Mortality in hemodialysis patients over 65 years of age

Five-year survival analysis and predictors of mortality of adult hemodialysis patients in Indonesia: a nationwide database analysis

BACKGROUND

Hemodialysis (HD) is the modality of renal replacement therapy (RRT) with the highest mortality rate. The identification of prognostic factors will help achieve better outcomes among HD patients. In this study, we sought to conduct a survival analysis and determine the predictors of mortality among adult Indonesian HD patients using data obtained from Indonesian Renal Registry (IRR).

METHODS

This is a retrospective cohort study which reviewed all adult hemodialysis patients in Indonesia based on the records of IRR during 2007-2022. Variables analyzed in this study include age, sex, etiology of CKD, cause of death, HD frequency, and initial vascular access. A 5-year follow-up was performed until the outcome of death or drop out was found.

RESULTS

Among 99,552 eligible patients, the mean survival length was 1536.21 ± 2.50 days. The 5-year survival rate was 77%. Cox proportional hazard regression model revealed demographic and clinical characteristics that are significantly associated with mortality: male sex (HR: 1.038, 95% CI 1.002-1.075), age of 60 years or older (HR: 1.329, 95% CI 1.281-1.379), diabetic nephropathy (HR: 1.347, 95% CI 1.249-1.452), twice-weekly hemodialysis frequency (HR: 1.080, 95% CI 1.011-1.155), initial vascular accesses with femoral vein puncture (HR: 2.710, 95% CI 2.568-2.860), and CVC (HR: 2.992, 95% CI 2.848-3.144).

CONCLUSIONS

The 5-year survival rate of Indonesian HD patients is 77. Male sex, age of HD onset at 60 years or older, diabetic nephropathy, twice-weekly HD frequency, and the initial vascular accesses with femoral vein puncture and CVC are associated with increased risk of mortality.

Bacteremia and Mortality among Patients with Nontunneled and Tunneled Catheters for Hemodialysis

Introduction

Central venous catheters for hemodialysis (HD) can be nontunneled catheters (NTC) or tunneled catheters (TC). Bacteremia and dysfunction are complications that can impact morbidity and mortality. We decided to compare the rates of bacteremia and dysfunction between NTC and TC and patient survival 90 days after catheter insertion.

Methods

Retrospective cohort to evaluate catheters inserted between January 2011 and December 2020 in a tertiary hospital. Catheters in patients with end-stage chronic kidney disease were included. Patients with acute kidney injury, catheters that lasted less than three HD sessions, and patients who died within one week after insertion were excluded. Bacteremia and dysfunction rates, bacteremia-free survival, and dysfunction-free survival were investigated. Multivariable analysis was performed using a Cox proportional hazards regression model for patient survival at 90 days.

Results

670 catheters were analyzed in 287 patients, 422 NTC (63%), and 248 TC (37%). The rates of confirmed bacteremia per 1,000 catheter-days were 1.19 for NTC and 0.20 for TC (p < 0.0001). The confirmed or possible bacteremia rates were 2.27 and 0.37 per 1,000 catheter-days for NTC and TC, respectively (p < 0.0001). The dysfunction rates were 3.96 and 0.86 for NTC and TC, respectively (p < 0.0001). Patient survival at 90 days was higher in the TC group than the NTC group (96.8% vs. 89.1%; p < 0.0001).

Conclusion

We found lower rates of bacteremia and dysfunction for TC and demonstrated that using NTC affects patient mortality.

Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients

Life expectancy is likely to be substantially reduced in patients undergoing chronic hemodialysis (CHD). However, machine learning (ML) may predict the risk factors of mortality in patients with CHD by analyzing the serum laboratory data from regular dialysis routine. This study aimed to establish the mortality prediction model of CHD patients by adopting two-stage ML algorithm-based prediction scheme, combined with importance of risk factors identified by different ML methods. This is a retrospective, observational cohort study. We included 800 patients undergoing CHD between December 2006 and December 2012 in Shin-Kong Wu Ho-Su Memorial Hospital. This study analyzed laboratory data including 44 indicators. We used five ML methods, namely, logistic regression (LGR), decision tree (DT), random forest (RF), gradient boosting (GB), and eXtreme gradient boosting (XGB), to develop a two-stage ML algorithm-based prediction scheme and evaluate the important factors that predict CHD mortality. LGR served as a bench method. Regarding the validation and testing datasets from 1- and 3-year mortality prediction model, the RF had better accuracy and area-under-curve results among the five different ML methods. The stepwise RF model, which incorporates the most important factors of CHD mortality risk based on the average rank from DT, RF, GB, and XGB, exhibited superior predictive performance compared to LGR in predicting mortality among CHD patients over both 1-year and 3-year periods. We had developed a two-stage ML algorithm-based prediction scheme by implementing the stepwise RF that demonstrated satisfactory performance in predicting mortality in patients with CHD over 1- and 3-year periods. The findings of this study can offer valuable information to nephrologists, enhancing patient-centered decision-making and increasing awareness about risky laboratory data, particularly for patients with a high short-term mortality risk.

Effect of Intradialytic Supine Ergometer Exercise on Hemodialysis Patients with Different Nutritional Status

OBJECTIVE

It is important for hemodialysis patients to exercise while their nutritional status is being monitored. This study aimed to examine the difference in physical exercise function and the effect of exercise intervention in hemodialysis patients who were divided into two groups (high-nutrition and low-nutrition groups) based on the serum albumin levels.

METHOD

A total of 26 outpatients (18 men and 8 women) undergoing hemodialysis (age: 66 ± 10 years) were included in this study. The patients' body composition data (weight, body mass index, percentage of body fat, fat-free mass, and total body water) and physical functions (grip strength, knee extensor strength, open-eyed one-legged standing time, long sitting trunk anteflexion, and 6-minute walking distance [6MWD] test) were measured. The intervention was supine ergometer exercise during hemodialysis, and the patients exercised for 30 minutes during hemodialysis thrice a week. The intervention period was three months.

RESULTS

Compared to the high-nutrition group, the low-nutrition group showed a significant decrease in muscle strength. Furthermore, long sitting trunk anteflexion in the high-nutrition group and 6MWD in the low-nutrition group improved significantly after the intervention.

CONCLUSION

The result of this study may indicate that 6MD can be improved by exercise during dialysis, regardless of nutritional status. It is said that low nutritional status has a negative impact on survival rate; thus, considering the impact on survival rate, it is hemodialysis patients with a low nutritional status that should be considered to introduce more active exercise during dialysis.

Construction data mining methods in the prediction of death in hemodialysis patients using support vector machine, neural network, logistic regression and decision tree

Objectives

Chronic kidney disease (CKD) is one of the main causes of morbidity and mortality worldwide. Detecting survival modifiable factors could help in prioritizing the clinical care and offers a treatment decision-making for hemodialysis patients. The aim of this study was to develop the best predictive model to explain the predictors of death in Hemodialysis patients by data mining techniques.

Methods

In this study, we used a dataset included records of 857 dialysis patients. Thirty-one potential risk factors, that might be associated with death in dialysis patients, were selected. The performances of four classifiers of support vector machine, neural network, logistic regression and decision tree were compared in terms of sensitivity, specificity, total accuracy, positive likelihood ratio and negative likelihood ratio.

Results

The average total accuracy of all methods was over 61%; the greatest total accuracy belonged to logistic regression (0.71). Also, logistic regression produced the greatest specificity (0.72), sensitivity (0.69), positive likelihood ratio (2.48) and the lowest negative likelihood ratio (0.43).

Conclusions

Logistic regression had the best performance in comparison to other methods for predicting death among hemodialysis patients. According to this model female gender, increasing age at diagnosis, addiction, low Iron level, C-reactive protein positive and low urea reduction ratio (URR) were the main predictors of death in these patients.

Vascular access for elderly hemodialysis patients: what should we aim for?

PURPOSE

Elderly chronic kidney disease (CKD) patients are one of the fastest growing groups in hemodialysis (HD). However, overall mortality and arteriovenous fistulae failure are still high in this population. Therefore, a different vascular access policy may be advised for this group of patients.

METHODS

We searched PubMed for relevant articles published in English between the years 2000-2015. Studies investigating vascular access-related outcomes in elderly CKD patients were included.

RESULTS

The scarce literature on this topic suggests that elderly CKD patients are more likely to undergo unnecessary vascular access procedures. However, with appropriate vascular evaluation, arteriovenous access placement is a viable strategy for this group of patients and dialysis access-related outcomes are superior for arteriovenous access in comparison with dialysis catheters.

CONCLUSIONS

When deciding whether or not to create an arteriovenous vascular access in elderly CKD patients, physicians should consider the probability of CKD progression, the expected life expectancy and quality of life of the patient and the probability of success of an arteriovenous access creation.

INTEGRATING CHRONIC KIDNEY DISEASE PATIENT'S SPIRITUALITY IN THEIR CARE: HEALTH BENEFITS AND RESEARCH PERSPECTIVES

INTRODUCTION

Patients who suffer from chronic renal disease face problems in many aspects of their life; problems such as physical and social as well as mental such as stress, anxiety, depression. In addition, they exhibit an amount of spiritual needs, which relate and influence the psychological adaptation to the illness.

AIM

The aim of this article is to examine evidence from the international literature regarding the possible relation of spirituality and health outcomes, mostly in the complex codex of a chronic and life treathing disease such as CKD.

RESULTS

Spirituality is a very debatable issue and the term has no single and widely agreed definition. The key components of spirituality were 'meaning', 'hope', 'relatedness/connectedness', and 'beliefs/beliefs systems'. Spirituality has been characterized as the quest for meaning in life, mainly through experiences and expressions of mind, in a unique and dynamic process different for each individual. For many individuals spirituality and religion are important aspects of their existence, constituting a source support contribute to wellbeing and coping with life's daily difficulties.

CONCLUSION

Considering, assessing and addressing chronic kidney disease patient's spirituality and spiritual needs is necessary and it can have a positive outcome in health related quality of life, mental health and life expectancy.