Visit-to-visit ultrafiltration volume variability predicts all-cause mortality in patients receiving hemodialysis

AbstractPurpose Little is known about the effect of visit-to-visit ultrafiltration volume (UV) variability on the outcome. In this study, we investigated the association between visit-to-visit UV variability and all-cause mortality in patients receiving hemodialysis (HD).Methods We consecutively enrolled patients who received maintenance HD in our center from March 2015 to March 2021. UV variability was defined using standard deviation (UVSD) and coefficient of variation (UVCV) (standard deviation divided by the mean). The relationship between UV variability and all-cause mortality was assessed using univariate and multivariate Cox proportional hazard regression models. Receiver operating characteristic curves were used to evaluate the predictive abilities of UVSD and UVCV for short-term and long-term survival rates.Results A total of 283 HD patients were included. The mean age was 57.54 years, and 53% were males. Follow-up was done for a median of 3.38 years (IQR 1.83–4.78). During the follow-up period, 73 patients died. Cox proportional hazards models indicated that UVSD and UVCV (higher versus lower) were positively associated with all-cause mortality (p=.003 and p<.001, respectively), while in multivariable-adjusted models, only higher UVCV remained significantly associated with all-cause mortality in patients receiving HD (HR 2.55 (95% CI 1.397–4.654), p=.002). Moreover, subgroup analyses showed that the predictive performance of UVCV was more accurate among older patients, males and patients with comorbidities.Conclusions Visit-to-visit UV variability, especially UVCV, is a helpful indicator for predicting all-cause mortality in patients receiving HD, especially for older patients, males and those with comorbidities

Image_3_Comprehensive analysis of cellular senescence-related genes in the prognosis, tumor microenvironment, and immunotherapy/chemotherapy of clear cell renal cell carcinoma.jpeg

BackgroundThe transcriptome public database and advances in biological discoveries contributed to significant progresses in identifying the drivers of cancer progression. Cellular senescence (CS) is considered as a leading factor resulting in cancer development. The purpose of this study was to explore the significance of CS-related genes in the molecular classification and survival outcome of clear cell renal cell carcinoma (ccRCC).MethodsCS-related genes were obtained from the CellAge database, and patients from TCGA-KIRC dataset and ICGC dataset were clustered by ConsesusClusterPlus. The characteristics of overall survival (OS), genomic variation, and tumor microenvironment (TME) of each cluster were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was conducted to develop a CS-related risk model to score ccRCC patients and assess the risk scores in predicting patients’ response to immunotherapy and chemotherapy. A nomogram based on the risk model was established to improve the risk stratification of patients.ResultsCcRCC was divided into three molecular subtypes based on CS-related genes. The three molecular phenotypes showed different OS and clinical manifestations, mutation patterns, and TME states. Five genes were obtained from nine differentially expressed CS-related genes in the three molecular subtypes to develop a risk model. Patients with ccRCC were divided into high- and low-risk subgroups. The former showed an unfavorable OS, with a significantly higher genomic variation rate, TME score, and numerous immune checkpoint expressions when compared to the low-risk subgroup. Risk score reflected the response of patients to axitinib, bortezomib, sorafenib, sunitinib, and temsirolimus.ConclusionsIn general, CS-related genes divided ccRCC into three molecular subtypes with distinct OS, mutation patterns, and TME states. The risk model based on the five CS-related genes can predict the prognosis and therapeutic outcome of ccRCC patients, providing a theoretical basis for further study on the molecular mechanism of CS-related ccRCC.</p

Image_4_Comprehensive analysis of cellular senescence-related genes in the prognosis, tumor microenvironment, and immunotherapy/chemotherapy of clear cell renal cell carcinoma.jpeg

BackgroundThe transcriptome public database and advances in biological discoveries contributed to significant progresses in identifying the drivers of cancer progression. Cellular senescence (CS) is considered as a leading factor resulting in cancer development. The purpose of this study was to explore the significance of CS-related genes in the molecular classification and survival outcome of clear cell renal cell carcinoma (ccRCC).MethodsCS-related genes were obtained from the CellAge database, and patients from TCGA-KIRC dataset and ICGC dataset were clustered by ConsesusClusterPlus. The characteristics of overall survival (OS), genomic variation, and tumor microenvironment (TME) of each cluster were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was conducted to develop a CS-related risk model to score ccRCC patients and assess the risk scores in predicting patients’ response to immunotherapy and chemotherapy. A nomogram based on the risk model was established to improve the risk stratification of patients.ResultsCcRCC was divided into three molecular subtypes based on CS-related genes. The three molecular phenotypes showed different OS and clinical manifestations, mutation patterns, and TME states. Five genes were obtained from nine differentially expressed CS-related genes in the three molecular subtypes to develop a risk model. Patients with ccRCC were divided into high- and low-risk subgroups. The former showed an unfavorable OS, with a significantly higher genomic variation rate, TME score, and numerous immune checkpoint expressions when compared to the low-risk subgroup. Risk score reflected the response of patients to axitinib, bortezomib, sorafenib, sunitinib, and temsirolimus.ConclusionsIn general, CS-related genes divided ccRCC into three molecular subtypes with distinct OS, mutation patterns, and TME states. The risk model based on the five CS-related genes can predict the prognosis and therapeutic outcome of ccRCC patients, providing a theoretical basis for further study on the molecular mechanism of CS-related ccRCC.</p

Effectiveness of vaccination in reducing hospitalization and mortality rates in dialysis patients with Omicron infection in China: A single-center study

The impact of vaccination on the outcomes of dialysis patients with Omicron infections in China remains unknown. This study aimed to examine the relationship between vaccination and hospitalization as well as all-cause mortality. We included patients who had undergone maintenance hemodialysis (HD) for at least three months at our center. The follow-up period spanned from December 2022 to February 2023. We assessed the connections between vaccination and hospitalization as well as all-cause mortality using univariable and multivariable logistic regression models. Receiver operating characteristic (ROC) curves were used to assess the diagnostic accuracy for hospitalization and all-cause mortality. Ultimately, a total of 427 HD patients with confirmed SARS-CoV-2 infections were included. The patients had a mean age of 54 years, and 59.4% of them were male. Prior to the investigation, 108 patients had received vaccinations, with 81 of them having completed or received booster vaccinations. Throughout the follow-up period, 81 patients were admitted to the hospital, and 39 patients died. Multivariable logistic regression revealed that vaccination significantly decreased all-cause mortality (OR 0.25, 95% CI 0.07–1.94, P = .04). Moreover, completed or booster vaccinations were effective in reducing the hospitalization rate (OR 0.41, 95%CI 0.17–0.99, P = .047). It is noteworthy that both unvaccinated and vaccinated individuals experienced mild symptoms, and the hospitalization rates were relatively low in both groups. Despite the reduced pathogenicity of Omicron compared to previous strains in dialysis patients, both vaccinated and unvaccinated, vaccination still provides benefits for improving the prognosis

Image_2_Comprehensive analysis of cellular senescence-related genes in the prognosis, tumor microenvironment, and immunotherapy/chemotherapy of clear cell renal cell carcinoma.jpeg

BackgroundThe transcriptome public database and advances in biological discoveries contributed to significant progresses in identifying the drivers of cancer progression. Cellular senescence (CS) is considered as a leading factor resulting in cancer development. The purpose of this study was to explore the significance of CS-related genes in the molecular classification and survival outcome of clear cell renal cell carcinoma (ccRCC).MethodsCS-related genes were obtained from the CellAge database, and patients from TCGA-KIRC dataset and ICGC dataset were clustered by ConsesusClusterPlus. The characteristics of overall survival (OS), genomic variation, and tumor microenvironment (TME) of each cluster were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was conducted to develop a CS-related risk model to score ccRCC patients and assess the risk scores in predicting patients’ response to immunotherapy and chemotherapy. A nomogram based on the risk model was established to improve the risk stratification of patients.ResultsCcRCC was divided into three molecular subtypes based on CS-related genes. The three molecular phenotypes showed different OS and clinical manifestations, mutation patterns, and TME states. Five genes were obtained from nine differentially expressed CS-related genes in the three molecular subtypes to develop a risk model. Patients with ccRCC were divided into high- and low-risk subgroups. The former showed an unfavorable OS, with a significantly higher genomic variation rate, TME score, and numerous immune checkpoint expressions when compared to the low-risk subgroup. Risk score reflected the response of patients to axitinib, bortezomib, sorafenib, sunitinib, and temsirolimus.ConclusionsIn general, CS-related genes divided ccRCC into three molecular subtypes with distinct OS, mutation patterns, and TME states. The risk model based on the five CS-related genes can predict the prognosis and therapeutic outcome of ccRCC patients, providing a theoretical basis for further study on the molecular mechanism of CS-related ccRCC.</p

Image_1_Comprehensive analysis of cellular senescence-related genes in the prognosis, tumor microenvironment, and immunotherapy/chemotherapy of clear cell renal cell carcinoma.jpeg

BackgroundThe transcriptome public database and advances in biological discoveries contributed to significant progresses in identifying the drivers of cancer progression. Cellular senescence (CS) is considered as a leading factor resulting in cancer development. The purpose of this study was to explore the significance of CS-related genes in the molecular classification and survival outcome of clear cell renal cell carcinoma (ccRCC).MethodsCS-related genes were obtained from the CellAge database, and patients from TCGA-KIRC dataset and ICGC dataset were clustered by ConsesusClusterPlus. The characteristics of overall survival (OS), genomic variation, and tumor microenvironment (TME) of each cluster were analyzed. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was conducted to develop a CS-related risk model to score ccRCC patients and assess the risk scores in predicting patients’ response to immunotherapy and chemotherapy. A nomogram based on the risk model was established to improve the risk stratification of patients.ResultsCcRCC was divided into three molecular subtypes based on CS-related genes. The three molecular phenotypes showed different OS and clinical manifestations, mutation patterns, and TME states. Five genes were obtained from nine differentially expressed CS-related genes in the three molecular subtypes to develop a risk model. Patients with ccRCC were divided into high- and low-risk subgroups. The former showed an unfavorable OS, with a significantly higher genomic variation rate, TME score, and numerous immune checkpoint expressions when compared to the low-risk subgroup. Risk score reflected the response of patients to axitinib, bortezomib, sorafenib, sunitinib, and temsirolimus.ConclusionsIn general, CS-related genes divided ccRCC into three molecular subtypes with distinct OS, mutation patterns, and TME states. The risk model based on the five CS-related genes can predict the prognosis and therapeutic outcome of ccRCC patients, providing a theoretical basis for further study on the molecular mechanism of CS-related ccRCC.</p