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Enteric infectious diseases claim more than 1 million lives annually and are among the top ten causes of death in children younger than 5 years. Remarkable global investment has been dedicated to enteric infectious disease prevention and control; however, the shifting global health landscape is testing the continuance of progress. To evaluate the current status and guide future interventions, we present the latest epidemiological estimates of enteric infectious diseases from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 and assess progress towards the Global Action Plan for the Prevention and Control of Pneumonia and Diarrhoea (GAPPD) mortality target of fewer than 20 deaths per 100 000 children younger than 5 years by 2025. We quantified the incidence, mortality, and disability-adjusted life-years (DALYs) of enteric infectious diseases by age, sex, and year across 204 countries and territories from 1990 to 2023. In GBD 2023, the following were considered under the category of enteric infectious diseases: diarrhoeal diseases, enteric fever (typhoid and paratyphoid), invasive non-typhoidal Salmonella spp (iNTS) infections, and other intestinal infectious diseases. We also examined 15 aetiologies contributing to diarrhoeal diseases. Incidence and prevalence were estimated with DisMod-MR (version 2.1), a Bayesian meta-regression tool, drawing on data from systematic reviews, population-based surveys, claims data, and hospital sources. Cause-specific mortality was modelled with Cause of Death Ensemble Modelling based on data from sources including vital registration, mortality surveillance, verbal autopsy, and minimally invasive tissue sampling. Years of life lost and years lived with disability were computed and combined to derive DALYs. For aetiology-specific estimation, population-attributable fractions (PAFs) for 15 pathogens were derived with a counterfactual framework. Point estimates and 95% uncertainty intervals (UIs) were generated from 250 draws from the posterior distribution. In 2023, enteric infectious diseases resulted in an estimated 1·27 million (95% UI 0·963-1·68) deaths globally, declining from 3·69 million (3·04-4·56) in 1990. The global age-standardised mortality rate (ASMR) decreased from 74·1 (62·0-92·9) per 100 000 population to 16·4 (12·6-21·3) per 100 000 population during the same period. Diarrhoeal diseases accounted for most deaths in 2023 (1·11 million [0·811-1·54]), followed by enteric fever and iNTS. South Asia and sub-Saharan Africa remained the most affected regions in 2023, with 599 000 (441 000-882 000) and 501 000 (373 000-648 000) deaths due to enteric infectious diseases, respectively, predominantly from diarrhoeal disease. Rotavirus was the leading cause of all-age diarrhoeal disease deaths (PAF 16·3% [12·0-21·5]), followed by norovirus (10·2% [2·4-17·0]) and Shigella spp (9·3% [5·4-15·2]). Among children younger than 5 years, PAFs of deaths due to diarrhoeal diseases were 40·2% (32·5-48·5) for rotavirus, 24·0% (15·1-36·7) for Shigella spp, and 23·4% (13·7-34·3) for adenovirus. Across 204 countries and territories, 141 met the GAPPD mortality target in 2023. The driving aetiologies among countries that did not meet the target in 2023 varied slightly by GBD super-region, but the highest or second-highest number of deaths in children younger than 5 years were consistently attributed to rotavirus. Astrovirus and sapovirus, newly included in GBD 2023, were responsible for 24 600 (6290-49 000) and 18 800 (4650-44 400) deaths, respectively, in 2023, mainly in children younger than 5 years. Our findings show that mortality and ASMRs of enteric infectious diseases declined substantially between 1990 and 2023. This decline is consistent with the expansion of public health measures and broader socioeconomic development. However, the burden in 2023 remains considerably high, with the highest mortality concentrated in sub-Saharan Africa and south Asia. Considering that more than a quarter of all countries had yet to meet the GAPPD mortality target in 2023, sustained efforts are needed to address the persistent burden in affected countries and to adapt to the changing global health landscape. Gates Foundation.
Rapid population aging and a worsening shortage of care workers necessitate the identification of older adults who require proactive interventions. Although machine learning (ML) has been increasingly applied in gerontology, existing studies have predominantly focused on social isolation, loneliness, depression, falls, and frailty in isolation rather than on the integrated construct of care needs. This study aimed to develop and interpret an explainable ML model that identifies care needs in community-dwelling Korean older adults. Beyond physical health indicators such as disease and functional status, this study adopted a comprehensive approach that included mental health, cognitive function, health behaviors, and socioenvironmental determinants, such as social participation, social support, and the housing environment, to present an integrated model encompassing both health and social care needs. Data were obtained from the 2023 Korea Senior Survey, a nationally representative sample of 10,078 community-dwelling adults aged 60 years and older. The data were split 70:30 into training (n=7054) and held-out test (n=3024) sets. Seven algorithms were compared (logistic regression, decision tree, support vector machine, random forest, gradient-boosted decision trees, extreme gradient boosting, and light gradient boosting machine) using stratified 5-fold cross-validation on the training set. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC). Model interpretability used Shapley additive explanations with bootstrap stability assessment across folds. Model A (excluding activities of daily living or instrumental activities of daily living [IADL]) achieved good discrimination (AUC 0.892, 95% CI 0.866-0.918), adequate calibration (calibration slope=0.826), and positive clinical net benefit, demonstrating that upstream factors alone can identify older adults with care needs without relying on functional status. Shapley additive explanations analysis identified age, nutritional risk, employment status, depressive symptoms, self-rated health, cognitive function, household income, and home modification as the leading predictors, with high rank stability across cross-validation folds. Model B (including activities of daily living or IADL) yielded a higher AUC (0.976, 95% CI 0.962-0.989), but this reflected the near-tautological relationship between IADL and self-reported care needs rather than genuine upstream predictive value. Using an objective composite outcome yielded equivalent discrimination (AUC 0.892), supporting robustness to the outcome definition. Explainable ML models offer high predictive accuracy and strong interpretability for identifying care needs among older adults. Care needs in community-dwelling Korean older adults can be identified with good discrimination, calibration, and clinical net benefit using multidimensional nonfunctional factors alone. By highlighting the significant roles of health, social, and environmental factors, this study provides empirical evidence to support evidence-based decision-making for the Long-Term Care Insurance system and integrated community care policies.
Malnutrition in older patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is frequently overlooked. New-onset hypoalbuminemia during hospitalization is an important signal of worsening nutritional and inflammatory burden and is closely associated with adverse outcomes such as prolonged mechanical ventilation, readmission, and mortality. Early proactive warning tools based on admission data are lacking. This dual-center retrospective cohort study included patients aged ≥65 years who were hospitalized for AECOPD between January 2023 and December 2025 and had normal serum albumin at admission (≥35 g/L). The Zigong cohort (n=1,502) was used for model development and internal validation, whereas the Qiannan cohort (n=1,086) served as the external test cohort. Among the Zigong cohort, 1052 patients were assigned to the training set and 450 to the internal validation set. The primary outcome was defined as new-onset hypoalbuminemia recorded in the discharge diagnosis during the same hospitalization among patients with normal admission albumin. Because the retrospective source database did not retain a standardized schedule for inpatient albumin re-testing, albumin measurement frequency and the median time from admission to outcome ascertainment could not be evaluated. To minimize information leakage, all feature-selection procedures were performed exclusively in the training set. Missing data were handled using multiple imputation. Candidate predictors were demographics and routine laboratory tests completed within 24 hours of admission. Core features were selected using the intersection of univariable screening, least absolute shrinkage and selection operator (LASSO) regression, and the Boruta algorithm. Logistic regression, decision tree, random forest, XGBoost, LightGBM, support vector machine, and artificial neural network models were developed with five-fold cross-validation and grid-search tuning. Discrimination, calibration, and decision-curve analysis were evaluated in an internal validation set and an external test set. Model interpretability was assessed with SHapley Additive exPlanations (SHAP), and the optimal model was deployed as an online risk calculator. Among 1502 older AECOPD inpatients, 335 (22.3%) met the study definition of new-onset hypoalbuminemia by discharge. Eight core predictors available at admission were retained: cholinesterase (CHE), high-sensitivity C-reactive protein (hs-CRP), hematocrit (HCT), anion gap (AG), serum magnesium (Mg), alanine aminotransferase (ALT), age, and international normalized ratio (INR). XGBoost achieved AUCs of 0.85 in the internal validation set and 0.83 in the external test set, compared with 0.84 and 0.82, respectively, for logistic regression, indicating only a modest performance advantage. SHAP indicated that lower CHE, HCT, AG, and Mg and higher hs-CRP, age, and INR were associated with higher risk. An interpretable model derived from routine admission laboratory tests may support early risk stratification for new-onset hypoalbuminemia in older hospitalized patients with AECOPD. Nevertheless, further prospective validation is required to confirm its clinical utility and generalizability.
The population of adults aged 65 and older is rapidly increasing, while the availability of caregivers is declining. Smart homes that provide unobtrusive, continuous monitoring and alerting on clinically relevant changes in daily activity patterns offer a potentially innovative solution for aging in place. This study aims to evaluate the barriers and facilitators to the adoption of a low-cost smart home embedded within a community-based approach to health monitoring for older adults with multiple chronic conditions and who are experiencing poverty. Using a prospective, mixed methods design and iterative community co-design, 46 older adults from 7 different language groups were continuously monitored for 6 months with ambient sensors installed in their homes. Two older adults were monitored for 4 and 5 months, respectively, resulting in a total sample of 48. The system generated alerts based on movement pattern changes and escalated notifications to participants, support persons, community health workers, and nurses. Sensor data were analyzed descriptively to quantify alert patterns and response rates, while written text-based data from in-the-moment surveys, community health workers' and registered nurses' notes, and semistructured interviews underwent qualitative descriptive analysis and reflexive thematic coding. The system generated 37 million sensor readings condensed into 1.2 million high-level events and 4719 novel alerts. Qualitative data comprised 34,086 words of text. Participants responded to 1.57% (74) of the initial email alerts and 7.79% (368) of the follow-up SMS text message alerts sent when no email response was received. Community health workers and registered nurses responded to 78.36% (n=3698) of the escalated alerts, resulting in 1060 contacts with participants in response to alerts. Clinical contacts resulted in 72 interventions. Three major qualitative themes emerged: (1) Alone, (2) Trust, and (3) Human Connection. Subthemes included Safety, Personalization, and Digital Distress defined as stress associated with interacting with digital health-monitoring systems. Participants rated the system highly (mean likelihood-to-recommend rating 8.68/10, SD 1.68); however, they expressed a strong preference for phone calls over automated alerts. Cultural expectations influenced adoption, particularly in multigenerational households. Communities can effectively engage in technology-delivered health care. Future research is needed to improve technical aspects of smart home monitoring systems, including accurate alerting using machine learning, data visualizations for older adults and health care workers, and culturally sensitive features. Additional work should address how and when to communicate automated messaging, engage older adults with their own data, and integrate sensor-based monitoring into health care workflows. Research should also explore personalization through advanced computational approaches such as machine learning and strategies to reduce digital distress.
While molded puree is marketed as a dignified alternative to traditional texture-modified diets for older adults and patients with dementia, the clinical implications of their rapid commercialization remain under-examined. To investigate the knowledge, clinical expectations, and psychosocial drivers regarding molded puree for older adults among community carers and aged-care professionals. This concurrent mixed-methods study recruited 164 community carers (public cohort) and 60 aged-care professionals (staff cohort) who work in non-governmental residential aged-care networks in Hong Kong SAR. Quantitative data were collected via cross-sectional surveys evaluating self-rated knowledge, swallow safety perceptions, and clinical expectations on consumption of molded puree in older adults. Qualitative data were obtained through semi-structured, in-depth interviews with a purposive sub-sample of 12 community carers to explore underlying behavioral drivers. Quantitative differences were analyzed using Mann-Whitney U and Chi-Square tests, while qualitative data underwent thematic analysis. Self-rated knowledge about molded puree was significantly lower in the public cohort compared to professional staff (p < 0.001). A puree equivalence fallacy was prevalent; 23.8% of the public believed molded puree shares the exact texture of conventional puree, and 39.3% assumed it requires no chewing. Staff were significantly more aware of biomechanical differences (65.0% disagreed with equivalence). The public held significantly higher expectations regarding improvements in feeding behaviors (50.6% agreement) and nutritional value (26.2%) compared to professionals (p< 0.05). Qualitatively, thematic analysis revealed twohigher-order themes: psychosocial motivations centered on restoringdining dignity and avoiding dietary stigma, and structural hurdlesinvolving procedural ambiguity. A significant knowledge-perception gap exists between the general public and healthcare professionals. While molded puree offers substantial psychosocial benefits, the prevalence of the puree equivalence fallacy underscores a potential risk for vulnerable older adults. Clinical interventions should prioritize evidence-based swallow safety and standardized screening over aesthetic appeal. Why was this Study Done? Age-related swallowing difficulties, known as dysphagia, often require foods to be blended into a smooth puree. Molded purees are a newer option where blended food is reshaped using gelling agents to look like standard meals, improving visual appeal and dining dignity. This study investigated whether the general public and healthcare professionals understand how to utilize these products safely. How was the Study Conducted? We surveyed 164 community members and 60 professional staff from residential aged-care facilities to compare their understanding. We also conducted in-depth interviews with 12 public respondents to explore their underlying motivations. What did the Study Find? A significant knowledge gap exists between the public and professional staff. Many community members mistakenly believed that molded purees share the exact texture of standard pureed food and require no chewing. In reality, gelling agents alter food behavior, requiring oral processing to swallow safely. Public respondents held highly optimistic expectations regarding nutritional and behavioral improvements. Interviews revealed that the public is heavily motivated by the desire to restore dining dignity and avoid dietary stigma, an aesthetic focus that often overrides essential swallow safety considerations. What does this Mean for the Future? Molded puree effectively enhance mealtimes. However, public education must emphasize that a speech-language pathologist should evaluate individuals before introducing these meals, ensuring physical safety is maintained alongside visual appeal.
Older adults with type 2 diabetes are often subjected to intensive glucose-lowering therapies with a high likelihood of hypoglycaemia, falls, functional decline, and treatment burden. While international recommendations advocate for individualised glycaemic targets, they provide little clarification on when, or how, to de-intensify therapies as health status changes. We undertook a targeted evidence synthesis of randomised controlled trials, observational cohort studies, target trial emulation studies, and international clinical guidelines focusing on glucose-lowering agents for the treatment of diabetes mellitus in patients aged 65 years and older. Evidence was synthesised and prioritised based on relevance for the important outcomes in later life, including hypoglycaemia, hospitalisation, functional decline, quality of life, and mortality. From this evidence and the principles of geriatric medicine, we constructed a pragmatic, frailty-informed clinical decision framework for the purposes of guiding deprescribing and the de-intensification of glucose-lowering therapy. Older adults in a variety of care continuum settings, particularly with frailty, multimorbidity, cognitive impairment, or a limited life expectancy, tend to stay on intensive glucose-lowering therapies for very little, if any, benefit. Target trial emulation studies and observational studies most often show that hypoglycaemia and treatment burden are lowered by de-intensifying high-risk medications, particularly insulin and sulfonylureas, without any clinically important deterioration in glycaemic control. Even with the evidence, existing guidance lacks a practical operational framework to implement such evidence in everyday clinical practice. We suggest a frailty-informed clinical decision framework that integrates functional status, physiologic vulnerability, treatment-related risks, and patient preferences in making glucose-lowering treatment decisions in older adults with type 2 diabetes. This framework shifts the focus to patient safety and functional outcomes, instead of HbA1c targets, to provide a practical approach to deprescribing and de-intensifying treatment to minimise iatrogenic harm and, most importantly, to align care with the preferences of older adults.
Iran is undergoing a rapid demographic transition toward population aging, which poses significant challenges for the health system, social protection mechanisms, and food and nutrition policies. Despite the well-established role of nutrition in healthy aging, its integration into aging-related policies in Iran has not been comprehensively examined. Although national nutrition frameworks exist, they remain fragmented and insufficiently operationalized for the elderly. As this demographic transition in Iran, its struggle to integrate nutrition into aging policies mirrors challenges faced by many other lower-middle-income countries. This study evaluates the coherence, implementation capacity, and strategic alignment of Iran's aging-related policies concerning nutritional needs. This comprehensive review of secondary data is a descriptive analytical policy was conducted using qualitative content analysis. The study examined national policy documents, demographic indicators, and regulatory frameworks published between 2011 and 2025, focusing on five key dimensions: policy convergence, age-specific characteristics, implementation mechanisms, monitoring, and equity. Findings reveal a significant structural imbalance; vast majority (over 70%) of analyzed documents prioritize curative medical interventions over preventive nutritional strategies. Critical gaps identified include the absence of age-specific dietary guidelines, fragmented screening protocols, and a lack of intersectoral coordination between health and social welfare sectors. Unlike successful integrated models such as Thailand's community-based approach, Iran's framework is characterized by fragmentation. To mitigate the health risks of aging, a paradigm shift is required, moving from generalized health policies toward "nutrition-sensitive" aging strategies that integrate systematic screening and localized, preventive interventions into the national policy landscape. Although the findings are context-specific, the framework applied in this study may be adapted by other middle-income countries to assess geriatric nutrition policies within their own health system and policy contexts.
To examine the associations among sleep quality, self-efficacy, depressive symptoms, and cognitive function in older adults, and to explore whether self-efficacy may be involved in the association between sleep quality and cognitive function and whether depressive symptoms may moderate this association. A cross-sectional survey was conducted among 2,030 older adults in Ningxia, China. Sleep quality, cognitive function, self-efficacy, and depressive symptoms were assessed using validated instruments. After adjusting for age, gender, marital status, education level, residence, smoking and drinking status, living alone, exercise, hypertension, diabetes, and coronary heart disease, mediation and moderated mediation analyses were performed using the PROCESS macro. Significant correlations were observed among sleep quality, self-efficacy, depressive symptoms, and cognitive function. Self-efficacy showed a significant indirect effect in the association between sleep quality and cognitive function (B = -0.034, 95% CI: -0.048 to -0.022). In addition, depressive symptoms significantly moderated the association between self-efficacy and cognitive function (B = 0.009, p < 0.01). The magnitude of the indirect association between sleep quality and cognitive function through self-efficacy was stronger at higher levels of depressive symptoms. Poor sleep quality was associated with poorer cognitive function in older adults, and self-efficacy may be involved in this association. Depressive symptoms may further strengthen this indirect association. These findings suggest that sleep quality, self-efficacy, and depressive symptoms may all be relevant to cognitive health among older adults. Memory and thinking problems become more common with age. Poor sleep is also common in older adults and may be linked to worse brain health. However, less is known about the emotional and psychological factors that may be involved in this relationship. We carried out this study to better understand how sleep quality, confidence in handling daily challenges, and depressive symptoms are related to cognitive function in older adults. We studied 2,030 older adults in Ningxia, China. We collected information on their sleep quality, depressive symptoms, and self-efficacy, which refers to a person’s confidence in managing problems and daily tasks. We also assessed cognitive function, including memory and other thinking abilities. We then examined how these factors were related to one another. We found that older adults with poorer sleep quality tended to have lower cognitive function. Poorer sleep was also linked to lower self-efficacy, and lower self-efficacy was linked to poorer cognitive function. In addition, depressive symptoms were related to a stronger negative relationship between self-efficacy and cognitive function. These findings suggest that sleep, mood, and confidence in daily self-management may all be important for cognitive health in older adults. This does not prove cause and effect, but it highlights several factors that may be useful for future prevention and support. Efforts to improve sleep and support emotional well-being may help promote healthy cognitive aging.
Cognitive frailty (CF), the coexistence of physical frailty and cognitive impairment without dementia, is associated with disability, institutionalization, and mortality, yet exercise prescriptions for this syndrome remain insufficiently targeted. This review examined whether four-limb coordinated training, defined as exercise that combines integrated upper- and lower-limb movement with meaningful coordination or motor-cognitive demands, is associated with neuroplasticity-related and clinical benefits in older adults with CF. Following PRISMA 2020 guidance, we conducted a focused PubMed-based systematic search and included six controlled studies for qualitative synthesis. Because interventions, comparators, and outcomes were highly heterogeneous, results were synthesized narratively using a tiered framework covering direct neuroplasticity outcomes, mechanistic proxy biomarkers, and indirect clinical outcomes. Across Baduanjin, virtual reality motor-cognitive training, exergaming, and functional resistance exercise, the direction of effect was generally favorable for global cognition, executive-related outcomes, frailty status, and physical performance including gait, balance, and chair-rise function. Direct mechanistic evidence was limited but suggested possible improvements in cerebral hemodynamics and hippocampal subregion structure, while biomarker studies indicated reductions in oxidative stress and inflammatory burden. Taken together, current evidence suggests that four-limb coordinated training is a biologically plausible and clinically relevant intervention candidate for CF, but confidence remains limited by the single-database search, small samples, study heterogeneity, indirect mechanistic endpoints, and short follow-up. The distinctive contribution of this review is the integration of intervention classification with a cautious neuroplasticity framework that can guide future trials toward standardized definitions, better reporting of coordination complexity and dose, and multimodal mechanistic assessment.
Apathy in Parkinson's disease is characterized by reduced self-initiated, goal-directed behaviour and may overlap clinically with bradykinesia, akinesia, freezing of gait, fatigue, depression, and executive dysfunction. This narrative Review argues that reduced voluntary activity in Parkinson's disease reflects two interacting but partially dissociable domains: motor-execution deficits mediated mainly by nigrostriatal cortico-basal ganglia circuits, and motivational-initiation deficits mediated by mesolimbic, prefrontal-striatal, limbic, and non-dopaminergic modulatory systems. We integrate clinical, neuroimaging, electrophysiological, computational, and preclinical evidence to explain how disrupted dopaminergic signalling, striatal D1/D2 pathway imbalance, prefrontal effort valuation, noradrenergic arousal, serotonergic and cholinergic dysfunction, and glutamatergic control may influence the transition from motor capacity to self-initiated behaviour. We also review validated clinical tools for assessing apathy and discuss how apathy should be distinguished from depression, fatigue, cognitive impairment, normal aging, and advanced motor disability. The proposed framework helps explain why dopaminergic therapy, behavioural rehabilitation, and conventional neuromodulation may improve motor performance without consistently restoring spontaneous initiative or rehabilitation engagement. Future work should prioritize validated motivational phenotyping, PD-specific rehabilitation trials, careful differentiation between established and emerging interventions, and clinically feasible strategies for improving self-initiated daily activity.
Osteoporosis (OP) is a chronic systemic skeletal disorder that predominantly affects the elderly. It is characterized by an imbalance in bone homeostasis, reduced bone mass, microarchitectural deterioration of bone tissue, and increased bone fragility, ultimately leading to a higher risk of fractures and related complications. With the progression of global population aging, the prevalence of OP continues to rise, underscoring the importance of early diagnosis and timely intervention. However, the diagnosis and management of OP-particularly its early detection-remain limited by material constraints such as diagnostic equipment and by subjective factors including clinician experience, which hinder widespread screening. In recent years, artificial intelligence (AI) has emerged as a transformative technology with advantages of efficiency, objectivity, and scalability, and has been increasingly integrated into various medical domains. For example, AI-assisted musculoskeletal measurements on leg and foot radiographs can reduce the measurement time from 166 seconds to 40 seconds, resulting in an overall efficiency improvement of approximately 70%. Applying AI to the diagnosis and treatment of OP can reduce human error, save labor costs, and improve diagnostic accuracy and clinical efficiency. Numerous studies have investigated AI-based approaches in OP-related research and clinical practice. Despite these promising developments, several important limitations should be acknowledged. Considerable heterogeneity exists among published studies regarding patient populations, AI algorithms, and evaluation metrics. Besides, consistent external validation remains insufficient in many studies. Challenges related to data imbalance and potential selection bias further highlight the need for standardized reporting frameworks and multicenter collaborative research to promote safe clinical adoption of AI technologies in osteoporosis management. This review summarizes current AI applications in OP diagnosis, risk prediction and therapy. We highlight key methodological limitations and emerging trends, aiming to guide future research and facilitate safe clinical implementation of AI in OP management.
Cardiac surgery-associated acute kidney injury (CSA-AKI) is a frequent and devastating postoperative complication, particularly among older adults. Accurate risk stratification and early prediction of CSA-AKI are essential for guiding preventive strategies and optimizing clinical decision-making. In this retrospective study, data from two centers (n=623) were utilized for model training and internal validation, whereas data from a third, distinct center (n=110) were reserved for external validation. CSA-AKI was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) Serum creatinine criteria. Key predictors were identified using a consensus of four methods: Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination (RFE), Boruta, and Random Forest-based filtering. Six machine learning (ML) models, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), were developed utilizing five-fold cross-validation. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) approach was applied to interpret the best-performing model. Development of CSA-AKI was noted in 177 patients (24.1%) during the first postoperative week. In terms of comparative performance, LightGBM exhibited the greatest AUC (0.784, 95% confidence interval [CI]: 0.702-0.859). The most influential features were lactate, surgical duration, activated partial thromboplastin time (APTT), transfusion volume, and Prothrombin Time (PT). SHAP-based summary and force plots interpreted the model at global and local levels. Furthermore, SHAP dependence plots elucidated non-linear effects of single features on CSA-AKI risk. Machine learning models demonstrate high efficacy in predicting CSA-AKI risk in older adults. The LightGBM model outperformed other algorithms; coupled with interpretability tools, it can assist clinicians to identify high-risk patients earlier and optimize perioperative management.
The Short Physical Performance Battery (SPPB) is a common tool for examining lower extremity physical functioning and respective intervention effects in older adults. It is primarily used in community-dwelling older adults, but also in multimorbid populations such as nursing home residents. However, previous literature reported floor effects (receiving the lowest score) in this vulnerable population, particularly for the scoring of the 4-meter walking subtest. This study aimed to propose norm values and a scoring system addressing floor effects, to compare sensitivity to performance changes between the original and revised systems, and to externally validate the revised scoring. Data from three randomized controlled trials (PROCARE (n=399, 84.0±7.8 years, 77% female), PROfit (n=97, 82.4±9.8 years, 72% female), and PROGRESS (n=97, 84.6±7.7 years, 75% female)) were re-analyzed. SPPB was administered at baseline and post-intervention. Quartiles for time to complete the 4-meter walking test at baseline of the PROCARE dataset were analyzed, and norm values and a corresponding scoring system (0-4 points) were proposed. Floor effects were analyzed by comparing the number of participants in each scoring category. Sensitivity of the new scoring was evaluated by comparing means and standard deviations of time for completion at baseline and post-interventions. External validation was done using the PROfit and PROGRESS datasets. Based on the adjusted scoring system, a completion time faster than 5.65 seconds (old: 4.82 seconds) corresponded to the highest score (4 points), and a time exceeding 10.80 seconds (old: 8.70 seconds) to the lowest score (1 point). The proposed scoring system reduced the floor effect from 36% to 21%, and analysis of sensitivity revealed a better fit with time to completion. External validation indicated that the proposed scoring categories appropriately reflected participants' functional and cognitive characteristics. The new scoring differentiated walking performance in a multimorbid sample, particularly within the group of low performers and better displayed changes in performance. Thus, it can contribute to detecting changes due to physiological deterioration, limited mobility, and use of walking aids which otherwise may be missed in this vulnerable population.
With the rapid aging of the population and the increasing prevalence of terminal cancer among older adults worldwide, the demand for high-quality home hospice care is growing. However, there is a lack of culturally appropriate and systematic outcome quality evaluation indicators. This study aimed to construct a set of outcome quality evaluation indicators for home hospice care in older patients with terminal cancer in China, guided by the Harmony Nursing Theory. A three-phase methodological design was employed. First, a 12-member multidisciplinary research team was established. Second, preliminary indicators were developed through a literature review of 18 relevant studies and qualitative interviews with 12 healthcare professionals. Third, two rounds of Delphi expert consultation were conducted to refine and validate the indicators. A pilot test was subsequently performed to assess feasibility. We calculated the expert positive coefficient (response rate), expert authority coefficient, and expert opinion coordination coefficient (Kendall's W). In the first round of Delphi consultation, 24 questionnaires were distributed and 22 were returned, yielding a response rate of 91.7% In the second round, 22 questionnaires were distributed and all were returned (100% response rate). The expert authority coefficient was 0.96 in both rounds. Kendall's W was 0.247 and 0.296 in the first and second rounds, respectively. After the first round, three indicators were deleted, fifteen indicators were revised, and four indicators were added. Following the second round, two indicators were modified. The outcome quality evaluation indicator for home hospice care consisted of 4 first-level indicators, 12 second-level indicators, and 45 third-level indicators. This study developed a culturally relevant and methodologically sound outcome quality evaluation indicator for home hospice care in older patients with terminal cancer in China. The indicators provide a tool for evaluating care quality, guiding clinical practice, and improving patient-centered outcomes in home hospice settings.
The metabolic effects of Metformin (Met) and lifestyle modification in early type 2 diabetes mellitus (T2DM) management are well established, but their influence on bone remodeling remains uncertain in under-investigated populations, particularly during early treatment when weight loss and metabolic shifts may transiently affect skeletal turnover. This study examined the short- and mid-term effects of metformin, lifestyle intervention, and their combination on bone turnover markers (BTMs) in treatment-naïve Saudi adults with T2DM. In this 6-month pilot, open-label multicenter randomized controlled trial, 114 treatment-naïve Saudi adults with newly diagnosed T2DM (90 males, 24 females; mean age ± SD 53.6 ± 8.4 years; mean BMI 30.3 ± 3.9 kg/m2; mean HbA1c 7.0 ± 0.6) were assigned to Met (1000 mg/day), lifestyle modification, or combined therapy (n = 38/group). Serum markers of bone resorption (C-terminal telopeptide of type I collagen [CTX], primary outcome) and bone formation (procollagen type I N-terminal propeptide [P1NP], osteocalcin), along with sclerostin (SOST), were measured at baseline, 3 months, and 6 months. Anthropometric, glycemic, lipid, renal, and hepatic parameters were assessed as secondary outcomes. After adjustment for baseline P1NP levels, only the lifestyle group demonstrated a significant reduction in CTX after 6 months (p < 0.05), while osteocalcin, SOST and P1NP showed no significant changes across all interventions. All interventions were associated with improvements in anthropometric and glycemic measures; most pronounced in the combined intervention group. In treatment-naïve adults with T2DM, lifestyle intervention alone was associated with a significant reduction in CTX after 6 months, while metformin-based interventions showed no significant effects on bone turnover markers. These exploratory findings may suggest short-term modulation of bone turnover but should be interpreted cautiously given the pilot design.
Falls among community-dwelling older adults are prevalent and have serious individual, societal and economic consequences. Allied health professionals provide key falls prevention interventions yet their representation in current clinical practice guidelines is inconsistent. Increased recognition of allied health roles and delivering context-specific guidelines for falls care could help to address workforce gaps and optimise care approaches. This scoping review explored the roles of the allied health professions in falls prevention screening, assessment and intervention for community-dwelling older adults. Scoping review, using the Joanna Briggs Institute methodology for scoping reviews. PubMed, CINAHL, Scopus, Cochrane, Web of Science and Allied and Complementary Medicine Database databases. The initial search was completed in November 2023, with a secondary search performed in July 2025. Sources were eligible if they identified or described a specific role of at least one allied health professional in falls prevention care for older adults. No restrictions were placed on publication type or date. Study protocols and conference abstracts were excluded, and only English-language sources were included. ChatGPT-4o was used for initial data extraction. Authors then cross-checked and updated inaccuracies as required. A numerical descriptive analysis, and a qualitative content analysis were performed to answer the research questions. The search identified 442 relevant sources from 34 countries. The roles of 17 allied health professions in falls prevention for community-dwelling older adults were discussed. Screening, assessment and intervention roles were identified spanning medical, physical capacity, environmental, education and behavioural-psychological domains. Profession-specific interventions closely aligned with their primary scope of practice, and notable areas of overlap between professions were highlighted. This review highlights the diverse and overlapping contributions of allied health professionals to falls prevention in community-dwelling older adults. Varying levels of evidence are available across the professions and evidence gaps were highlighted for smaller allied health professions, indicating a need for foundational research to substantiate their roles and facilitate their inclusion in future practice guidelines. TRIAL REGISTRATION DETAILS: https://doi.org/10.17605/OSF.IO/7SV3F.
Osteoporotic vertebral compression fractures (OVCFs) cause significant morbidity in aging populations. Hounsfield unit (HU) from CT and the vertebral bone quality (VBQ) from MRI show promise in assessing bone quality and fracture risk. This study aims to directly compare the predictive efficacy of HU and VBQ for OVCFs and develop a nomogram model integrating HU and VBQ. A retrospective study was conducted involving 385 patients (127 with OVCFs, 258 controls) who were hospitalized at our hospitals between September, 2020 and September, 2024. HU and VBQ were derived from picture archiving and communication system (PACS). Other variables included demographic, clinical, and radiological data. Statistical analyses included t-tests, chi-square tests, multivariable logistic regression, the least absolute shrinkage and selection operator method (LASSO) regression, and receiver operating characteristic (ROC) curve analysis. Then, a nomogram model was established. The calibration, discrimination and clinical practicability of the nomogram model were also evaluated. The OVCF group had significantly higher VBQ and lower HU compared to controls. ROC analysis showed higher diagnostic accuracy for HU than VBQ.A nomogram model for predicting the risk of OVCF occurrence in patients has been developed based on three independent predictors, namely BMI, HU and VBQ. The AUC was 0.84 in the training set and 0.87 in the test set. The model has good practicability for clinics according to the decision curve analysis (DCA) and clinical impact curve (CIC). Both HU and VBQ are effective predictors of OVCFs. The nomogram model showed good internal discrimination and calibration in our study. These findings suggest potential utility for opportunistic screening of OVCF risk in patients undergoing routine spinal CT and MRI. However, external validation in prospective and multi-center cohorts is needed before clinical implementation.
There is growing evidence that nutritional status is a predictor of prognosis for elderly patients, However, specific threshold values for albumin and prealbumin are still lacking for elderly patients undergoing posterior lumbar interbody fusion (PLIF) surgery. This study aims to determine these thresholds in elderly PLIF patients and to perform risk stratification analysis using the combination of albumin and prealbumin. A total of 660 elderly patients undergoing PLIF surgery at Xuan Wu Hospital from November 2018 to April 2024 were included in this retrospective analysis. The optimal cut-off concentrations of albumin and prealbumin for predicting adverse events (AEs) were determined via ROC curve analysis. Subsequently, patients were categorized into four groups according to these cut-off values: Coapa-hh (both markers above cut-off), Coapa-hl (albumin above, prealbumin below), Coapa-lh (albumin below, prealbumin above), and Coapa-ll (both below cut-off). Chi-square analysis revealed that the Coapa-ll group had the highest perioperative complication rate (P < 0.001). Surgical site infection rates were lower in the Coapa-hh (0.90%) and Coapa-hl (0.70%) groups than in the Coapa-lh (5.63%) and Coapa-ll (4.14%) groups (P = 0.023). Patients in the Coapa-hh group also had significantly shorter total and postoperative hospital stays (P = 0.04 and P = 0.019, respectively). Multivariable logistic regression confirmed that the Coapa classification was independently associated with overall complications after PLIF surgery. Our findings suggest that the Coapa should be viewed as a convenient screening tool. It can identify high-risk elderly patients undergoing PLIF surgery who may benefit from perioperative nutritional optimization. This provides valuable guidance for reducing postoperative complications, improving clinical outcomes, and informing nursing interventions.
Prefrailty is associated with anomalies in protein metabolism; however, the serum proteomic signatures remain unclear. This study investigated protein profiles across different health statuses and evaluated their potential for the early identification of prefrailty. Older adults were categorized as robust, prefrail, and frail. Untargeted proteomic screening in a discovery cohort (n = 30) was followed by parallel reaction monitoring (PRM) validation (n = 99). Multidimensional clinical parameters and differentially expressed proteins were integrated within machine learning pipelines to refine the search for characteristic features of prefrailty. 166 proteins were found to be differentially expressed across frailty statuses, with 15 significantly prefrailty-associated proteins subsequently confirmed by PRM validation. These proteins were functionally enriched in pathways related to cell signaling, protein metabolism, immune regulation, and skeletal muscle function maintenance. A Random Forest model, further assembled from gait speed, skeletal muscle mass, E3-independent E2 ubiquitin-conjugating enzyme (UBE2O), Timed Up and Go test time, alpha-actinin-3 (ACTN3), and Mini-Mental State Examination score, exhibited the most robust performance for early frailty identification among multiple algorithms compared. This exploratory study identified candidate serum protein biomarkers associated with prefrailty. Preliminary machine learning models incorporating UBE2O and ACTN3 suggested the feasibility of discriminating prefrailty from robust status, reflecting underlying proteomic heterogeneity among community-dwelling older adults.
Evidence on the safety and clinical outcomes of endovascular thrombectomy (EVT) in very elderly patients with acute ischemic stroke remains limited, particularly in low- and middle-income countries. To evaluate the safety and clinical outcomes of EVT in patients aged ≥80 years with acute ischemic stroke. This prospective multicenter cohort study was conducted at three major stroke centers in Vietnam between 2024 and 2025. Patients aged ≥80 years with acute ischemic stroke due to anterior circulation large vessel occlusion were enrolled. The primary outcome was functional independence at 90 days. Secondary outcomes included malignant cerebral edema, intracranial hemorrhage (ICH), and mortality. Multivariable logistic regression was used to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs). A total of 370 patients were included (190 EVT, 180 non-EVT). In adjusted analyses, EVT was associated with a higher likelihood of functional independence at 90 days (aOR 2.84, 95% CI 1.38-5.87; p=0.005) and a lower risk of malignant cerebral edema during hospitalization (aOR 0.50, 95% CI 0.27-0.93; p=0.029). EVT was also associated with a higher risk of any intracranial hemorrhage (aOR 4.78, 95% CI 2.90-7.89; p<0.001) and symptomatic intracranial hemorrhage (aOR 4.11, 95% CI 1.42-11.91; p=0.009). Although mortality at 90 days was numerically lower in the EVT group, the association did not reach statistical significance after adjustment (aOR 0.78, 95% CI 0.49-1.24; p=0.29). In very elderly patients with acute ischemic stroke, EVT was associated with improved functional outcomes but an increased risk of intracranial hemorrhage, without a significant increase in mortality. These findings support individualized treatment decision-making regarding EVT in carefully selected patients aged ≥80 years and highlight the importance of greater inclusion of very elderly populations in future clinical trials.