Chronic inflammation and metabolic dysregulation are key drivers of cardiovascular disease (CVD). Although the triglyceride-glucose (TyG) index and C-reactive protein (CRP) have each been associated with cardiovascular risk, their combined use as a C-reactive protein-triglyceride-glucose index (CTI) may improve risk prediction. This study aimed to investigate the association between the CTI and CVD, with particular focus on various population subgroups, and to evaluate its predictive capacity for incident CVD. This investigation utilized data from the China Health and Retirement Longitudinal Study (CHARLS) cohort, which included 8084 middle-aged and older participants. The association between the CTI and the incidence of CVD was examined using Kaplan-Meier survival analysis, multivariable Cox proportional hazards regression models, and restricted cubic splines (RCS). The predictive performance of CTI was evaluated by receiver operating characteristic (ROC) curve analysis, along with net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Furthermore, subgroup and sensitivity analyses were performed to test the robustness of the results. During follow-up, 2,133 participants (26.39%) developed CVD. After adjustment for potential confounders, each one-unit increase in the CTI was associated with a 44% higher risk of CVD (HR = 1.44, 95% CI: 1.17–1.78). Compared with the lowest quartile, higher quartiles of CTI were associated with a stepwise increase in risk (HR = 1.23, 1.29, and 1.40; P for trend < 0.001). Subgroup analyses indicated stronger associations among participants aged 45–60 years and those who were married. RCS analyses further supported an overall linear relationship between CTI and CVD risk. Moreover, the CTI index improved reclassification metrics (NRI/IDI) and discriminative ability (AUC). Sensitivity analyses corroborated these primary findings. The CTI, as a composite biomarker reflecting integrated metabolic and inflammatory status, demonstrated a significant positive association with CVD risk. Furthermore, this association exhibited variations across age groups and marital status, indicating the potential utility of CTI as a novel and clinically relevant biomarker for CVD risk stratification.

The pharmacokinetics of vancomycin exhibit significant inter-individual variability, particularly among elderly patients. This study aims to develop a predictive model that integrates machine learning with population pharmacokinetics (popPK) to facilitate personalized medication management for this demographic. A retrospective analysis incorporating 33 features, including popPK parameters such as clearance and volume of distribution. A combination of multiple algorithms and Shapley Additive Explanations was utilized for feature selection to identify the most influential factors affecting drug concentrations. The performance of each algorithm with popPK parameters was superior to that without popPK parameters. Our final ensemble model, composed of support vector regression, light gradient boosting machine, and categorical boosting in a 6:3:1 ratio, included 16 optimized features. This model demonstrated superior predictive accuracy compared to models utilizing all features, with testing group metrics including an R2 of 0.656, mean absolute error of 3.458, mean square error of 28.103, absolute accuracy within ± 5 mg/L of 81.82%, and relative accuracy within ± 30% of 76.62%. This study presents a rapid and cost-effective predictive model for estimating vancomycin plasma concentrations in elderly patients. The model offers a valuable tool for clinicians to accurately determine effective plasma concentration ranges and tailor individualized dosing regimens, thereby enhancing therapeutic outcomes and safety.

Background: Optimizing immunosuppression based on immunological risk profiles remains the preferred regimen for improving long-term graft outcomes after renal transplantation. Tacrolimus (TAC), the cornerstone drug of the calcineurin inhibitors, is currently the most commonly used immunosuppressant for renal transplantation. Under-exposure can lead to rejection events, while overexposure has been shown to cause drug-related renal injury. This study aims to assess the impact of TAC exposure on graft renal function to identify high-risk individuals of post-transplantation. Methods: The present study employed a single-center retrospective cohort design to enroll adult recipients who underwent allogeneic kidney transplantation from 1 January 2019 to 31 December 2023. Cox regression analysis was used to analyze the independent risk effects of exposure parameters, which included mean, median, standard deviation (STD) and coefficient of variation (CV). Hereafter, the Kaplan-Meier method was applied to perform the survival analysis of stabilized graft renal function. Results: 69 cases (19.22%) in the study cohort developed graft renal insufficiency. And a STD of TAC target trough concentration≥5 was associated with a 4.55-fold elevated risk (95% CI 1.60-12.95, p = 0.004). Recipients in the higher CV had an increased risk of graft renal insufficiency in comparison with those in low CV tertile. As indicated by the survival analysis curves, it was determined that TAC mean trough concentrations >8 ng/mL, CV>0.5 and TTR<0.3 were associated with a higher risk of graft renal insufficiency. Conclusion: Higher TAC trough concentrations, higher CV, and lower TTR post-transplantation significantly increases the risk of transplant renal insufficiency. Identifying these high-risk transplant recipients, correcting potentially influential factors and keeping TAC within recommended concentrations will significantly improve the outcomes and survival of renal transplant recipients.

Objective: The pathophysiology and disease status of critically ill patients have a significant impact on the pharmacokinetics/pharmacodynamics (PK/PD) of antimicrobial agents. However, the effect of fungal co-infection on the plasma trough concentration (C_min) of teicoplanin in critically ill patients remains unclear. Materials and Methods: A retrospective cohort study was carried out. Clinical data of patients admitted to the intensive care unit and receiving teicoplanin therapeutic drug monitoring were collected. Multiple linear stepwise regression analysis and binary logistic regression analysis were used to identify the factors influencing teicoplanin C_min and the achievement of the target C_min (≥ 15.0 μg/mL). Results: A total of 404 teicoplanin C_min values from 231 patients were included. The mean teicoplanin C_min was 20.63 ± 10.40 μg/mL, and the percentage of C_min > 30.0 μg/mL was 15.8%. In the multivariate analysis, fungal co-infection was identified as an independent factor affecting teicoplanin C_min (B=4.056, 95% CI 2.089– 6.023; p< 0.001) and the attainment of the target C_min (OR=3.233, 95% CI 1.538– 6.795; p=0.002). Sex, weight, teicoplanin dose, levels of direct bilirubin, blood urea nitrogen, estimated glomerular filtration rate, and uric acid were also found to be influencing factors. Patients with fungal co-infection had a higher teicoplanin C_min (p< 0.001) and a higher percentage of C_min > 30.0 μg/mL (20.3% vs 12.0%; p=0.022) compared to those without, despite similar teicoplanin doses (p=0.302). The percentage of patients receiving continuous renal replacement therapy was higher in the fungal co-infection cohort (p=0.016), along with an older age and a lower body weight. Conclusion: For critically ill patients with fungal co-infections, the teicoplanin dose should be decreased, or at least not increased. This is essential for reducing the potential risk of toxicity and customizing dosing strategies to meet individual patient needs. A large-scale, multi-center, prospective study is necessary to confirm the findings related to this dosing approach.