Clinical Interventions in Aging最新文献

Background/purpose: Older people with cognitive impairment (CI) are at significantly higher fall risk compared to those without CI. Their care partners' engagement is critical to facilitate their participation and adherence in fall risk management (FRM) programs. This systematic review aims to synthesize terms and measures of care partner engagement (CPE) in FRM programs for community-dwelling older people with CI, facilitators and barriers to CPE, and promising CPE enhancement strategies.

Methods: We conducted a systematic search of eight databases and included relevant literature published between 1985 and 2024 through a manual search. Guided by a conceptual framework of CPE informed by existing literature, we conducted content analysis and thematic synthesis to address our research aims. We assessed the quality of included studies using the Mixed Methods Appraisal Tool.

Results: Thirty-two studies were included in the synthesis. There was substantial heterogeneity of CPE terms and measures. CPE facilitators and barriers were summarized under three categories: older people with CI (eg, interest, health, and functional statuses), care partners (eg, motivation, perceived burden, caring relationships), and service providers or programs (eg, supportive instructors, service disruptions). CPE enhancement strategies (eg, tailored intervention content, provision of professional and social support) were summarized, with some (eg, using a discussion tool, providing flexible schedules) showing promising effects on CPE.

Conclusion: Our review synthesized the common practice of CPE in FRM programs for community-dwelling older people with CI and introduced a novel conceptual framework to clarify the multidimensional nature of CPE. Our findings emphasized the urgent need to develop consistent language and validated measures for describing and assessing CPE. This review has also identified important considerations, including facilitators, barriers, and promising strategies to enhance CPE in these programs, informing the development of effective care-partner-engaged FRM programs for older people with CI.

With the global aging population, the impact of aging on various organ systems is becoming increasingly significant. The gastrointestinal tract, a key site of immune activity and microbial colonization, undergoes functional decline that is closely associated with a range of intestinal and systemic diseases. While aging-related fibrosis has been extensively studied in organs such as the lungs, liver, heart, and kidneys, its role in intestinal fibrosis remains underexplored. This review discusses mechanisms by which aging may promote or increase the risk of intestinal fibrosis, including immunosenescence, cellular senescence, gut microbiota dysbiosis, and dysregulated growth factor signaling. Additionally, both traditional and emerging therapeutic strategies are summarized to guide future interventions.

Background: Long-term bedridden elderly individuals face a high risk of community-acquired pneumonia (CAP) amid China's aging population, yet the specific mortality impact remains insufficiently studied.

Objective: To compare the clinical characteristics and in-hospital mortality between long-term bedridden and ambulatory elderly patients with CAP, and to identify risk factors for mortality.

Methods: This retrospective study included 453 patients aged ≥75 years hospitalized with CAP from March 2016 to March 2019, divided into a bedridden group (n = 162) and a non-bedridden group (n = 291). Data on demographics, comorbidities, frailty (modified Frailty Index-5, mFI-5), functional status (Barthel Index), and laboratory parameters (eg, hs-CRP) were collected. Logistic regression analysis was used to identify predictors of in-hospital mortality.

Results: The bedridden group had a significantly higher mortality rate (27.16% vs 2.06%, P < 0.001) and elevated hs-CRP levels (40.2 ± 44.0 mg/L vs 19.9 ± 20.3 mg/L). Multivariate analysis identified bedridden status (OR = 11.99, 95% CI: 4.31-33.40), respiratory failure (OR = 6.80, 95% CI: 3.03-15.28), and renal dysfunction (elevated serum creatinine; OR = 1.01, 95% CI: 1.00-1.02) as independent risk factors for mortality.

Conclusion: Long-term bedridden status is an independent predictor of in-hospital mortality in elderly CAP patients with inflammatory response potentially playing a critical role.

Purpose: The Saga Fall-related Injury Risk Model (SFIRM) was developed in an acute care hospital to predict fall-related injuries based on six factors upon admission: age, sex, emergency transport, medical referral letters, history of falls, and bedriddenness ranks. This study aims to validate the applicability of the model across various hospitals through external validation using data from multiple hospitals. Additionally, the common predictors of fall-related injuries across these hospitals were explored.

Patients and methods: This multicenter, retrospective, observational study included patients aged 20 years and older who were admitted to 8 hospitals (chronic-care, acute-care, and tertiary acute-care) between April 2018 and March 2021. A calculated sample size of patients was selected and the area under the curve (AUC) of the SFIRM was determined for fall-related injuries during hospitalization. Multivariate analyses were conducted for each hospital using the surveyed factors as covariates and fall-related injuries as outcomes. The significant factors associated with fall-related injuries were compared across hospitals.

Results: From 144,777 patients, 2376 were randomly sampled and analyzed. Among them, 51 patients (2.1%) experienced falls during hospitalization and 35 (1.5%) sustained fall-related injuries. The AUC of SFIRM was 0.617 (95% confidence interval 0.534-0.701). In multivariate analyses by hospital, age and bedriddenness ranks were significantly associated with fall-related injuries in five hospitals, whereas male sex, history of falls, and diabetes were significantly associated with fall-related injuries in four hospitals.

Conclusion: The SFIRM demonstrated low discrimination in a population from various hospitals. The predictive models for fall-related injuries require redevelopment and validation to suit various hospitals. In the multivariate analyses across hospitals, age, bedriddenness ranks, male sex, history of falls, and diabetes mellitus were common and significant factors associated with fall-related injuries. These factors are most favorable for developing a predictive model for fall-related injuries.

Objective: This study aimed to identify the key risk factors for hypokalemia in older adults with acute cerebral hemorrhage (ACH) and to develop a clinically practical risk predictive model based on logistic regression.

Methods: A total of 209 older adult ACH patients (age 60-82 years) treated at The First Hospital of Hebei Medical University from July 2022 to July 2024 were included in this retrospective cohort study. Patients were divided into two groups: hypokalemic (serum potassium < 3.5 mmol/L, n = 56) and normokalemic (serum potassium 3.5-5.5 mmol/L, n = 153). Clinical outcomes were compared, and logistic regression was used to identify risk factors for hypokalemia. A risk prediction model was constructed and presented as a nomogram. The diagnostic value of the model was assessed using receiver operating characteristic (ROC) curves.

Results: Hypokalemia was associated with significantly higher in-hospital mortality, poorer functional outcomes, longer hospital stays, and more frequent neurological deterioration (all P < 0.05). Univariate and multivariate logistic regression identified female gender (OR=2.713), higher NIHSS scores at admission (OR=2.375), GFR ≤ 60 mL/min/1.73 m² (OR=2.316), and furosemide use > 20 mg/d (OR=2.351) as independent risk factors for hypokalemia. ROC analysis showed an area under the curve (AUC) for the multivariable predictive model of 0.859, which was superior to individual predictors.

Conclusion: Female gender, higher neurological deficit severity (NIHSS score), impaired renal function (GFR ≤ 60 mL/min/1.73 m²), and use of furosemide > 20 mg/d are significant independent risk factors for hypokalemia in older adult ACH patients. Given its association with adverse outcomes, early prediction is crucial. The predictive model and corresponding nomogram provide a practical tool for identifying high-risk patients, facilitating timely intervention.

Purpose: To compare the medical accuracy and content comprehensiveness of three large language models (LLMs) in generating responses to frequently asked osteoporosis-related questions and to determine their potential role in clinical support.

Methods: Twenty-five questions covering six clinical domains were submitted to each model in isolated sessions. Five senior orthopedic physicians, each with over 25 years of clinical experience, independently rated the medical accuracy of each response using a 5-point Likert scale. Responses rated as "acceptable" or above were further evaluated for content comprehensiveness. Statistical analysis included the Kruskal-Wallis test and Dunn's post hoc test with Bonferroni correction.

Results: A total of 75 unique responses (25 questions × 3 models) were evaluated by five orthopedic experts, yielding 375 ratings. ChatGPT-4o achieved the highest accuracy score (median: 4.6; IQR: 4.4-4.8), significantly outperforming Gemini-2.5 Pro (p=0.039) and DeepSeek-R1 (p<0.001). For content comprehensiveness, both ChatGPT-4o and Gemini-2.5 Pro had a median score of 4.4, higher than DeepSeek-R1 (median: 4.2), though differences did not reach statistical significance (p=0.0536). Gemini-2.5 Pro was noted for its fluent and user-friendly language but lacked clinical depth in some responses. DeepSeek-R1, despite offering source citations, demonstrated greater inconsistency.

Conclusion: LLMs have clear potential as tools for patient education in osteoporosis. ChatGPT-4o demonstrated the most balanced and clinically reliable performance. Nonetheless, expert medical oversight remains essential to ensure safe and context-appropriate use in healthcare settings.

Purpose: To utilize the developed nomogram for evaluating the risk of recurrence in non-valvular atrial fibrillation (NVAF) patients after radiofrequency catheter ablation (RFCA) and compare the model's performance with the APPLE, ATLAS, and Antwerp scores.

Patients and methods: 242 patients with NVAF requiring RFCA were enrolled. These patients were randomly divided into a training cohort (n=169) and a validation cohort (n=73) according to 7:3. A nomogram was developed based on LAVI, RAVI, SII, NYHA classification, CHA₂DS₂-VASc score to estimate the risk of AF recurrence after RFCA. The APPLE, ATLAS, and Antwerp scores were calculated using the "pROC" package in R software. The AUC value of the nomogram compared with each of the three scores was evaluated using the DeLong test. The integrated discrimination improvement and net reclassification index were calculated to compare the predictive performance of the nomogram against the scores in R software.

Results: The nomogram achieved significantly higher values with an AUC of 0.837 (95% CI: 0.774-0.899) in the training cohort and 0.895 (95% CI: 0.823-0.968) in the validation cohort (all P < 0.05) than the three scores. It also achieved better positive and negative predictive values, indicating enhanced discriminatory power. By integrating multidimensional parameters and optimizing risk stratification, it significantly reduced misjudgment rates. Furthermore, the model demonstrated a more balanced sensitivity-specificity profile and greater predictive stability than single-dimensional scores. It also provides more robust clinical decision support for predicting post-RFCA recurrence across diverse datasets.

Conclusion: The APPLE, ATLAS, and Antwerp scores all demonstrated effectiveness in predicting AF recurrence after RFCA in patients with NVAF. Among these established scoring systems, the APPLE score showed better performance compared to the other two. More importantly, our newly developed nomogram exhibited superior performance compared to all three existing scores, demonstrating a marked improvement in predicting the risk of AF recurrence. While our model represents a promising tool, it is still in the preliminary stage and requires further validation in larger, multi-center, prospective cohorts to confirm its generalizability.

Objective: To develop an estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio (ACR) risk stratification for rapid kidney function decline across aging phenotypes in older adults.

Methods: We included 1539 older adults (486 healthy aging, 661 aging with comorbidities, 392 aging with CKD) from the Rugao Longevity and Aging Study and Huashan Hospital. Rapid decline was defined as a ≥30% decrease in eGFR over 2 years. We estimated adjusted incidence of rapid decline across baseline eGFR (≥90, 75-<90, 60-<75, <60 mL/min/1.73 m²) and ACR (<30 vs ≥30 mg/g) categories within each aging phenotype. We defined adjusted incidence rate of <5%, 5-7.5%, 7.5-15%, and >15% as no risk, low risk, moderate risk, and high risk, respectively. Random forests assessed the relative contribution of pre-specified eGFR and ACR categories.

Results: Mean ages were 77.7 ± 4.4, 78.0 ± 4.1, and 77.7 ± 5.5 years in healthy, comorbidity, and CKD cohort, respectively. Among healthy participants, the adjusted incidence remained in low risk when eGFR was between 60 and 75 mL/min/1.73 m², but increased to moderate risk when eGFR <60 mL/min/1.73 m². In the comorbidity cohort, a low risk classification was observed with ACR <30 mg/g and eGFR ≥75 mL/min/1.73 m², or with ACR ≥30 mg/g and eGFR ≥90 mL/min/1.73 m², other combinations were associated with moderate risk. In the CKD cohort, moderate risk corresponded to ACR <30 mg/g with eGFR ≥60 mL/min/1.73 m² or ACR ≥30 mg/g with eGFR ≥75 mL/min/1.73 m², while all other scenarios were classified as high risk. Random forest results corroborated that eGFR dominated discrimination in healthy aging, whereas ACR carried greater weight in comorbidity and CKD cohorts.

Conclusion: Phenotype-specific eGFR-ACR thresholds provide pragmatic risk stratification to guide targeted monitoring and earlier intervention in older adults.

Background: Dysregulation of microRNAs contributes to bone diseases. However, the microRNAs involved in primary hyperparathyroidism (PHPT)-induced osteoporosis remain unknown.

Methods: The parathyroid tissue samples were obtained from PHPT patients with or without osteoporosis (n = 5/group) during parathyroid resection and subjected to high throughput microRNA sequencing. The differentially expressed microRNAs were identified and further verified using qRT-PCR. Alizarin Red Staining was performed to detected the osteogenic differentiation. Gain- and loss-of-function assays were performed to investigate the role of miR-874-3p, which was upregulated in PHPT patients with osteoporosis, in human mesenchymal stem cells (hMSCs) undergoing osteoblastic differentiation.

Results: We identified 32 significantly upregulated and 18 significantly downregulated microRNAs in PHPT patients with osteoporosis. miR-874-3p was increased in PHPT osteoporosis patients, meanwhile, miR-874-3p in parathyroid tissue and peripheral blood extracellular vesicles of PHPT osteoporosis mice were increased. The miR-874-3p level was remarkably elevated in hMSCs grown in osteogenic medium. Overexpression of miR-874-3p repressed the hMSC osteogenic differentiation and reduced the osteogenic marker expression in hMSCs, whereas miR-874-3p inhibitor showed a contrasting effect. The results of the dual luciferase reporting system showed that miR-874-3p could reduce the luciferase activity of wild-type FTO-WT-3 '-UTR. However, there was no significant change in the luciferase activity of the mutant compared with the control group.

Conclusion: MiR-874-3p might specifically binds to FTO suppress osteogenic differentiation of hMSCs, thereby contributing to the development of osteoporosis in PHPT patients.

Background: Coronary artery bypass grafting (CABG) is key for severe coronary artery disease, but postoperative acute kidney injury (AKI) may increase mortality and prolong hospital stays. Reliable models for early prediction of post-CABG AKI remain lacking.

Methods: Data of 520 CABG patients (September 2021-December 2024) from the Affiliated Hospital of Xuzhou Medical University were collected, and the patients were divided into a training group (70%, for model building) and a validation group (30%). Key variables were screened through Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by the construction of six machine learning models: Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Light Gradient Boosting Machine (LightGBM), Softmax Regression, and Support Vector Machine (SVM). The SHapley Additive exPlanations (SHAP) was used to quantify feature importance.

Results: The incidence of post-CABG AKI was 25.96%, and the median age of patients in the AKI group was significantly higher than that in the non-AKI group (66.09 ± 8.15 vs 64.32 ± 7.76, p = 0.025). In the training group, the XGBoost model using the top 5 important variables outperformed other models (Area Under the Curve [AUC] = 0.89, 95% Confidence Interval [CI]: 0.86-0.91), followed by the LightGBM model using the top 5 important variables and the RF model using the top 5 important variables (both had an AUC of 0.88; 95% CI: 0.85-0.90 and 0.85-0.91, respectively). In the validation group, the LR model using the top 15 important variables and the Softmax Regression model using the top 15 important variables maintained the highest stability (both had an AUC of 0.86, 95% CI: 0.79-0.92). SHAP analysis confirmed that estimated glomerular filtration rate (eGFR), intraoperative epinephrine use and calcium levels were the top three predictive factors.

Conclusion: The machine learning models constructed in this study can effectively predict post-CABG AKI, facilitating early identification of high-risk patients.