Utility of cystatin C and serum creatinine-based glomerular filtration rate equations in predicting vancomycin clearance: A population pharmacokinetics analysis in elderly Chinese patients

Renal function is an important factor affecting the pharmacokinetics of vancomycin. The renal function in elderly patients gradually decreases with age. An accurate estimated glomerular filtration rate (GFR) is essential in drug dosing. The study aimed to determine the most appropriate renal function estimation equations to describe vancomycin pharmacokinetics in elderly patients using population pharmacokinetic analysis. Data were obtained retrospectively from elderly patients aged ≥65 years who received vancomycin for infection from September 2016 to January 2022. Renal function was estimated using the Cockcroft-Gault equation (CG), Modification of Diet in Renal Disease equation (MDRD), three Chronic Kidney Disease Epidemiology Collaboration equations (CKD-EPIcys-scr , CKD-EPIscr , and CKD-EPIcys ) and two Berlin Initiative Study equations (BIS-1 and BIS-2). The CKD-EPIcys-scr and BIS-2 equations were based on cystatin C (Cys C) and serum creatinine (Scr). The others were based on Cys C or Scr. A nonlinear mixed effects model (NONMEM) was used to develop the population pharmacokinetic model. A total of 471 serum concentrations from 313 elderly patients were used to develop the population pharmacokinetic model. Weight and GFR were identified as significant covariates affecting the pharmacokinetics of vancomycin. Cys C and Scr-based GFR (CKD-EPIcys-scr and BIS-2) yielded significant improvement performance compared with the other equations in model building. The interindividual variability of CL was reduced from 49.4% to 23.6% and 49.4% to 23.7% in CKD-EPIcys-scr and BIS-2 based models, respectively. However, greater interindividual variabilities of CL (from 26.6% to 29.0%) were represented in the other five models which were based on either Cys C or Scr. The GFR estimated by EPIcys-scr and BIS-2 equations and vancomycin CL exhibited a good correlation (r = 0.834 and 0.833). In the external validation with 124 serum concentrations, the predictive performances of the CKD-EPIcys-scr and BIS-2 based models (the mean relative prediction errors were less than 1%, the mean relative absolute prediction errors were about 23%) were also superior to the other five models (the mean relative prediction errors were about 2%, the mean relative absolute prediction errors were greater than 25%) which are based on either Cys C or Scr. In this study, we determined that the equation used to estimate GFR can affect the population pharmacokinetic model fitting result. Population pharmacokinetics model with CKD-EPIcys-scr or BIS-2 can be used to optimize vancomycin dosage in elderly Chinese patients.

Low albumin-to-fibrinogen ratio predicts adverse clinical outcomes after primary total joint arthroplasty: A retrospective observational investigation

Nutritional markers for adverse clinical outcomes following total joint arthroplasty (TJA) remain controversial. This study attempted to explore the validity of the albumin-to-fibrinogen ratio (AFR) in nutritional assessment and assess its predictive value for adverse postoperative outcomes in patients receiving TJA. 2137 patients who underwent primary TJA between January 2016 and June 2021 were screened. We performed receiver operating characteristic curves and area under the curve (AUC) to assess predictive value and establish optimal thresholds. Multivariate regression models were then used to assess potential associations between AFR and adverse postoperative outcomes. AFR might predict postoperative deep surgical site infections (AUC = 0.699, P = .023). The optimal threshold for wound complications, determined by the Youden index, was 12.96. Compared with patients with reduced AFR, patients with high AFR exhibited an enhanced risk of adverse postoperative outcomes (adjusted OR: 4.010-8.832, all P < .05). Using multivariate Cox regression analysis, we further confirmed a higher risk of adverse postoperative outcomes in patients with low AFR (adjusted HR: 3.733-7.335, all P < .05). Reduced preoperative AFR markedly enhanced adverse postoperative outcomes. Hence, AFR may serve as a potential biomarker for nutritional assessment, and may predict postoperative wound complications following primary TJA.

Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study

Purpose

There remains a lack of a machine learning (ML) model incorporating body composition to assess the risk of bone mineral density (BMD) decreases in type 2 diabetes mellitus (T2DM) patients. We aimed to use ML algorithms and the traditional multivariate logistic regression to establish prediction models for BMD decreases in T2DM patients over 50 years of age, and compare the performance of the two methods.

Patients and Methods

This cross-sectional study was conducted among 450 patients with T2DM from 1 August 2016 to 31 December 2022. The participants were divided into a normal BMD group and a decreased BMD group. Traditional multivariate logistic regression and six ML algorithms were selected to construct male and female models. Two nomograms were constructed to evaluate the risk of BMD decreases in the male and female T2DM patients, respectively. The ML models with the highest area under the curve (AUC) were compared with the traditional multivariate logistic regression models in terms of discriminant ability and clinical applicability.

Results

The optimal ML model was the extreme gradient boost (XGBoost) model. The AUCs of the traditional multivariate logistic regression and the XGBoost models were 0.722 and 0.800 in the male testing dataset, respectively, and 0.876 and 0.880 in the female testing dataset, respectively. The decision curve analysis results suggested that using the XGBoost models to predict the risk of BMD decreases obtained more net benefits compared with the traditional models in both sexes.

Conclusion

We preliminarily proved that the XGBoost models outperformed most other ML models in both sexes and achieved higher accuracy than traditional analyses. Due to the limited sample size in the study, it is necessary to validate our findings in larger prospective cohort studies.

The prognostic value of monocyte-to-lymphocyte ratio in peritoneal dialysis patients

BACKGROUND

The monocyte-to-lymphocyte ratio (MLR) is considered as a new inflammation marker. This study was aimed to investigate the prognostic value of MLR for all-cause mortality and new-onset cardiovascular disease (CVD) events in peritoneal dialysis (PD) patients.

METHODS

This study enrolled patients receiving PD treatment for  ≥ 3 months. Baseline characteristics were obtained within 1 week before PD catheterization. The receiver operating characteristic curve analysis was conducted to determine the optimal cut-off value of MLR. The Kaplan-Meier curve estimated the cumulative survival rate and new CVD free survival rate. Univariate and multivariate Cox regression models were preformed to investigate the association between MLR and clinical outcomes.

RESULTS

A total of 369 PD patients participated in this study. During a median follow-up period of 32.83 months, 65 patients (24.2%) died, and 141 patients (52.4%) occurred new-onset CVD events. The Kaplan-Meier curve revealed that survival rate in high MLR group (MLR  > 0.2168) was significantly lower than in low MLR group (P = 0.008). Patients in high MLR group were more likely to experience CVD events (P = 0.002). Even after adjustment of traditional risk factors, including age, diabetes mellitus, CVD history, smoking, hyperlipidemia, high MLR remained an independent predictor of all-cause mortality [hazard ration (HR) = 2.518, 95% confidence intervals (CI) = 1.020-6.214, P = 0.045] and new-onset CVD events (HR = 1.815, 95% CI = 1.157-2.849, P = 0.010).

CONCLUSIONS

This study suggested that high MLR was significantly and independently associated with all-cause mortality and CVD events in PD patients. The MLR is an inexpensive and straightforward indicator to reflect systemic inflammation status and help clinicians improve PD management.

The predictive value of [18F]FDG PET/CT radiomics combined with clinical features for EGFR mutation status in different clinical staging of lung adenocarcinoma

BACKGROUND

This study aims to construct radiomics models based on [¹⁸F]FDG PET/CT using multiple machine learning methods to predict the EGFR mutation status of lung adenocarcinoma and evaluate whether incorporating clinical parameters can improve the performance of radiomics models.

METHODS

A total of 515 patients were retrospectively collected and divided into a training set (n = 404) and an independent testing set (n = 111) according to their examination time. After semi-automatic segmentation of PET/CT images, the radiomics features were extracted, and the best feature sets of CT, PET, and PET/CT modalities were screened out. Nine radiomics models were constructed using logistic regression (LR), random forest (RF), and support vector machine (SVM) methods. According to the performance in the testing set, the best model of the three modalities was kept, and its radiomics score (Rad-score) was calculated. Furthermore, combined with the valuable clinical parameters (gender, smoking history, nodule type, CEA, SCC-Ag), a joint radiomics model was built.

RESULTS

Compared with LR and SVM, the RF Rad-score showed the best performance among the three radiomics models of CT, PET, and PET/CT (training and testing sets AUC: 0.688, 0.666, and 0.698 vs. 0.726, 0.678, and 0.704). Among the three joint models, the PET/CT joint model performed the best (training and testing sets AUC: 0.760 vs. 0.730). The further stratified analysis found that CT_RF had the best prediction effect for stage I-II lesions (training set and testing set AUC: 0.791 vs. 0.797), while PET/CT joint model had the best prediction effect for stage III-IV lesions (training and testing sets AUC: 0.722 vs. 0.723).

CONCLUSIONS

Combining with clinical parameters can improve the predictive performance of PET/CT radiomics model, especially for patients with advanced lung adenocarcinoma.

Evaluating renal iron overload in diabetes mellitus by blood oxygen level-dependent magnetic resonance imaging: a longitudinal experimental study

BACKGROUND

Iron overload plays a critical role in the pathogenesis of diabetic nephropathy. Non-invasive evaluation of renal iron overload in diabetes in the management and intervention of diabetic nephropathy is of great significance. This study aimed to explore the feasibility of blood oxygen level-dependent (BOLD) magnetic resonance imaging (MRI) in evaluating renal iron overload in diabetes using a rabbit model.

METHODS

The rabbits were randomly divided into control, iron-overload (I), diabetes (D), and diabetes with iron-overload (DI) groups (each n = 19). The diabetes models were generated by injecting intravenous alloxan solution, and the iron-overload models were generated by injecting intramuscular iron-dextran. BOLD MRI was performed immediately (week 0) and at week 4, 8, and 12 following modeling. The differences in renal cortex (CR2*) and outer medulla R2* (MR2*) and the ratio of MR2*-CR2* (MCR) across the different time points were compared.

RESULTS

Iron was first deposited in glomeruli in the I group and in proximal tubular cells in renal cortex in the D group. In the DI group, there was iron deposition in both glomeruli and proximal tubular cells at week 4, and the accumulation increased subsequently. The degree of kidney injury and iron overload was more severe in the DI group than those in the I and D groups at week 12. At week 8 and 12, the CR2* and MR2* in the DI group were higher than those in the I and D groups (all P < 0.05). The MCR in the I, D, and DI groups decreased from week 0 to 4 (all P < 0.001), and that in the I group increased from week 8 to 12 (P = 0.034). CR2* and MR2* values displayed different trends from week 0-12. Dynamic MCR curves in the D and DI groups were different from that in the I group.

CONCLUSION

It presents interactions between diabetes and iron overload in kidney injury, and BOLD MRI can be used to evaluate renal iron overload in diabetes.

LncRNA LUADT1 regulates miR-34a/SIRT1 to participate in chondrocyte apoptosis

It is known that miR-34a can promote the apoptosis of chondrocytes, which directly contribute to osteoarthritis (OA). Through bioinformatics analysis, we found that long noncoding RNA LUADT1 may interact with miR-34a. We, therefore, further investigate the interactions between them in osteoarthritis. We found that LUADT1 was downregulated, while miR-34a was upregulated in OA synovial fluid. Correlation analysis revealed no significant correlation between them. Overexpression experiment also revealed no significant effects of LUADT1 and miR-34a on the expression of each other. However, the dual-luciferase assay showed that LUADT1 and miR-34a can directly interact with each other. Moreover, LUADT1 overexpression led to the upregulation of SIRT1, which is a downstream target of miR-34a. Cell apoptosis showed that LUADT1 and SIRT1 overexpression led to decreased, while miR-34a led to increased apoptotic rates of chondrocytes. Therefore, LUADT1 regulates miR-34a/SIRT1 to participate in chondrocyte apoptosis.