Potential life-years gained over a 5-year period by correcting DOPPS-identified modifiable practices in haemodialysis: results from the European MONITOR-CKD5 study

Anemia biomarkers and mortality in hemodialysis patients with or without diabetes: A 10-year follow-up study

BACKGROUND

Many studies have assessed the association between anemia and mortality in hemodialysis but few compared patients with and without diabetes. Our study aims to investigate the impact of hemoglobin and iron parameters on mortality in hemodialysis patients with or without diabetes.

METHODS

This is a two-center retrospective study that included all adult patients who started hemodialysis between February 2012 and February 2020, followed until January 2021. Averages of hemoglobin, ferritin and transferrin saturation of entire follow-up were recorded. Kaplan Meier survival, log rank test and cox regression analyses were performed to assess the association between anemia biomarkers and mortality.

RESULTS

A total of 214 patients were included. Mean age was 67.98 ±12.41 years, mean hemoglobin was 10.92 ±0.75 g/dL, mean ferritin was 504.43 ± 221.42 ng/mL and mean transferrin saturation was 26.23 ±7.77%. Log rank test showed an association between hemoglobin ≥11 g/dL and better survival in patients without diabetes (P = 0.028). Based on cox regression analysis, hemoglobin was associated with all-cause mortality in all patients (HR = 0.66; CI:0.49,0.89; P = 0.007). When comparing patients with and without diabetes, this association remained significant only in patients without diabetes (HR = 0.53; CI:0.37,0.77; P<0.001). Based on different multivariate models, hemoglobin, ferritin and age were independent factors associated with mortality in patients without diabetes.

CONCLUSIONS

This study showed that hemoglobin ≥11 g/dL is associated with better survival in hemodialysis patients without diabetes but not in those with diabetes. These differences need to be further explored in other countries and settings. An individualization of the hemoglobin target level might be necessary to improve patients' outcomes.

[Diagnosis and treatment of catheter-related bloodstream infection in hemodialysis: 10 years later]

Patients in hemodialysis on central venous catheter as vascular access are at risk of infections. Catheter-related bloodstream infection is one of the most serious catheter-complications in hemodialysis patients. Its clinical and microbiological diagnosis is challenging. The implementation of empiric antibiotic therapy is based on old recommendations proposing the combination of a molecule targeting methicillin-resistant Staphylococcus aureus and a betalactamin active on P. aeruginosa, and also adapting this probabilistic treatment by carrying out a microbiological register on a local scale, which is rarely done. In our hemodialysis center at Bordeaux University Hospital, an analysis of the microorganisms causing all catheter-related bloodstream infection over the period 2018-2020 enabled us to propose, in agreement with the infectious disease specialists, an adapted probabilistic antibiotic therapy protocol. This approach allowed us to observe a low incidence of meticillinoresistance of Staphylococcus. For catheters inserted more than 6 months ago, we observed no Staphylococcus, no multi-resistant Pseudomonas, and only 2% of Enterobacteria resistant to cephalosporins. A frequent updating of the microbiological epidemiology of catheter-related bloodstream infection, in partnership with the infectious diseases team in each hemodialysis center, allowing an adaptation of the probabilistic antibiotic therapy, and seems to have a good feasibility. This strategy might favor the preservation of microbial ecology on an individual and collective scale in maintenance hemodialysis patients.

An effective model for predicting serum albumin level in hemodialysis patients

Patients on hemodialysis (HD) are known to be at an increased risk of mortality. Hypoalbuminemia is one of the most important risk factors of death in HD patients, and is an independent risk factor for all-cause mortality that is associated with cardiac death, infection, and Protein-Energy Wasting (PEW). It is a clinical challenge to elevate serum albumin level. In addition, predicting trends in serum albumin level is effective for personalized treatment of hypoalbuminemia. In this study, we analyzed a total of 3069 records collected from 314 HD patients using a machine learning method that is based on an improved binary mutant quantum grey wolf optimizer (MQGWO) combined with Fuzzy K-Nearest Neighbor (FKNN). The performance of the proposed MQGWO method was evaluated using a series of experiments including global optimization experiments, feature selection experiments on open data sets, and prediction experiments on an HD dataset. The experimental results showed that the most critical relevant indicators such as age, presence or absence of diabetes, dialysis vintage, and baseline albumin can be identified by feature selection. Remarkably, the accuracy and the specificity of the method were 98.39% and 96.77%, respectively, demonstrating that this model has great potential to be used for detecting serum albumin level trends in HD patients.