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Background Frailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes. Methods The research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. Results One thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 ( n  = 793) and 2015 ( n  = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887–0.937) and 0.881 (95% CI 0.829–0.934). Hosmer–Lemeshow test values were P  = 0.824 and P  = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. Conclusions Comprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.

Background Subjective memory impairment (SMI) is common in older people. The aim of this study was to investigate the factors influencing SMI among older people in China, with specific focus on the interaction effect of midday napping duration and depressive symptoms on the risk of SMI. Methods Using a dataset representative of the Chinese population from a longitudinal study of health and retirement in China, subjects with SMI were screened using the question “how do you feel about your memory now?” and the Mini-Mental State Examination. A logistic regression model was applied to explore the factors affecting SMI. Additive and multiplicative models were used to analyze the interaction effect of midday napping duration and depressive symptoms on the risk of SMI. Results We enrolled 8,254 subjects included and the incidence of SMI was 63.9%. Depressive symptoms, nap time, and physical activity were influencing factors of SMI. Midday napping duration and depressive symptoms had positive additive interaction effects on the risk of SMI. When extended-length naps and depressive symptoms coexisted, the risk of SMI was 1.06 times greater than that for either alone (RERI, relative excess risk due to interaction = 0.27, 95% CI = 0.07–0.43; AP, attributable proportion = 0.14, 95% CI = 0.01–0.23; S, synergy index = 1.06, 95% CI = 0.57–1.62). When short naps and depressive symptoms coexisted, the risk of SMI was 1.2 times higher than that for either alone (RERI = 0.12, 95% CI=-0.14–0.39; AP = 0.13, 95% CI=-0.07–0.22; S = 1.20, 95% CI = 0.79–1.82). Limitations Since this was a cross-sectional study, the cause-and-effect relationships between the associated variables cannot be inferred. Conclusions The interaction effect that exists between nap time and depressive symptoms in older people is important for the identification and early intervention of people at risk for SMI.

Background Delirium is common among elderly patients in the intensive care unit (ICU) and is associated with prolonged hospitalization, increased healthcare costs, and increased risk of death. Understanding the potential risk factors and early prevention of delirium is critical to facilitate timely intervention that may reverse or mitigate the harmful consequences of delirium. Aim To clarify the effects of pre-admission falls on ICU outcomes, primarily delirium, and secondarily pressure injuries and urinary tract infections. Methods The study relied on data sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Statistical tests (Wilcoxon rank-sum or chi-squared) compared cohort characteristics. Logistic regression was employed to investigate the association between a history of falls and delirium, as well as secondary outcomes, while Kaplan–Meier survival curves were used to assess short-term survival in delirium and non-delirium patients. Results Study encompassed 22,547 participants. Delirium incidence was 40%, significantly higher in patients with a history of falls (54.4% vs. 34.5%, p < 0.001). Logistic regression, controlling for confounders, not only confirmed that a history of falls elevates the odds of delirium (OR: 2.11; 95% CI: 1.97–2.26; p < 0.001) but also showed it increases the incidence of urinary tract infections (OR:1.50; 95% CI:1.40–1.62; p < 0.001) and pressure injuries (OR:1.36; 95% CI:1.26–1.47; p < 0.001). Elderly delirium patients exhibited lower 30-, 180-, and 360-day survival rates than non-delirium counterparts (all p < 0.001). Conclusions The study reveals that history of falls significantly heighten the risk of delirium and other adverse outcomes in elderly ICU patients, leading to decreased short-term survival rates. This emphasizes the critical need for early interventions and could inform future strategies to manage and prevent these conditions in ICU settings.

Background Numerous studies have investigated the mean arterial pressure in patients with sepsis, and many meaningful results have been obtained. However, few studies have measured the systolic blood pressure (SBP) multiple times and established trajectory models for patients with sepsis with different SBP trajectories. Methods Data from patients with sepsis were extracted from the Medical Information Mart for Intensive Care-III database for inclusion in a retrospective cohort study. Ten SBP values within 10 h after hospitalization were extracted, and the interval between each SBP value was 1 h. The SBP measured ten times after admission was analyzed using latent growth mixture modeling to construct a trajectory model. The outcome was in-hospital mortality. The survival probability of different trajectory groups was investigated using Kaplan-Meier (K-M) analysis, and the relationship between different SBP trajectories and in-hospital mortality risk was investigated using Cox proportional-hazards regression model. Results This study included 3034 patients with sepsis. The median survival time was 67 years (interquartile range: 56–77 years). Seven different SBP trajectories were identified based on model-fit criteria. The in-hospital mortality rates of the patients in trajectory classes 1–7 were 25.5%, 40.5%, 11.8%, 18.3%, 23.5%, 13.8%, and 10.5%, respectively. The K-M analysis indicated that patients in class 2 had the lowest probability of survival. Univariate and multivariate Cox regression analysis indicated that, with class 1 as a reference, patients in class 2 had the highest in-hospital mortality risk (P < 0.001). Subgroup analysis indicated that a nominal interaction occurred between age group and blood pressure trajectory in the in-hospital mortality (P < 0.05). Conclusion Maintaining a systolic blood pressure of approximately 140 mmHg in patients with sepsis within 10 h of admission was associated with a lower risk of in-hospital mortality. Analyzing data from multiple measurements and identifying different categories of patient populations with sepsis will help identify the risks among these categories.

Understanding the intricate hydrological interactions between lakes and their surrounding watersheds is pivotal for advancing hydrological research, optimizing water resource management, and informing climate change mitigation strategies. Yet, these complex dynamics are often insufficiently captured in existing hydrological models, such as the bi-direction surface and subsurface flow. To bridge this gap, we introduce a novel lake–watershed coupled model, an enhancement of the Simulator of Hydrological Unstructured Domains. This high-resolution, distributed model employs unstructured triangles as its fundamental hydrological computing units (HCUs), offering a physical approach to hydrological modeling. We validated our model using data from Qinghai Lake in China, spanning from 1979 to 2018. Remarkably, the model not only successfully simulated the streamflow of the Buha River, a key river within the Qinghai Lake basin, achieving a Nash–Sutcliffe efficiency (NSE) of 0.62 and 0.76 for daily and monthly streamflow, respectively, but also accurately reproduced the decrease–increase U-shaped curve of lake level change over the past 40 years, with an NSE of 0.71. Our model uniquely distinguishes the contributions of various components to the lake's long-term water balance, including river runoff, surface direct runoff, lateral groundwater contribution, direct evaporation, and precipitation. This work underscores the potential of our coupled model as a powerful tool for understanding and predicting hydrological processes in lake basins, thereby contributing to more effective water resource management and climate change mitigation strategies.