. However, these factors are relatively underreported in both the fields of nephrology and geriatric medicine, and prospective, randomized evidence is lacking. This narrative review article, authored by both nephrologists and geriatricians, discusses specific geriatric issues that arise in older people with CKD and why they matter. The available evidence for KRT or CC in older people with frailty is outlined. The importance of performing a comprehensive geriatric assessment, or a modified nephrogeriatric assessment, to ensure a systematic evaluation of the person’s medical problems and life needs, goals, and values is described. We consider different models of nephrogeriatric care and how they may be implemented. Kidney supportive care—addressing an individual’s symptoms and overall well-being alongside the more traditional nephrological principles of preventing disease progression and optimizing risk—is highlighted throughout the article. We outline ways of identifying the later stages of a person’s disease journey, when transition to palliative care is indicated, and elaborate methods of preparing patients for this through multidisciplinary advance care planning. Finally, we discuss practice and systems for nephrogeriatric care in five different European countries and consider future directions, challenges, and highlights in this rapidly evolving, increasingly relevant field. Full article

Introduction: Malnutrition is a major contributor to poor treatment and survival outcomes in pancreatic cancer, yet nutritional assessment is not standardized or consistently implemented in the care of oncology patients. The Geriatric Nutritional Risk Index (GNRI), calculated from serum albumin and body weight, may be useful as a practical tool for identifying patients at risk of poor nutritional status. Purpose: To provide a preliminary analysis using a limited selection of variables to examine the association of the GNRI at diagnosis and the GNRI change over time with overall survival in patients with pancreatic cancer. Methods: This retrospective study included 314 patients aged ≥18 years with pancreatic cancer. The GNRI was calculated at diagnosis and ≥30 days later. Patients were categorized by the GNRI at diagnosis (no risk >98, any risk ≤98) and change in the GNRI over time (no change/increase, mild decrease, and severe decrease). Additional variables included were demographics and stage. Comparative analysis included t-tests, chi-square tests, and ANOVA. Survival was analyzed using Kaplan–Meier curves, log-rank tests, and Cox proportional hazards modeling. Results: Median survival was significantly decreased in patients in the any-nutritional-risk group compared to the no-nutritional-risk group at diagnosis (442 vs. 1105 days), and those experiencing severe decreases in the GNRI scores compared to mild decreases and no change or increases (372.5 vs. 712 vs. 1791 days), respectively. Survival analysis stratified by the GNRI at diagnosis shows that both mild (HR 2.19, 95%, and CI 1.46–3.30) and severe decreases (HR 4.04, 95%, and CI 2.64–6.18) in the GNRI scores were independently associated with decreased survival versus no change or increase in the GNRI group after controlling for stage. Log-rank tests also show patients with any nutritional risk at diagnosis had significantly lower survival than those with no nutritional risk (p = 0.00052). Conclusions: Lower GNRI scores showing greater nutritional risk at diagnosis and decreasing GNRI scores over time were predictors of decreased survival in pancreatic cancer. Our findings indicate that the GNRI may be valid and effective for the early identification of patients with a high nutritional risk who require nutritional interventions to improve outcomes in pancreatic cancer. However, more research is needed using larger samples and a greater variety of variables to confirm the presence and strength of this relationship, examine the effect of patient factors known to be associated with survival and nutrition, and explore potential influential confounders. Full article