Advance prediction of early death in patients starting maintenance dialysis

External validation of the 2-year mortality prediction tool in hemodialysis patients developed using a Bayesian network

Background

In recent years, a number of predictive models have appeared to predict the risk of medium-term mortality in hemodialysis patients, but only one, limited to patients aged over 70 years, has undergone sufficiently powerful external validation. Recently, using a national learning database and an innovative approach based on Bayesian networks and 14 carefully selected predictors, we have developed a clinical prediction tool to predict all-cause mortality at 2 years in all incident hemodialysis patients. In order to generalize the results of this tool and propose its use in routine clinical practice, we carried out an external validation using an independent external validation database.

Methods

A regional, multicenter, observational, retrospective cohort study was conducted to externally validate the tool for predicting 2-year all-cause mortality in incident and prevalent hemodialysis patients. This study recruited a total of 142 incident and 697 prevalent adult hemodialysis patients followed up in one of the eight Association pour l'Utilisation du Rein Artificiel dans la région Lyonnaise (AURAL) Alsace dialysis centers.

Results

In incident patients, the 2-year all-cause mortality prediction tool had an area under the receiver curve (AUC-ROC) of 0.73, an accuracy of 65%, a sensitivity of 71% and a specificity of 63%. In prevalent patients, the performance for the external validation were similar in terms of AUC-ROC, accuracy and specificity, but was lower in term of sensitivity.

Conclusion

The tool for predicting all-cause mortality at 2 years, developed using a Bayesian network and 14 routinely available explanatory variables, obtained satisfactory external validation in incident patients, but sensitivity was insufficient in prevalent patients.

Comparison of the Charlson comorbidity index, the modified Charlson comorbidity index, and the recipient risk score in prediction of the graft and patient survival among renal graft recipients: historical cohort in a single center

OBJECTIVE

To compare the predictive values of Charlson comorbidity index (CCI), modified Charlson comorbidity index kidney transplant (mCCI-KT) and recipient risk score (RRS) indices in prediction of patient and graft survival in kidney transplant patients.

METHODS

In this retrospective study, all patients who underwent a live-donor KT from 2006 to 2010, were included. Demographic data, comorbidities and survival time after KT were extracted and the association between above indices with patient and graft survival were compared.

RESULTS

In ROC curve analysis of 715 included patients, all three indicators were weak in predicting graft rejection with the area under curve (AUC) less than 0.6. The best models for predicting the overall survival were mCCI-KT and CCI with AUC of 0.827 and 0.780, respectively. Sensitivity and specificity of mCCI-KT at cut point of 1 were 87.2 and 75.6. Sensitivity and specificity of CCI at cut point of 3 were 84.6 and 68.3 and for RRS at cut point of 3 were 51.3 and 81.2, respectively.

CONCLUSION

The mCCI-KT index followed by the CCI index provided the best model in predicting the 10-year patient survival; however, they were poor in predicting graft survival and this model can be used for better stratifying transplant candidates prior to surgery.

Predicting outcomes in chronic kidney disease: needs and preferences of patients and nephrologists

INTRODUCTION

Guidelines on chronic kidney disease (CKD) recommend that nephrologists use clinical prediction models (CPMs). However, the actual use of CPMs seems limited in clinical practice. We conducted a national survey study to evaluate: 1) to what extent CPMs are used in Dutch CKD practice, 2) patients' and nephrologists' needs and preferences regarding predictions in CKD, and 3) determinants that may affect the adoption of CPMs in clinical practice.

METHODS

We conducted semi-structured interviews with CKD patients to inform the development of two online surveys; one for CKD patients and one for nephrologists. Survey participants were recruited through the Dutch Kidney Patient Association and the Dutch Federation of Nephrology.

RESULTS

A total of 126 patients and 50 nephrologists responded to the surveys. Most patients (89%) reported they had discussed predictions with their nephrologists. They most frequently discussed predictions regarded CKD progression: when they were expected to need kidney replacement therapy (KRT) (n = 81), and how rapidly their kidney function was expected to decline (n = 68). Half of the nephrologists (52%) reported to use CPMs in clinical practice, in particular CPMs predicting the risk of cardiovascular disease. Almost all nephrologists (98%) reported discussing expected CKD trajectories with their patients; even those that did not use CPMs (42%). The majority of patients (61%) and nephrologists (84%) chose a CPM predicting when patients would need KRT in the future as the most important prediction. However, a small portion of patients indicated they did not want to be informed on predictions regarding CKD progression at all (10-15%). Nephrologists not using CPMs (42%) reported they did not know CPMs they could use or felt that they had insufficient knowledge regarding CPMs. According to the nephrologists, the most important determinants for the adoption of CPMs in clinical practice were: 1) understandability for patients, 2) integration as standard of care, 3) the clinical relevance.

CONCLUSION

Even though the majority of patients in Dutch CKD practice reported discussing predictions with their nephrologists, CPMs are infrequently used for this purpose. Both patients and nephrologists considered a CPM predicting CKD progression most important to discuss. Increasing awareness about existing CPMs that predict CKD progression may result in increased adoption in clinical practice. When using CPMs regarding CKD progression, nephrologists should ask whether patients want to hear predictions beforehand, since individual patients' preferences vary.

A Machine Learning Model for Predicting Mortality within 90 Days of Dialysis Initiation

Background

The first 90 days after dialysis initiation are associated with high morbidity and mortality in end-stage kidney disease (ESKD) patients. A machine learning-based tool for predicting mortality could inform patient-clinician shared decision making on whether to initiate dialysis or pursue medical management. We used the eXtreme Gradient Boosting (XGBoost) algorithm to predict mortality in the first 90 days after dialysis initiation in a nationally representative population from the United States Renal Data System.

Methods

A cohort of adults initiating dialysis between 2008-2017 were studied for outcome of death within 90 days of dialysis initiation. The study dataset included 188 candidate predictors prognostic of early mortality that were known on or before the first day of dialysis and was partitioned into training (70%) and testing (30%) subsets. XGBoost modeling used a complete-case set and a dataset obtained from multiple imputation. Model performance was evaluated by c-statistics overall and stratified by subgroups of age, sex, race, and dialysis modality.

Results

The analysis included 1,150,195 patients with ESKD, of whom 86,083 (8%) died in the first 90 days after dialysis initiation. The XGBoost models discriminated mortality risk in the nonimputed (c=0.826, 95% CI, 0.823 to 0.828) and imputed (c=0.827, 95% CI, 0.823 to 0.827) models and performed well across nearly every subgroup (race, age, sex, and dialysis modality) evaluated (c>0.75). Across predicted risk thresholds of 10%-50%, higher risk thresholds showed declining sensitivity (0.69-0.04) with improving specificity (0.79-0.99); similarly, positive likelihood ratio was highest at the 40% threshold, whereas the negative likelihood ratio was lowest at the 10% threshold. After calibration using isotonic regression, the model accurately estimated the probability of mortality across all ranges of predicted risk.

Conclusions

The XGBoost-based model developed in this study discriminated risk of early mortality after dialysis initiation with excellent calibration and performed well across key subgroups.

Shared decision-making in advanced kidney disease: a scoping review

OBJECTIVES

To provide a comprehensive overview of interventions that support shared decision-making (SDM) for treatment modality decisions in advanced kidney disease (AKD). To provide summarised information on their content, use and reported results. To provide an overview of interventions currently under development or investigation.

DESIGN

The JBI methodology for scoping reviews was followed. This review conforms to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist.

DATA SOURCES

MEDLINE, Embase, Web of Science, Cochrane Library, Emcare, PsycINFO, PROSPERO and Academic Search Premier for peer-reviewed literature. Other online databases (eg, clinicaltrials.gov, OpenGrey) for grey literature.

ELIGIBILITY FOR INCLUSION

Records in English with a study population of patients >18 years of age with an estimated glomerular filtration rate <30 mL/min/1.73 m2. Records had to be on the subject of SDM, or explicitly mention that the intervention reported on could be used to support SDM for treatment modality decisions in AKD.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently screened and selected records for data extraction. Interventions were categorised as prognostic tools (PTs), educational programmes (EPs), patient decision aids (PtDAs) or multicomponent initiatives (MIs). Interventions were subsequently categorised based on the decisions they were developed to support.

RESULTS

One hundred forty-five interventions were identified in a total of 158 included records: 52 PTs, 51 EPs, 29 PtDAs and 13 MIs. Sixteen (n=16, 11%) were novel interventions currently under investigation. Forty-six (n=46, 35.7%) were reported to have been implemented in clinical practice. Sixty-seven (n=67, 51.9%) were evaluated for their effects on outcomes in the intended users.

CONCLUSION

There is no conclusive evidence on which intervention is the most efficacious in supporting SDM for treatment modality decisions in AKD. There is a lot of variation in selected outcomes, and the body of evidence is largely based on observational research. In addition, the effects of these interventions on SDM are under-reported.

Predicting 3-month and 1-year mortality for patients initiating dialysis: a population-based cohort study

BACKGROUND

Despite the continual improvements in dialysis treatments, mortality in end-stage kidney disease (ESKD) remains high. Many mortality prediction models are available, but most of them are not precise enough to be used in the clinical practice. We aimed to develop and validate two prediction models for 3-month and 1-year patient mortality after dialysis initiation in our population.

METHODS

Using population-based data of insurance claims in Taiwan, we included more than 210,000 patients who initiated dialysis between January 1, 2006, and June 30, 2015. We developed two prognostic models, which included 9 and 11 variables, respectively (including age, sex, myocardial infarction, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, peptic ulcer disease, malignancy, moderate to severe liver disease, and first dialysis in intensive care unit).

RESULTS

The models showed adequate discrimination (C-statistics were 0.80 and 0.82 for 3-month and 1-year mortality, respectively) and good calibration. In both our models, the first dialysis in the intensive care unit and moderate-to-severe liver disease were the strongest risk factors for mortality.

CONCLUSION

The prediction models developed in our population had good predictive ability for short-term mortality in patients initiating dialysis in Taiwan and could help in decision-making regarding dialysis initiation, at least in our setting, supporting a patient-centered approach to care.

Age, Gender and Diabetes as Risk Factors for Early Mortality in Dialysis Patients: A Systematic Review

Objective: To study the impact of age, gender, and presence of diabetes (any type) on the risk of early deaths (180-day mortality) in patients starting long-term hemodialysis (HD) therapy.Design: Systematic review of the literature.Setting: Out-patient (non-hospitalized), community-based HD therapy world-wide.Participants: Patients with advanced chronic kidney disease (CKD) starting long-term HD treatment for end-stage renal disease (ESRD).Methods: Medline and EMBASE were searched for studies published between 1/1/1985 and 12/31/2017. Observational studies involving adult subjects commencing HD were included. Data extracted included population characteristics and settings. In addition, patient or treatment related factors studied with reference to their relationship with the risk of early mortality were documented. The Quality in Prognosis Studies tool was used to assess risk of bias in individual studies. Findings were summarized, and a narrative account was drawn.Results: Included were 26 studies (combined population 1,098,769; representing 287,085 person-years of observation for early mortality). There were 17 cohort and 9 case-control studies. Risk of bias was low in 13 and high in a further 13 studies. Patients who died in the early period were older than those who survived. Mortality rates increased with advancing age. Female gender was associated with slightly increased early mortality rates in larger and higher quality studies. The available data showed conflicting results in relation to the association of diabetes and risk of early mortality.Conclusions: This systematic review evaluated the impact of key demographic and co-morbid factors on risk of early mortality in patients starting maintenance HD. The information could help in delivering more tailored prognostic information and planning of future interventions.

Validation of prognostic indices for short term mortality in an incident dialysis population of older adults >75

Rational and objective

Prognosis provides critical knowledge for shared decision making between patients and clinicians. While several prognostic indices for mortality in dialysis patients have been developed, their performance among elderly patients initiating dialysis is unknown, despite great need for reliable prognostication in that context. To assess the performance of 6 previously validated prognostic indices to predict 3 and/or 6 months mortality in a cohort of elderly incident dialysis patients.

Study design

Validation study of prognostic indices using retrospective cohort data. Indices were compared using the concordance (“c”)-statistic, i.e. area under the receiver operating characteristic curve (ROC). Calibration, sensitivity, specificity, positive and negative predictive values were also calculated.

Setting & participants

Incident elderly (age ≥75 years; n = 349) dialysis patients at a tertiary referral center.

Established predictors

Variables for six validated prognostic indices for short term (3 and 6 month) mortality prediction (Foley, NCI, REIN, updated REIN, Thamer, and Wick) were extracted from the electronic medical record. The indices were individually applied as per each index specifications to predict 3- and/or 6-month mortality.

Results

In our cohort of 349 patients, mean age was 81.5±4.4 years, 66% were male, and median survival was 351 days. The c-statistic for the risk prediction indices ranged from 0.57 to 0.73. Wick ROC 0.73 (0.68, 0.78) and Foley 0.67 (0.61, 0.73) indices performed best. The Foley index was weakly calibrated with poor overall model fit (p <0.01) and overestimated mortality risk, while the Wick index was relatively well-calibrated but underestimated mortality risk.

Limitations

Small sample size, use of secondary data, need for imputation, homogeneous population.

Conclusion

Most predictive indices for mortality performed moderately in our incident dialysis population. The Wick and Foley indices were the best performing, but had issues with under and over calibration. More accurate indices for predicting survival in older patients with kidney failure are needed.

Predicting mortality in hemodialysis patients using machine learning analysis

Background

Besides the classic logistic regression analysis, non-parametric methods based on machine learning techniques such as random forest are presently used to generate predictive models. The aim of this study was to evaluate random forest mortality prediction models in haemodialysis patients.

Methods

Data were acquired from incident haemodialysis patients between 1995 and 2015. Prediction of mortality at 6 months, 1 year and 2 years of haemodialysis was calculated using random forest and the accuracy was compared with logistic regression. Baseline data were constructed with the information obtained during the initial period of regular haemodialysis. Aiming to increase accuracy concerning baseline information of each patient, the period of time used to collect data was set at 30, 60 and 90 days after the first haemodialysis session.

Results

There were 1571 incident haemodialysis patients included. The mean age was 62.3 years and the average Charlson comorbidity index was 5.99. The mortality prediction models obtained by random forest appear to be adequate in terms of accuracy [area under the curve (AUC) 0.68–0.73] and superior to logistic regression models (ΔAUC 0.007–0.046). Results indicate that both random forest and logistic regression develop mortality prediction models using different variables.

Conclusions

Random forest is an adequate method, and superior to logistic regression, to generate mortality prediction models in haemodialysis patients.

Early Mortality Rates After Commencement of Maintenance Hemodialysis: A Systematic Review and Meta-Analysis

Mortality rates are reported to be high soon after the commencement of maintenance HD for ESRD. Our aim was to estimate early mortality rates (deaths within 180 days of starting therapy), through a systematic review of literature, in this patient population. Medline and EMBASE were searched for publications between 1 January 1985 and 31 December 2017. Observational studies reporting deaths involving adults commencing HD were included. The Quality in Prognosis Studies tool was used to assess risk of bias in studies. Crude mortality rates (expressed in 100 person-years) and age-standardized mortality ratios (SMR) were calculated. Meta-analyses of these rates were conducted for studies with lowest risk of bias (i.e. highest quality). In total, 32 studies were included (combined population: 1 083 264) representing 283 277 person-years of observation; median follow-up: 90 days. Mortality rates ranged between 12.8 and 55.6 per 100 person-years. Cardiovascular causes accounted for the majority of early deaths. Meta-analysis of high-quality studies showed an overall crude mortality rate of 32.6 per 100 person-years (95% CI 32.4-32.8). This equates to 16.3% mortality in first 180 days of starting HD. Six high-quality studies contained sufficient data for calculation of SMR. Meta-analysis of SMRs showed that patients starting HD therapy sustain 8.8 times higher mortality rates compared to the general population. We have combined the results of high-quality studies to produce new estimates of early mortality rates after commencement of HD therapy. This information can help relay more reliable prognostic information to this patient population.

Prediction of Risk of Death for Patients Starting Dialysis: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

Dialysis is a preference-sensitive decision where prognosis may play an important role. Although patients desire risk prediction, nephrologists are wary of sharing this information. We reviewed the performance of prognostic indices for patients starting dialysis to facilitate bedside translation.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Systematic review and meta-analysis following the PRISMA guidelines. We searched Ovid MEDLINE, Ovid Embase, Ovid Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus for eligible studies of patients starting dialysis published from inception to December 31, 2018.

SELECTION CRITERIA

Articles describing validated prognostic indices predicting mortality at the start of dialysis. We excluded studies limited to prevalent dialysis patients, AKI and studies excluding mortality in the first 1-3 months. Two reviewers independently screened abstracts, performed full text assessment of inclusion criteria and extracted: study design, setting, population demographics, index performance and risk of bias. Pre-planned random effects meta-analysis was performed stratified by index and predictive window to reduce heterogeneity.

RESULTS

Of 12,132 articles screened and 214 reviewed in full text, 36 studies were included describing 32 prognostic indices. Predictive windows ranged from 3 months to 10 years, cohort sizes from 46 to 52,796. Meta-analysis showed discrimination area under the curve (AUC) of 0.71 (95% confidence interval, 0.69 to 073) with high heterogeneity (I2=99.12). Meta-analysis by index showed highest AUC for The Obi, Ivory, and Charlson comorbidity index (CCI)=0.74, also CCI was the most commonly used (ten studies). Other commonly used indices were Kahn-Wright index (eight studies, AUC 0.68), Hemmelgarn modification of the CCI (six studies, AUC 0.66) and REIN index (five studies, AUC 0.69). Of the indices, ten have been validated externally, 16 internally and nine were pre-existing validated indices. Limitations include heterogeneity and exclusion of large cohort studies in prevalent patients.

CONCLUSIONS

Several well validated indices with good discrimination are available for predicting survival at dialysis start.

Machine Learning to Identify Dialysis Patients at High Death Risk

Introduction

Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality.

Methods

This study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation.

Results

The mean (±SD) age of our cohort was 68.7 ± 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994–0.7377), 0.7446 (0.7346–0.7546), 0.7504 (0.7425–0.7583), and 0.7488 (0.7421–0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors.

Conclusion

We implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD.

Comparing Five Comorbidity Indices to Predict Mortality in Chronic Kidney Disease: A Retrospective Cohort Study

Background:

Several different indices summarize patient comorbidity using health care data. An accurate index can be used to describe the risk profile of patients, and as an adjustment factor in analyses. How well these indices perform in persons with chronic kidney disease (CKD) is not well known.

Objective:

Assess the performance of 5 comorbidity indices at predicting mortality in 3 different patient groups with CKD: incident kidney transplant recipients, maintenance dialysis patients, and individuals with low estimated glomerular filtration rate (eGFR).

Design:

Population-based retrospective cohort study.

Setting:

Ontario, Canada, between 2004 and 2014.

Patients:

Individuals at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an eGFR less than 45 mL/min per 1.73m².

Measurements:

Five comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins’ Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Our primary outcome was 1-year all-cause mortality.

Methods:

Comorbidity indices were estimated using information in the prior 2 years. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically.

Results:

We identified 4111 kidney transplant recipients, 23 897 individuals receiving maintenance dialysis, and 181 425 individuals with a low eGFR. Within 1 year, 108 (2.6%), 4179 (17.5%), and 17 898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate with median c-statistics less than 0.7 for all 5 comorbidity indices for all 3 groups. Calibration was also poor for all models.

Limitations:

The study used administrative health care data so there is the potential for misclassification. Indices were modeled as continuous scores as opposed to indicators for individual conditions to limit overfitting.

Conclusions:

Existing comorbidity indices do not accurately predict 1-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.

Development and Validation of Prediction Scores for Early Mortality at Transition to Dialysis

OBJECTIVE

To develop and validate a risk prediction model that would help individualize treatment and improve the shared decision-making process between clinicians and patients.

PATIENTS AND METHODS

We developed a risk prediction tool for mortality during the first year of dialysis based on pre-end-stage renal disease characteristics in a cohort of 35,878 US veterans with incident end-stage renal disease who transitioned to dialysis treatment between October 1, 2007, and March 31, 2014 and then externally validated this tool among 4284 patients in the Kaiser Permanente Southern California (KPSC) health care system who transitioned to dialysis treatment between January 1, 2007, and September 30, 2015.

RESULTS

To ensure model goodness of fit, 2 separate models were selected for patients whose last estimated glomerular filtration rate (eGFR) before dialysis initiation was less than 15 mL/min per 1.73 m² or 15 mL/min per 1.73 m² or higher. Model discrimination in the internal validation cohort of veterans resulted in C statistics of 0.71 (95% CI, 0.70-0.72) and 0.66 (95% CI, 0.65-0.67) among patients with eGFR lower than 15 mL/min per 1.73 m² and 15 mL/min per 1.73 m² or higher, respectively. In the KPSC external validation cohort, the developed risk score exhibited C statistics of 0.77 (95% CI, 0.74-0.79) in men and 0.74 (95% CI, 0.71-0.76) in women with eGFR lower than 15 mL/min per 1.73 m² and 0.71 (95% CI, 0.67-0.74) in men and 0.67 (95% CI, 0.62-0.72) in women with eGFR of 15 mL/min per 1.73 m² or higher.

CONCLUSION

A new risk prediction tool for mortality during the first year after transition to dialysis (available at www.DialysisScore.com) was developed in the large national Veterans Affairs cohort and validated with good performance in the racially, ethnically, and gender diverse KPSC cohort. This risk prediction tool will help identify high-risk populations and guide management strategies at the transition to dialysis.

Early mortality in patients with chronic kidney disease who started emergency haemodialysis in a Peruvian population: Incidence and risk factors

OBJECTIVES

To estimate early mortality in patients with chronic kidney disease who started emergency haemodialysis between 2012 and 2014 in a national referral hospital in Lima, Peru, and to identify risk factors.

DESIGN, CHARACTERISTICS, PARTICIPANTS AND MEASUREMENTS

A retrospective cohort study was conducted by reviewing the medical records of all patients admitted to the hospital's Haemodialysis Unit from 2012 to 2014. Early mortality, defined as death within the first 90 days of starting haemodialysis, as well as age, gender, chronic kidney disease aetiology, comorbidities, cause of death, estimated glomerular filtration rate, vascular access and other variables were evaluated in patients who initiated emergency haemodialysis. Early mortality was estimated using frequencies and risk factors were determined by Poisson regression with robust variance.

RESULTS

43.4% of patients were female, 51.5% were aged≥65 years and the early mortality rate was 9.3%. The main risk factors were estimated glomerular filtration rate>10 ml/min/1.73m² (RR: 2.72 [95% CI: 1.60-4.61]); age≥65 years (RR: 2.51 [95% CI: 1.41-4.48]); central venous catheter infection, RR: 2.25 (95% CI: 1.08-4.67); female gender, RR: 2.15 (95% CI: 1.29-3.58); and albumin<3.5g/dl (RR: 1.97 [95% CI: 1.01-3.82]).

CONCLUSIONS

Early mortality was 9.3%. The main risk factor was starting haemodialysis with an estimated glomerular filtration rate>10ml/min/1.73m².

Predicting 6-month mortality risk of patients commencing dialysis treatment for end-stage kidney disease

Background

There is evidence that end-stage kidney disease patients who are older or with more comorbidity may have a poor trade-off between benefits of dialysis and potential harms. We aimed to develop a tool for predicting patient mortality in the early stages of receiving dialysis.

Methods

In 23 658 patients aged 15+ years commencing dialysis between 2000 and 2009 in Australia and New Zealand a point score tool was developed to predict 6-month mortality based on a logistic regression analysis of factors available at dialysis initiation. Temporal validation used 2009-11 data from Australia and New Zealand. External validation used the UK Renal Registry.

Results

Within 6 months of commencing dialysis 6.1% of patients had died. A small group (4.7%) of patients had a high predicted mortality risk (>20%), as predicted by the point score tool. Predictive variables were: older age, underweight, chronic lung disease, coronary artery disease, peripheral vascular disease, cerebrovascular disease (particularly for patients <60 years of age), late referral to nephrologist care and underlying cause of renal disease. The new point score tool outperformed existing models, and had an area under the receiver operating characteristic curve of 0.755 on temporal validation with acceptable calibration and 0.713 on external validation with poor calibration.

Conclusion

Our point score tool for predicting 6-month mortality in patients at dialysis commencement has sufficient prognostic accuracy to use in Australia and New Zealand for prognosis and identification of high risk patients who may be given appropriate supportive care. Use in other countries requires further study.

Prediction models for the mortality risk in chronic dialysis patients: a systematic review and independent external validation study

OBJECTIVE

In medicine, many more prediction models have been developed than are implemented or used in clinical practice. These models cannot be recommended for clinical use before external validity is established. Though various models to predict mortality in dialysis patients have been published, very few have been validated and none are used in routine clinical practice. The aim of the current study was to identify existing models for predicting mortality in dialysis patients through a review and subsequently to externally validate these models in the same large independent patient cohort, in order to assess and compare their predictive capacities.

METHODS

A systematic review was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To account for missing data, multiple imputation was performed. The original prediction formulae were extracted from selected studies. The probability of death per model was calculated for each individual within the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD). The predictive performance of the models was assessed based on their discrimination and calibration.

RESULTS

In total, 16 articles were included in the systematic review. External validation was performed in 1,943 dialysis patients from NECOSAD for a total of seven models. The models performed moderately to well in terms of discrimination, with C-statistics ranging from 0.710 (interquartile range 0.708-0.711) to 0.752 (interquartile range 0.750-0.753) for a time frame of 1 year. According to the calibration, most models overestimated the probability of death.

CONCLUSION

Overall, the performance of the models was poorer in the external validation than in the original population, affirming the importance of external validation. Floege et al's models showed the highest predictive performance. The present study is a step forward in the use of a prediction model as a useful tool for nephrologists, using evidence-based medicine that combines individual clinical expertise, patients' choices, and the best available external evidence.

Infrainguinal Bypass in Patients with End-stage Renal Disease: Survival and Ambulation

The purpose of this study was to evaluate the outcomes of infrainguinal bypass surgery and ambulatory status in patients with end-stage renal disease (ESRD).

Vascular registry data and the medical records of patients requiring infrainguinal bypass surgery from 1985 through 1995 were reviewed retrospectively. Patients with chronic limb-threatening ischemia requiring maintenance hemodialysis or peritoneal dialysis for ≥ 6 months were compared to a randomly selected group of patients under going foot salvage infrainguinal bypass in the absence of ESRD. Primary outcomes were mortality, amputation, and ambulatory status at 30 days and 1 year. Data were analyzed using Pearson's chi-square methods, Fisher's Exact test, Mann-Whitney U, life table analyses, and Quality of Life-Class (QL-Class) ranking.

There were 57 patients with ESRD who underwent 66 infrainguinal bypass proce dures. Mean age was 65.8 ±9.8 years (41-85 years). The 30-day operative mortality rate was 12.3% (7 patients). The cumulative survival at 1 year was 51.8% ±0.9%, and at 2 years it was 32.8% ± 1.3%. The cumulative limb loss was 29.7% ± 1.1% at 1 year and 36.7% ±2.6% at 2 years. In the comparison group, 46 patients without ESRD underwent 50 infrainguinal bypass procedures. The mean age of these patients was 72.3 ±9.1 years (36-90 years). The cumulative survival for the patients without ESRD was significantly higher (p < 0.001) both at 1 year (91.1% ±0.6%) and 2 years (88.8% ±0.8%). The cumulative limb loss for the comparison group was significantly lower (p < 0.001) at 1 year (4.1% ±0.4%) and at 2 years (6.3% ±0.5%). At both 30 days and 1 year, the QL- Class walking status rating was lower for the group with ESRD compared to patients without ESRD (p < 0.001).

Patients with ESRD have a high mortality rate in the first 24 months after infrain guinal bypass grafting for foot salvage surgery compared with a similar group of patients without ESRD. Although foot salvage can be achieved in some survivors with ESRD, the ambulatory rate is low. These results support a very conservative approach when recom mending infrainguinal bypass grafting for foot salvage surgery for patients with ESRD.

Mortality Prediction after the First Year of Kidney Transplantation: An Observational Study on Two European Cohorts

After the first year post transplantation, prognostic mortality scores in kidney transplant recipients can be useful for personalizing medical management. We developed a new prognostic score based on 5 parameters and computable at 1-year post transplantation. The outcome was the time between the first anniversary of the transplantation and the patient’s death with a functioning graft. Afterwards, we appraised the prognostic capacities of this score by estimating time-dependent Receiver Operating Characteristic (ROC) curves from two prospective and multicentric European cohorts: the DIVAT (Données Informatisées et VAlidées en Transplantation) cohort composed of patients transplanted between 2000 and 2012 in 6 French centers; and the STCS (Swiss Transplant Cohort Study) cohort composed of patients transplanted between 2008 and 2012 in 6 Swiss centers. We also compared the results with those of two existing scoring systems: one from Spain (Hernandez et al.) and one from the United States (the Recipient Risk Score, RRS, Baskin-Bey et al.). From the DIVAT validation cohort and for a prognostic time at 10 years, the new prognostic score (AUC = 0.78, 95%CI = [0.69, 0.85]) seemed to present significantly higher prognostic capacities than the scoring system proposed by Hernandez et al. (p = 0.04) and tended to perform better than the initial RRS (p = 0.10). By using the Swiss cohort, the RRS and the the new prognostic score had comparable prognostic capacities at 4 years (AUC = 0.77 and 0.76 respectively, p = 0.31). In addition to the current available scores related to the risk to return in dialysis, we recommend to further study the use of the score we propose or the RRS for a more efficient personalized follow-up of kidney transplant recipients.

Risk factors for in-hospital mortality in patients starting hemodialysis

BACKGROUND

Incident hemodialysis patients have the highest mortality in the first several months after starting dialysis. This study evaluated the in-hospital mortality rate after hemodialysis initiation, as well as related risk factors.

METHODS

We examined in-hospital mortality and related factors in 2,692 patients starting incident hemodialysis. The study population included patients with acute kidney injury, acute exacerbation of chronic kidney disease, and chronic kidney disease. To determine the parameters associated with in-hospital mortality, patients who died in hospital (nonsurvivors) were compared with those who survived (survivors). Risk factors for in-hospital mortality were determined using logistic regression analysis.

RESULTS

Among all patients, 451 (16.8%) died during hospitalization. The highest risk factor for in-hospital mortality was cardiopulmonary resuscitation, followed by pneumonia, arrhythmia, hematologic malignancy, and acute kidney injury after bleeding. Albumin was not a risk factor for in-hospital mortality, whereas C-reactive protein was a risk factor. The use of vancomycin, inotropes, and a ventilator was associated with mortality, whereas elective hemodialysis with chronic kidney disease and statin use were associated with survival. The use of continuous renal replacement therapy was not associated with in-hospital mortality.

CONCLUSION

Incident hemodialysis patients had high in-hospital mortality. Cardiopulmonary resuscitation, infections such as pneumonia, and the use of inotropes and a ventilator was strong risk factors for in-hospital mortality. However, elective hemodialysis for chronic kidney disease was associated with survival.

Development and validation of a predictive mortality risk score from a European hemodialysis cohort

Although mortality risk scores for chronic hemodialysis (HD) patients should have an important role in clinical decision-making, those currently available have limited applicability, robustness, and generalizability. Here we applied a modified Framingham Heart Study approach to derive 1- and 2-year all-cause mortality risk scores using a 11,508 European incident HD patient database (AROii) recruited between 2007 and 2009. This scoring model was validated externally using similar-sized Dialysis Outcomes and Practice Patterns Survey (DOPPS) data. For AROii, the observed 1- and 2-year mortality rates were 13.0 (95% confidence interval (CI; 12.3–13.8)) and 11.2 (10.4–12.1)/100 patient years, respectively. Increasing age, low body mass index, history of cardiovascular disease or cancer, and use of a vascular access catheter during baseline were consistent predictors of mortality. Among baseline laboratory markers, hemoglobin, ferritin, C-reactive protein, serum albumin, and creatinine predicted death within 1 and 2 years. When applied to the DOPPS population, the predictive risk score models were highly discriminatory, and generalizability remained high when restricted by incidence/prevalence and geographic location (C-statistics 0.68–0.79). This new model offers improved predictive power over age/comorbidity-based models and also predicted early mortality (C-statistic 0.71). Our new model delivers a robust and reproducible mortality risk score, based on readily available clinical and laboratory data.

Do clinical symptoms and signs predict reduced renal function among hospitalized adults?

BACKGROUND

Reduced renal function manifests as reduced glomerular filtration rate (GFR), which is estimated using the serum creatinine levels. This condition is frequently encountered among hospitalized adults. Renal dysfunction remains clinically asymptomatic, until late in the course of disease, and its symptoms and screening strategies are poorly defined.

AIM

We conducted this study to understand if the presence of renal dysfunction related clinical symptom and signs (either alone or in combination) can predict reduced GFR. Further, we aimed to determine if the combination of symptoms and signs are useful for prediction of different levels of reduced GFR.

SUBJECTS AND METHODS

We performed a cross-sectional clinical prediction study and included all consecutive patients admitted to the medical wards of the hospital. We used a renal dysfunction related clinical predictors as index tests and low estimated GFR ([eGFR] < 60 ml/min/1.73 m(2)) as a reference standard. We identified symptoms with a high likelihood ratio (LR) for prediction of low eGFR and constructed different risk score models. We plotted receiver operating curves for each score and used area under the curve (AUC) for comparison. The score with the highest AUC was considered as most discriminant. All statistical analysis was performed using the statistical software STATA (version 11.0, lake drive, Texas, USA).

RESULTS

A total of 341 patients participated in the study. None of the predictor variables had statistically significant LRs for eGFR less than 60 ml/min or eGFR less than 30 ml/min. Positive LRs were significant for prediction of eGFR < 15 ml/min for the presence of hypertension, vomiting pruritis, peripheral edema, hyperpigmentation, peripheral neuropathy and severe anemia. The best predictive model for eGFR less than 15 ml/min/1.73 m(2), included Age > 45 years, the presence of hypertension, vomiting, peripheral edema, hyperpigmentation, and severe anemia and had AUC of 0.82.

CONCLUSION

Clinical symptoms and signs are poorly predictive of reduced renal function, except for very low eGFR of less than 15 ml/min/1.73 m(2).

Relationship between major adverse cardiac events and angiographic findings in dialysis patients

BACKGROUND

In dialysis patients, coronary angiography (CA) predicts major adverse coronary events (MACE) better than non-invasive tests. The aim of this study was to investigate in such patients the relationship between coronary atherosclerotic damage shown by angiography and MACE, during an average follow-up period of more than 5 years.

PATIENTS AND METHODS

Coronary angiography was performed in 63 dialysis patients (mean age 56 ± 12 years, 49 men); 37 subjects awaiting kidney transplantation had no history of cardiac disease, whereas the remaining 26 patients had clinical evidence of coronary artery disease (CAD). During a follow-up period of 62 ± 20 months (range 12-109), all the MACE were recorded. Statistical analysis was carried out by dividing the patients into two groups, those who had MACE (MACE group) and those who were free of cardiac events (FCE group). Severe CAD on CA was defined as luminal stenosis ≥ 75% in at least one vessel. Logistic regression analysis and Cox regression analysis were carried out in order to evaluate which variable was associated with MACE.

RESULTS

At the end of follow-up, 17 subjects had MACE and severe CAD was shown in the epicardial arteries of 31 patients (49%). Compared to the FCE group, the MACE group had older age (65 ± 10 vs 53 ± 11 years, P = 0.002), lower diastolic blood pressure (79 ± 7 vs 85 ± 7 mmHg, P = 0.0037), higher prevalence of CAD (82 vs 30%, P = 0.0002) and cerebrovascular disease (41 vs 15%, P = 0.0278). Coronary artery damage was higher in the MACE group than in the FCE group. Logistic and Cox regression analyses showed that age was the only variable independently associated with MACE (OR 1.109 95% CI 1.022-1.204, P = 0.0133, hazard ratio 1.066 95% CI 1.010-1.125, P = 0.02, respectively). After removal of age from the model, MACE were independently associated with haemodynamic stenosis of coronary arteries (OR 7.429 95% CI 1.829-30.173, P = 0.005, hazard ratio 5.992 95% CI 1.655-21.698, P = 0.006, respectively). Event-free survival was much better in the 37 renal transplant candidates with no history of CAD than in the 26 patients who had clinical evidence of CAD.

CONCLUSIONS

This observational study confirms that in dialysis patients coronary atherosclerotic damage shown by angiography is strongly related to MACE and that age and severe CAD are major risk factors for MACE.

Predicting the risk of 1-year mortality in incident dialysis patients: accounting for case-mix severity in studies using administrative data

BACKGROUND

Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population.

OBJECTIVE

Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments.

RESEARCH DESIGN

Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically.

RESULTS

The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration.

CONCLUSION

The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment.

Low serum testosterone, arterial stiffness and mortality in male haemodialysis patients

BACKGROUND

In the general population, accumulating data support a link between low testosterone levels and mortality by all causes, but especially by cardiovascular disease (CVD). Also, accelerated arterial stiffness has been recognized as an important cardiovascular risk factor. Here, we explored the association between testosterone levels and risk of death in male haemodialysis (HD) patients, whose arterial system is characterized by generalized stiffening.

METHODS

In this three-centre prospective observational study, 111 male HD patients after completion of baseline assessment, including measurement of male sex hormones and pulse wave velocity (PWV), were followed up for CVD and all-cause mortality.

RESULTS

Of the 111 patients studied, 54 were found with and 57 without testosterone deficiency, defined as testosterone levels <8 nmol/L. During a median follow-up period of 37 months, 49 deaths occurred, 28 (57%) of which were caused by CVD. Testosterone deficiency patients had increased CVD and all-cause mortality {crude hazard ratio: 3.14 [95% confidence interval (CI), 1.21-8.16] and 3.09 (95% CI, 1.53-6.25), respectively}, even after adjustment for age, body mass index, serum albumin and C-reactive protein, prevalent CVD and HD vintage. The association of testosterone with CVD mortality, but not with all-cause mortality, was lost after adjusting for PWV, an index of arterial stiffness. Testosterone levels were inversely related to PWV (r = -0.441; P < 0.001).

CONCLUSION

We showed that testosterone deficiency in male HD patients is associated with increased CVD and all-cause mortality and that increased arterial stiffness may be a possible mechanism explaining this association.

Dietary potassium intake and mortality in long-term hemodialysis patients

BACKGROUND

Hyperkalemia has been associated with higher mortality in long-term hemodialysis (HD) patients. There are few data concerning the relationship between dietary potassium intake and outcome.

STUDY DESIGN

The mortality predictability of dietary potassium intake from reported food items estimated using the Block Food Frequency Questionnaire (FFQ) at the start of the cohort was examined in a 5-year (2001-2006) cohort of 224 HD patients in Southern California using Cox proportional hazards regression.

SETTING & PARTICIPANTS

224 long-term HD patients from 8 DaVita dialysis clinics.

PREDICTORS

Dietary potassium intake ranking using the Block FFQ.

OUTCOMES

5-year survival.

RESULTS

HD patients with higher potassium intake had greater dietary energy, protein, and phosphorus intakes and higher predialysis serum potassium and phosphorus levels. Greater dietary potassium intake was associated with significantly increased death HRs in unadjusted models and after incremental adjustments for case-mix, nutritional factors (including 3-month averaged predialysis serum creatinine, potassium, and phosphorus levels; body mass index; normalized protein nitrogen appearance; and energy, protein, and phosphorus intake) and inflammatory marker levels. HRs for death across the 3 higher quartiles of dietary potassium intake in the fully adjusted model (compared with the lowest quartile) were 1.4 (95% CI, 0.6-3.0), 2.2 (95% CI, 0.9-5.4), and 2.4 (95% CI, 1.1-7.5), respectively (P for trend = 0.03). Restricted cubic spline analyses confirmed the incremental mortality predictability of higher potassium intake.

LIMITATIONS

FFQs may underestimate individual potassium intake and should be used to rank dietary intake across the population.

CONCLUSIONS

Higher dietary potassium intake is associated with increased death risk in long-term HD patients, even after adjustments for serum potassium level; dietary protein; energy, and phosphorus intake; and nutritional and inflammatory marker levels. The potential role of dietary potassium in the high mortality rate of HD patients warrants clinical trials.

Outcome of patients with end stage renal disease admitted to an intensive care unit in India

AIMS

We sought to determine outcome and evaluate performance of Acute Physiology and Chronic Health Evaluation (APACHE) II and Sequential Organ Failure Assessment (SOFA) scores upon admission in predicting 30-day mortality of end-stage renal disease (ESRD) patients admitted in ICU.

METHODS

This prospective observational cohort study examined 73 consecutive ESRD patients admitted in an ICU of a tertiary care institute over 15 months. Primary outcome measure was 30-day mortality. Data on patient characteristics, reason for ICU admission, cause of ESRD, mode of renal replacement, and use of mechanical ventilation (MV) or inotropes were recorded. The APACHE 2 and SOFA scores were calculated based on admission characteristics.

RESULTS

First-day median APACHE II, SOFA, and APACHE II-predicted hospital mortality rates were 26 (14-49), 7 (4-17), and 56.9% (18.6-97.4%), respectively. Observed ICU and 30-day mortality rates were 27.4%, and 41.1%, respectively. During the ICU course, MV and inotropic support was required in 27 (37%) and 23 (35.1%) patients, respectively. Need for MV (p < 0.001) and inotropic support (p < 0.001) were predictors of 30-day mortality in univariate analysis. Area under receiver operating characteristic curve for APACHE II in predicting 30-day mortality was 0.86 (95% CI, 0.76-0.93) compared with 0.92 (95% CI, 0.83-0.97) for SOFA score (p = 0.16).

CONCLUSIONS

Outcome of ESRD patients admitted to ICU is poor, especially if they require other organ support. APACHE II and SOFA scores perform well as predictors of 30-day mortality.

Mechanisms of the cardiorenal syndromes

The kidney and heart have essential roles in maintaining blood volume homeostasis and in the regulation of systemic blood pressure. Acute or chronic dysfunction in either the heart or kidneys can induce dysfunction in the other organ, resulting in the so-called cardiorenal syndromes, which are classified into five different types. Abrupt worsening of cardiac function predisposes an individual to acute kidney injury from renal hypoperfusion or renal congestion. Progressive, sometimes permanent, chronic kidney impairment can result from chronic renal hypoperfusion or congestion. Heart failure is common in patients with acute kidney injury. Chronic kidney disease predisposes individuals to atherosclerotic, arteriosclerotic and cardiomyopathic disease. Finally, both cardiac and renal disease can also occur secondary to systemic conditions, such as diabetes or autoimmune disease. This Review examines the mechanisms presiding over the first four types of cardiorenal syndromes. These mechanisms provide a template that accounts for the heart-kidney interactions that occur in patients whose concomitant cardiac and renal conditions result from a third cause.

Kidney disease and cardiovascular disease: implications of dyslipidemia

Cardiovascular complications are common inpatients with kidney disease. Regulating the lipid levels in these patients is important so that the risks of kidney and cardiovascular complications can be minimized. Lipid regulation decreases the incidence of coronary vascular events and other vascular complications in patients with kidney disease; however, whether lipid regulation slows progression of kidney disease is not yet known. Additional studies of the implications of dyslipidemia in patients with kidney disease are needed.

The role of vitamin D in left ventricular hypertrophy and cardiac function

The role of vitamin D in left ventricular hypertrophy and cardiac function. Cardiovascular disease is the leading cause of death among patients with end-stage renal disease (ESRD). Traditional cardiac risk factors, as well as other factors specific to the ESRD population such as hyperphosphatemia, elevated calcium and phosphate product, abnormal lipid metabolism, hyperhomocysteinemia, and chronic inflammation play a role in the excessive risk of cardiovascular death in this population. Left ventricular disorders are proven risk factors for cardiac mortality in hemodialysis patients. These disorders are present in incident ESRD patients at rates far above the general population. There is an accumulating body of evidence that suggests that vitamin D plays a role in cardiovascular disease. Abnormal vitamin metabolism, through deficiency of the active form of 1,25-dihydroxyvitamin D(3), and acquired vitamin D resistance through the uremic state, have been shown to be important in ESRD. Vitamin D deficiency has long been known to affect cardiac contractility, vascular tone, cardiac collagen content, and cardiac tissue maturation. Recent studies using vitamin D receptor deficient mice as a model demonstrate a crucial role of vitamin D in regulation of the renin-angiotensin system. Additionally, there is emerging evidence linking treatment with vitamin D to improved survival on hemodialysis and improvement in cardiac function. The emergence of this data is focusing attention on the previously underappreciated nonmineral homeostatic effects of vitamin D that very likely play an important role in the pathogenesis of cardiac disease in ESRD.

Derivation and validation of a disease-specific risk score for cardiac risk stratification in chronic kidney disease

OBJECTIVE

Cardiac events (CE; cardiac death, non-fatal myocardial infarction and acute coronary syndrome) are the principal causes of death in patients with chronic kidney disease (CKD). We sought to devise and validate a cardiac risk score to risk-stratify patients with CKD.

METHODS

Clinical history and biochemical data were obtained in 167 CKD patients. CE were recorded over a median follow-up of 22 months. The hazard ratio (HR) of each independent variable using Cox regression analysis was used to derive a cardiac risk score for the prediction of events. The cardiac risk score was then applied to a validation population of 99 CKD patients to confirm its validity in predicting CE.

RESULTS

CE occurred in 20 patients in the derivation group. The independent predictors of CE were cardiac history (HR 9.83, P = 0.001), body mass index (BMI; HR 1.15, P = 0.002), dialysis duration (HR 1.24, P = 0.004) and serum phosphate (HR 4.29, P = 0.001). The resulting cardiac risk score (range 26-67) gave an area under the receiver operating characteristic curve of 0.86. CE occurred in 25 patients in the validation group; the ROC curve area was similar (0.84, P = 0.11). An optimal cardiac risk score cut-off of 50 assigned high risk to 29% of the derivation and 35% of the validation group (P = 0.26). CE occurred in 35 and 57% of the high-risk derivation and validation groups, respectively (P = 0.09), and in 2 and 8% of the low-risk groups (P = 0.15).

CONCLUSION

Application of a cardiac risk score using cardiac history, dialysis duration, BMI and phosphate identifies CKD patients at risk of future CE.

Outcome of patients with end-stage renal disease admitted to the intensive care unit

OBJECTIVES

To describe the clinical course of patients with end-stage renal disease (ESRD) admitted to the intensive care unit (ICU) and to compare the performance of Acute Physiology and Chronic Health Evaluation (APACHE) III and Sequential Organ Failure Assessment (SOFA) in predicting their outcome.

PATIENTS AND METHODS

This retrospective cohort study consisted of patients with ESRD admitted to 3 ICUs between January 1, 1997, and November 30, 2002. Data on demographics, APACHE III score, SOFA score, development of sepsis and organ failure, use of mechanical ventilation, and mortality were collected.

RESULTS

Of the 476 patients with ESRD who underwent dialysis during the study period, 93 (20%) required admission to the ICU. The most common ICU admission diagnosis was gastrointestinal bleeding. The first day median (Interquartile range) APACHE III score, SOFA score, and APACHE III predicted hospital mortality rate were 64 (47-79), 6 (5-8), and 12.9% (4.2%-30.8%), respectively. The observed ICU, hospital, and 30-day mortality rates were 9%, 16%, and 22%, respectively. Nonrenal organ failure developed in 48 patients (52%) and sepsis in 15 patients (16%). Mechanical ventilation was required In 26 patients (28%). The area under the receiver operating characteristic curve for the first-day APACHE III probability of hospital death in predicting 30-day mortality was 0.78 (95% confidence interval, 0.68-0.86) compared with 0.66 (95% confidence interval, 0.55-0.76) for the SOFA score (P = .16).

CONCLUSIONS

The observed hospital mortality of patients with ESRD admitted to the ICU is relatively low. There is no statistically significant difference in the performance of APACHE III and SOFA prognostic models in discriminating between 30-day survivors and nonsurvivors.

Angiographical severity of coronary atherosclerosis predicts death in the first year of hemodialysis

BACKGROUND

Cardiac deaths and events tend to cluster within the early-phase after starting dialysis. Our goal is to clarify the influence of severity of coronary atherosclerosis on early-phase death after starting hemodialysis (HD) therapy.

PATIENTS AND METHODS

Eighty-three consecutive patients [mean age 62 years; male/female 64/19; diabetic nephropathy in 50 (54%)] with end-stage renal disease who admitted to our hospital to initiate regular HD treatment, and then received coronary angiography within 3 months after first dialysis therapy, were eligible for this study. Angiographical severity of coronary atherosclerosis was scored by numerically using Gensini scoring system. The patients who died within one year from starting HD were compared with those who survived as control by means of logistic regression analysis.

RESULTS

Of 83 patients, 12 (14%) died less than one year after starting dialysis therapy. Of these 12 patients, nine died for cardiac causes. Confirmed predictors of death from cardiac cause were older age (>70 years), lower mean blood pressure (<100 mmHg), presence of ischemic heart disease (IHD), myocardial infarction (MI), angina pectoris (AP), chronic heart failure (CHF), poor cardiac function, abnormal wall motion of left ventricule (LV) and angiographical severity of coronary atherosclerosis by univariate model. Adjusting for confounding variables by multivariate model, only severity of coronary atherosclerosis (Gensini score >40 points) had a powerful influence, increasing risk for cardiac cause of early-phase death by about 17 times.

CONCLUSIONS

Severity of coronary atherosclerosis predicts death in the first year of HD. These findings suggest that the strategy for prevention of coronary atherosclerosis should be instituted during the early phase of chronic renal failure.

End-stage renal disease in Scotland: Outcomes and standards of care

BACKGROUND

The number of patients starting renal replacement therapy (RRT) for end-stage renal disease (ESRD) in the United Kingdom rises annually. Patients are increasingly elderly with a greater prevalence of comorbid illness. Unadjusted survival, from the time of starting RRT, is not improving. The United Kingdom Renal Association has published recommended standards of treatment, which all United Kingdom nephrologists strive to attain. This study was devised to define the impact of attaining recommended treatment standards, adjusting for patient age and comorbid illnesses, upon survival on RRT in the United Kingdom population.

METHODS

A prospective, registry based, observational study of all patients starting RRT in Scotland over a 1-year period, followed for the first 2 years of RRT.

RESULTS

Of the 523 patients who were studied, 217 (41.5%) had died by 2 years of follow-up, 32% excluding deaths within the first 90 days. Age, comorbidity, weight when starting RRT, and attaining the recommended standards for albumin and hemoglobin had a significant impact upon survival.

CONCLUSION

This study has emphasized the very high mortality of patients starting RRT in Scotland. By paying close attention to the attainment of recommended standards of care for patients with ESRD, it may be possible to improve upon current mortality figures. The monitoring of such success is only possible if correction is made for age and comorbidity.

Improved survival in patients with type 1 diabetes mellitus after renal transplantation compared with hemodialysis: a case-control study

BACKGROUND

Diabetes mellitus is the leading cause of renal failure worldwide. The question of which treatment modality-hemodialysis versus renal transplantation-is associated with the lowest risk of cardiovascular morbidity and mortality in the diabetic end-stage renal disease (ESRD) population has not yet been investigated in a controlled trial.

METHODS

We therefore conducted a case-control study of patients with ESRD caused by type 1 diabetes mellitus. The case patients were diabetics who received a renal graft between 1978 and 1997, whereas the controls were registered for renal transplantation but stayed on maintenance hemodialysis without ever undergoing transplantation. The groups were matched for age, sex, duration of diabetes, length of hemodialysis (up to the registration), and date of registration for renal transplantation.

RESULTS

Kaplan-Meier life table analysis, based on 46 case patients and 46 controls, demonstrated a highly significant (P=0.0001) poorer survival in the control group compared with the case group. Logistic regression showed that hemodialysis was a significant risk factor for death (P=0.0002) and cardiovascular morbidity (P=0.0023). Patients with cardiovascular complications such as coronary artery and peripheral vascular events were significantly more frequent in the control group. Additionally tested risk factors for cardiovascular complications (serum cholesterol, arterial blood pressure, number of antihypertensive drugs, serum calcium, serum phosphate, and glucose control [hemoglobin A(1c)]) showed no significant correlation to survival or morbidity in either group by logistic regression.

CONCLUSIONS

Renal transplantation is associated with a significantly improved survival compared with hemodialysis in patients with ESRD caused by type 1 diabetes mellitus. This seems to be a result of a reduced incidence of cardiovascular complications after renal transplantation.

[Analyses of factors concerning the short-term outcome of elderly patients beginning dialysis]

The aim of the present study is to clarify relevant factors concerning the short-term outcome of elderly patients beginning dialysis. One hundred nineteen patients aged 60 years and over who had newly started dialysis at our hospital were studied. The male/female ratio was 70:49. The age was 74 +/- 7 years (mean +/- standard deviation). In all patients, the timing of referral to a nephrologist (early/late), the urgency of the initiation of dialysis (non-urgent/urgent), the cause of renal failure (nondiabetes/diabetes), serum albumin concentration, comorbid conditions (cerebrovascular disease, ischemic heart disease, etc.), ambulation, cognitive function, and the outcome (relief/death) were surveyed. Twelve patients did not obtain relief and finally died. The influence of the timing of referral on the urgency of the initiation of dialysis was studied. Furthermore the influence of the urgency of the initiation of dialysis on the outcome was studied. The chi 2 test was used for statistical comparisons. The need for urgent dialysis was less among early referral cases as compared with late referral cases (p < 0.0001). The incidence of death was more frequent in urgent dialysis than in non-urgent dialysis (p = 0.016). Multivariate logistic regression analysis was performed using background factors as explanatory variables and the outcome as a dependent variable. Statistically significant factors were the urgency of the initiation (p = 0.040), serum albumin concentration (p = 0.022), and cerebrovascular disease (p = 0.002). The most common cause of death was severe infectious diseases (pneumonia, sepsis). It was speculated that background factors associated with the outcome could contribute to the onset and the progression of infectious diseases.

Adapting the Charlson Comorbidity Index for use in patients with ESRD

BACKGROUND

Accurate prediction of survival for patients with end-stage renal disease (ESRD) and multiple comorbid conditions is difficult. In nondialysis patients, the Charlson Comorbidity Index has been used to adjust for comorbidity. The purpose of this study is to assess the validity of the Charlson index in incident dialysis patients and modify the index for use specifically in this patient population.

METHODS

Subjects included all incident hemodialysis and peritoneal dialysis patients starting dialysis therapy between July 1, 1999, and November 30, 2000. These 237 patients formed a cohort from which new integer weights for Charlson comorbidities were derived using Cox proportional hazards modeling. Performance of the original Charlson index and the new ESRD comorbidity index were compared using Kaplan-Meier survival curves, change in likelihood ratio, and the c statistic.

RESULTS

After multivariate analysis and conversion of hazard ratios to index weights, only 6 of the original 18 Charlson variables were assigned the same weight and 6 variables were assigned a weight higher than in the original Charlson index. Using Kaplan-Meier survival curves, we found that both the original Charlson index and the new ESRD comorbidity index were associated with and able to describe a wide range of survival. However, the new study-specific index had better validated performance, indicated by a greater change in the likelihood ratio test and higher c statistic.

CONCLUSION

This study indicates that the original Charlson index is a valid tool to assess comorbidity and predict survival in patients with ESRD. However, our modified ESRD comorbidity index had slightly better performance characteristics in this population.

Management of heart failure and coronary artery disease in patients with chronic kidney disease

Cardiovascular disease (CVD) is a major contributor to the mortality and morbidity of patients who suffer from chronic kidney disease (CKD). Heart failure and ischemic heart disease (IHD) are both highly prevalent in this population. The diagnosis of myocardial dysfunction is usually based on echocardiography. As in the general population, systolic dysfunction is treated with a combination of diuretics, renin-angiotensin system blockade, and beta-receptor antagonists. Diastolic dysfunction is best managed by eliminating the cause. Non-invasive tests for coronary artery disease (CAD) may be less reliable in patients with renal disease compared with nonuremic patients. Medical therapy of IHD in this population is generally similar to that for other patient groups, but surgical revascularization appears to carry a higher risk of complications with poorer clinical outcomes. The choice of revascularization procedure (coronary artery bypass grafting versus percutaneous transluminal angioplasty) should be based on the specific coronary anatomy of a given patient as well as a consideration of other comorbid factors.

Comorbidity and its change predict survival in incident dialysis patients

BACKGROUND

Few studies have performed a comprehensive comparison of the prognostic importance of comorbidity to that of other case-mix factors influencing incident dialysis patients' survival. Longitudinal change in the comorbid illness burden of incident dialysis patients has not been measured. Comorbidity severity and its change may serve as important prognostic markers of survival, independent of other case-mix factors.

METHODS

The Choices for Healthy Outcomes in Caring for End-Stage Renal Disease Cohort Study used the Index of Coexistent Disease (ICED) to assess comorbidity at the initiation of chronic dialysis treatment (1,039 incident patients) and during follow-up (733 patients). Using proportional hazards regression analyses, the relationship to survival of baseline ICED level and change in ICED level was examined.

RESULTS

At the initiation of chronic dialysis treatment, 36% of patients were at ICED level 0 to 1 (least comorbidity severity); 35%, level 2; and 29%, level 3. After multivariable adjustment, baseline ICED level was the strongest predictor of subsequent mortality. Compared with ICED level 0 to 1, relative risks for mortality were 1.9 (95% confidence interval, 1.3 to 2.6) for ICED level 2 and 2.8 (95% confidence interval, 2.0 to 3.9) for ICED level 3. The prevalence and severity of most comorbid conditions increased during follow-up. After controlling for baseline ICED level and other factors, change in ICED level over time was significantly associated with mortality (P = 0.01).

CONCLUSION

Indexing comorbidity when patients begin chronic dialysis therapy and recording the evolution of index scores yields a predictor of mortality risk that is independent of other case-mix factors.

Treatment of the diabetic patient: focus on cardiovascular and renal risk reduction

Diabetes mellitus increases the risk for hypertension and associated cardiovascular diseases, including coronary, cerebrovascular, renal and peripheral vascular disease. The risk for developing cardiovascular disease is increased when both diabetes and hypertension co-exist; in fact, over 11 million Americans have both diabetes and hypertension. These numbers will continue to climb, internationally, since the leading associated risk for diabetes development, obesity, has reached epidemic proportions, globally. Moreover, the frequent association of diabetes with dyslipidemia, as well as coagulation, endothelial, and metabolic abnormalities also aggravates the underlying vascular disease process in patients who possess these comorbid conditions. The renin-angiotensin-aldosterone system (RAS) and arginine vasopressin (AVP) are overactivated in both hypertension and diabetes. Drugs that inhibit this system, such as ACE inhibitors and more recently angiotensin receptor antagonists (ARBs), have proven beneficial effects on the micro- and macrovascular complications of diabetes, especially the kidney. The BRILLIANT study showed that lisinopril reduces microalbuminuria better than CCB therapy. Numerous other long-term studies confirm this association with ACE inhibitors including the HOPE trial. Furthermore, the European Controlled trial of Lisinopril in Insulin-dependent Diabetes (EUCLID) study, showed that lisinopril slowed the progression of renal disease, even in individuals with mild albuminuria. In fact, there are now five appropriately powered randomized placebo-controlled trials to show that both ACE inhibitors and ARBs slow progression of diabetic nephropathy in people with type 2 diabetes. These effects were shown to be better than conventional blood pressure lowering therapy, including dihydropyridine CCBs. In patients with microalbuminuria, ACE inhibitors and ARBs reduce the progression of microalbuminuria to proteinuria and provide a risk reduction of between 38 and 60% for progression to proteinuria. This is important since microalbuminuria is known to be associated with increased vascular permeability and decreased responsiveness to vasodilatory stimuli. Recently, increased AVP levels have been lined to microalbuminuria and hyperfiltration in diabetes. The microvascular and macrovascular benefits of ACE inhibition, ARBs and possible role of AVP antagonists in diabetic patients will be discussed, as will be recommendations for its clinical use.

Slowing the progression of chronic renal failure: economic benefits and patients' perspectives

Because of the predicted increase in end-stage renal disease (ESRD) incidence (projected increase from 1998 to 2010; 86,825 to 172,667), prevalence (projected increase from 1998 to 2010; 326,217 to 661,330), and cost (total cost based on 1998 ratio of Medicare versus non-Medicare cost; $16.74 billion in 1998 to $39.35 billion in 2010), a cohesive national effort is needed to develop strategies to slow the progression of chronic renal failure (CRF). The question arises to how much reduction in the progression of CRF would lead to a meaningful decrease in the prevalence and cost of ESRD. There are no objective data that show the economic impact of slowing the progression of CRF. We developed a mathematical model to assess the economic impact of decreasing the progression of CRF by 10%, 20%, and 30%. US Renal Data System (USRDS) projections were used to model the rate of increase in ESRD incidence and prevalence. Glomerular filtration rate (GFR) at the initiation of ESRD therapy and cost per patient-year were based on USRDS data. The average decline in GFR in subjects with CRF was estimated to be 7.56 mL/min/y. All dollar savings reflect 1998 costs, discounted for the future at 3% per annum. We also determined how much slowing of the progression of CRF is important from patients' perspectives by means of a written questionnaire (which inquired about willingness to go on a restricted diet, take six extra medications per day, and make six extra office visits per year) and calculation of the pre-ESRD time gained for different degrees of reduction in the progression of CRF. If the rate of decline in GFR decreased by 10%, 20%, and 30% after December 31, 1999, in all patients with GFRs of 60 mL/min or less, cumulative direct healthcare savings through 2010 would equal approximately $18.56, $39.02, and $60.61 billion, respectively. For a 10%, 20%, and 30% decrease in the rate of decline in GFR in all patients with a GFR of 30 mL/min or less, estimated cumulative savings through 2010 equal $9.06, $19.98, and $33.37 billion, respectively. Responses to the questionnaire showed that approximately 79% of subjects with CRF (n = 113) perceived a few weeks' dialysis-free period significant (P < or = 0.0001), a period corresponding to a 10% reduction in the rate of decline in GFR. Our data suggest that the cumulative economic impact of slowing the progression of CRF, even by as little as 10%, would be staggering. They provide strong support for the development and implementation of intensive reno-protective efforts beginning at the early stages of chronic renal disease and continued throughout its course.

Comorbidity assessment using the Index of Coexistent Diseases in a multicenter clinical trial

BACKGROUND

The Hemodialysis (HEMO) Study is a multicenter trial designed to determine whether hemodialysis dose and membrane flux affect survival. Comorbid conditions are also important determinants of survival, and thus, an accurate and reliable method to assess comorbidity was required. Comorbidity was being assessed at baseline and annually in the HEMO Study using the Index of Coexistent Disease (ICED). We describe the instrument, its implementation in the HEMO Study, and the results of comorbidity assessment in the first 1000 randomized patients in the trial.

METHODS

The ICED aggregated the presence and severity of 19 medical conditions and 11 physical impairments within two scales: the Index of Disease Severity (IDS) and the Index of Physical Impairment (IPI). The final ICED score was determined by an algorithm combining the peak scores for the IDS and IPI. The range of the ICED was from 0 to 3, reflecting increasing severity.

RESULTS

Study personnel at 15 clinical centers were trained to update and abstract data from the dialysis medical records. Availability of data, measures of construct validity, and measures of reliability were adequate; 99.8% and 60.6% of patients had comorbid conditions in at least one IDS or IPI category, respectively. The distribution of patients by ICED level was 0 (0.2%), 1 (34.9%), 2 (31.2%), and 3 (33.7%). In multivariable analysis, the following factors were significantly associated with more severe comorbidity: older age, diabetes and other causes of renal disease, a lower level of education, employment status (unemployed and retired), longer duration of dialysis, and lower serum creatinine. There was a significant variation in the severity of comorbidity among clinical centers after adjustment for other factors. The R2 of the model was 25.3%, indicating that a substantial proportion of the variation in the ICED was not explained by these factors.

CONCLUSIONS

We conclude that comorbidity assessment using the ICED is feasible in multicenter clinical trials of dialysis patients. There is a large burden of comorbidity in dialysis patients, which is not well explained by the cause of renal disease, demographic, and socioeconomic factors and common clinical and laboratory measurements. These variables should not be considered substitutes for comorbid conditions in case-mix adjustment. Comorbidity assessment is useful to describe the sample population, to improve the precision of the treatment effect, and to use possibly as an outcome measurement.

Cardiac arrest and sudden death in dialysis units

BACKGROUND

For patients with end-stage renal disease and their providers, dialysis unit-based cardiac arrest is the most feared complication of hemodialysis. However, relatively little is known regarding its frequency or epidemiology, or whether a fraction of these events could be prevented.

METHODS

To explore clinical correlates of dialysis unit-based cardiac arrest, 400 reported arrests over a nine-month period from October 1998 through June 1999 were reviewed in detail. Clinical characteristics of patients who suffered cardiac arrest were compared with a nationally representative cohort of> 77,000 hemodialysis patients dialyzed at Fresenius Medical Care North America-affiliated facilities.

RESULTS

The cardiac arrest rate was 400 out of 5,744,708, corresponding to a rate of 7 per 100,000 hemodialysis sessions. Cardiac arrest was more frequent during Monday dialysis sessions than on other days of the week. Case patients were nearly twice as likely to have been dialyzed against a 0 or 1.0 mEq/L potassium dialysate on the day of cardiac arrest (17.1 vs. 8.8%). Patients who suffered a cardiac arrest were on average older (66.3 +/- 12.9 vs. 60.2 +/- 15.4 years), more likely to have diabetes (61.8 vs. 46.8%), and more likely to use a catheter for vascular access (34.1 vs. 27.8%) than the general hemodialysis population. Sixteen percent of patients experienced a drop in systolic pressure of 30 mm Hg or more prior to the arrest. Thirty-seven percent of patients who suffered cardiac arrest had been hospitalized within the past 30 days. Sixty percent of patients died within 48 hours of the arrest, including 13% while in the dialysis unit.

CONCLUSIONS

Cardiac arrest is a relatively infrequent but devastating complication of hemodialysis. To reduce the risk of adverse cardiac events on hemodialysis, the dialysate prescription should be evaluated and modified on an ongoing basis, especially following hospitalization in high-risk patients.

Comorbidity assessment in hemodialysis and peritoneal dialysis using the index of coexistent disease

The purpose of this paper is to describe the ICED, summarize outcomes of prior studies in which it was used, and describe the adaptations that have lead to the present instrument. We will then demonstrate its use in quantifying the burden of comorbid conditions in a sample of hemodialysis and peritoneal dialysis patients from our center, and show the relationship between ICED levels and outcomes in peritoneal dialysis patients.

Comparative hospitalization of hemodialysis and peritoneal dialysis patients in Canada

BACKGROUND

Most comparisons of hemodialysis (HD) and peritoneal dialysis (PD) have used mortality as an outcome. Relatively few studies have directly compared the hospitalization rates, an outcome of perhaps equal importance, of patients using these different dialysis modalities.

METHODS

Eight hundred twenty-two consecutive patients at 11 Canadian institutions with irreversible renal failure had an extensive assessment of comorbid illness and initial mode of dialysis collected prospectively immediately prior to starting dialysis therapy. The cohort was assembled between March 1993 and November 1994. The mean follow-up was 24 months. Admission data were used to compare hospitalization rates in HD and PD.

RESULTS

Thirty-four percent of patients at baseline and 50% at three months used PD. Twenty-five percent of HD and 32% of PD patients switched dialysis modality at least once after their first treatment (P = NS). Nine percent of HD patients and 30% of PD patients switched modality after three months (P < 0. 001). Total comorbidity was higher in HD patients at baseline (P < 0. 001) and at three months (P = 0.001). The overall hospitalization rate was 40.2 days per 1000 patient days after baseline and 38.0 days per 1000 patient days after three months. When an adjustment was made for baseline comorbid conditions, patients on PD had a lower rate of hospitalization in intention-to-treat analysis according to the type of dialysis in use at baseline (RR 0.85, 95% CI, 0.82 to 0.87, P < 0.001), but a higher rate according to the type of dialysis in use three months after study entry (RR 1.31, 95% CI, 1.27 to 1.34, P < 0.001). In analyses based on the amount of time actually spent on each treatment modality, PD was associated with a higher rate of hospitalization when analyzed according to the type of dialysis in use at baseline (RR 1.10, 95% CI, 1.07 to 1.13, P < 0.001) and according to the type of dialysis in use three months after study entry (RR 1.26, 95% CI, 1.23 to 1.30, P < 0.001).

CONCLUSIONS

Conclusions regarding comparative hospitalization rates are heavily dependent on the analytic starting point and on whether intention-to-treat or treatment-received analyses are used. When early treatment switches are accounted for, HD is associated with a lower rate of hospitalization than PD, but the effect is modest.

Comparative mortality of hemodialysis and peritoneal dialysis in Canada

BACKGROUND

Comparisons of mortality rates in patients on hemodialysis versus those on peritoneal dialysis have been inconsistent. We hypothesized that comorbidity has an important effect on differential survival in these two groups of patients.

METHODS

Eight hundred twenty-two consecutive patients at 11 Canadian institutions with irreversible renal failure had an extensive assessment of comorbid illness collected prospectively, immediately prior to starting dialysis therapy. The cohort was assembled between March 1993 and November 1994; vital status was ascertained as of January 1, 1998.

RESULTS

The mean follow-up was 24 months. Thirty-four percent of patients at baseline, 50% at three months, and 51% at six months used peritoneal dialysis. Values for a previously validated comorbidity score were higher for patients on hemodialysis at baseline (4.0 vs. 3.1, P < 0.001), three months (3.7 vs. 3.2, P = 0.001), and six months (3.6 vs. 3.2, P = 0.005). The overall mortality was 41%. The unadjusted peritoneal dialysis/hemodialysis mortality hazard ratios were 0.65 (95% CI, 0. 51 to 0.83, P = 0.0005), 0.84 (95% CI, 0.66 to 1.06, P = NS), and 0. 83 (95% CI, 0.64 to 1.08, P = NS) based on the modality of dialysis in use at baseline, three months, and six months, respectively. When adjusted for age, sex, diabetes, cardiac failure, myocardial infarction, peripheral vascular disease, malignancy, and acuity of renal failure, the corresponding hazard ratios were 0.79 (95% CI, 0. 62 to 1.01, P = NS), 1.00 (95% CI, 0.78 to 1.28, P = NS), and 0.95 (95% CI, 0.73 to 1.24, P = NS). Adjustment for a previously validated comorbidity score resulted in hazard ratios of 0.74 (95% CI, 0.58 to 0.94, P = 0.01), 0.94 (95% CI, 0.74 to 1.19, P = NS), and 0.88 (95% CI, 0.68 to 1.13, P = NS) at baseline, three months, and six months. There was no survival advantage for either modality in any of the major subgroups defined by age, sex, or diabetic status.

CONCLUSIONS

The apparent survival advantage of peritoneal dialysis in Canada is due to lower comorbidity and a lower burden of acute onset end-stage renal disease at the inception of dialysis therapy. Hemodialysis and peritoneal dialysis, as practiced in Canada in the 1990s, are associated with similar overall survival rates.

Left ventricular morphology in chronic renal failure by echocardiography

M-mode, two-dimensional, and Doppler echocardiography were performed in 38 chronic renal failure (CRD) patients on conservative management, 35 patients on hemodialysis, and 36 matched controls. The controls were matched for age, sex, and comorbidities. The incidence of hypertension, left ventricular (LV) end diastolic volume, LV end systolic volume, and LV mass index were significantly higher in patients on hemodialysis compared to the controls. The LV parameters in the predialysis patients were not significantly different from the controls, except the LV end systolic internal dimensions were significantly higher in the CRF patients. Multiple regression analysis underscored the strong association between increase in LV mass index (LVMI) and hypertension. The diabetic patients with renal failure had large LV internal diameter and end diastolic volume compared to non-diabetics. Systolic function was well preserved even in hypertensive and diabetic patients with uremia. The incidence of diastolic dysfunction and asymmetrical septal hypertrophy were not significantly different in the three groups of patients.

Prediction of early death in end-stage renal disease patients starting dialysis

Demand for dialysis for patients with end-stage renal disease is growing, as is the comorbidity of dialysis patients. Accurate prediction of those destined to die quickly despite dialysis could be useful to patients, providers, and society in making decisions about starting dialysis. To determine whether age and comorbidity accurately predict death within 6 months of first dialysis for end-stage renal disease, a prospective cohort study of 822 patients starting dialysis at one of 11 Canadian centers was performed. Patient characteristics were recorded at first dialysis. Follow-up continued until death or study end (at least 6 months after enrollment). One hundred thirteen of 822 (13.7%) patients died within 6 months. Although an existing scoring system predicted prognosis, adverse scores greater than 9 were found in only 9.7% of those who died; only 52% of those who scored higher than 9 died within 6 months. No score cutoff point combined high true-positive and low false-positive rates for predicting early death. Age, severity of heart failure or peripheral vascular disease, arrhythmias, malnutrition, malignancy, or myeloma were independent prognostic factors identified in multivariate models. However, the best fit discriminant and logistic models were also unable to accurately predict death within 6 months. Clinicians were very accurate in assigning patients to prognostic groups up to a 50% risk of death by 6 months, above which they tended to overestimate risk. However, clinicians were only marginally better than the predictive models in determining whether a given high-risk patient would die. The inability of a scoring system or clinical intuition to accurately predict death soon after starting dialysis for end-stage renal disease suggests that limiting access to dialysis on the basis of likely short survival may be inappropriate in Canada.