Socioeconomic measures and CKD in the United States and The Netherlands

BACKGROUND AND OBJECTIVES

According to the cost of health care utilization systems, there may be regional differences in the relative strength of association of income and education-based socioeconomic status measures with CKD. This study investigated the relative strength of the association of income and education with CKD in a United States and a Dutch population.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

This cross-sectional study examined individuals who participated in the 1999-2002 National Health and Nutritional Examination Survey (NHANES) and in Prevention of Renal and Vascular End-stage Disease (PREVEND 1997-1998), general population-based cohorts in the United States and The Netherlands, respectively. The main outcome was CKD, defined as estimated GFR <60 ml/min per 1.73 m(2) (using creatinine) or albuminuria ≥ 30 mg/24 hours or albumin-to-creatinine ratio ≥ 30 mg/g.

RESULTS

In NHANES (n=6428), income was strongly associated with CKD (adjusted odds ratio, 2.34 [95% confidence interval (CI), 1.68 to 3.27]; P for trend<0.001) but education was not (adjusted odds ratio, 1.62 [95% CI, 0.87 to 2.25]; P for trend=0.05]. In contrast, in PREVEND (n=7983), low income was weakly associated with CKD whereas low education had a strong association. The fit of the logistic regression model estimating association of income and education with CKD was significantly improved only after income was added in NHANES (P<0.001) and education was added in PREVEND (P=0.01). Sensitivity analyses that used other CKD-defining variables and restricted analyses to participants <65 years of age resulted in similar findings.

CONCLUSION

In the United States, where access to health care is traditionally income dependent, income appeared more strongly associated with CKD than in The Netherlands, where education showed a stronger association.

Screening for chronic kidney disease can be of help to prevent atherosclerotic end-organ damage

Atherosclerotic damage to the kidney is one of the most prevalent causes of chronic kidney disease and ultimately kidney failure. It frequently coincides with atherosclerotic damage to the heart, the brain and the lower extremities. In fact, the severity of the damage in the various end organs runs in parallel. As damage to the kidney is easy to measure by monitoring albuminuria and eGFR, and as the early phases of kidney damage frequently precede the alarming symptomatology in the heart, brain and peripheral vasculature, we argue that the nephrologist should consider taking the lead in better organizing early detection and management of CKD. The nephrologist can guide the general practitioner and general health care workers to offer better preventive care to the subjects at risk of progressive atherosclerotic end-organ damage.

Predictors of progression in albuminuria in the general population: results from the PREVEND cohort

Background

Urinary albumin excretion is known to be independently associated with progression of renal and cardiovascular disease. The aim of this study was to identify predictors for progression in albuminuria in the general population.

Methods

Data were used of the first 4 screening rounds of a community-based prospective cohort study (PREVEND). Included were 5,825 subjects that at baseline had no known renal disease or macroalbuminuria. Subjects were defined as having progressive albuminuria when they belonged to the quintile of subjects with highest absolute increase in urinary albumin excretion per year and a urinary albumin excretion during the last screening in which they participated of ≥150 mg/24 h. Change in urinary albumin excretion per year was calculated as last available urinary albumin excretion minus baseline UAE divided by follow-up time.

Results

During 9.3 years follow-up 132 subjects had progressive albuminuria. These subjects were significantly older, more often of male gender and had a worse cardiovascular risk profile. In a multivariable model, testing baseline values, significant predictors of progressive albuminuria were male gender (OR 2.23; p<0.001), age (OR 1.03; p<0.001), BMI (OR 1.06; p = 0.02) and baseline albuminuria (OR 5.71; p<0.001). Based on these findings a risk score was made to estimate a subject's risk for progressive albuminuria.

Conclusion

A high baseline albuminuria is by far the most important predictor of progressive albuminuria. Thus, screening for baseline albuminuria will be more important than screening for cardiovascular risk factors in order to identify subjects at risk for progressive albuminuria.

A simple tool to predict end-stage renal disease within 1 year in elderly adults with advanced chronic kidney disease

OBJECTIVES

To develop and validate a model to predict 1-year risk of end-stage renal disease (ESRD) in elderly subjects with advanced chronic kidney disease (CKD).

DESIGN

Retrospective.

SETTING

Veterans Affairs Medical Center.

PARTICIPANTS

Individuals aged 65 and older with CKD with an estimated glomerular filtration rate (eGFR) less than 30 mL/min per 1.73 m(2) .

MEASUREMENTS

The outcome was ESRD within 1 year of the index eGFR. Cox regression was used to develop a predictive model (Veterans Affairs (VA) risk score) that was validated in a separate cohort.

RESULTS

Of the 1,866 participants in the developmental cohort, 77 developed ESRD. Risk factors for ESRD in the final model were age, congestive heart failure, systolic blood pressure, eGFR, potassium, and albumin. In the validation cohort, the C index for the VA risk score was 0.823. The risk for developing ESRD at 1 year from lowest to highest tertile was 0.08%, 2.7%, and 11.3% (P < .001). The C-index for the recently published Tangri model in the validation cohort was 0.780.

CONCLUSION

A new model using commonly available clinical measures shows excellent ability to predict the onset of ESRD within the next year in elderly adults. The Tangri model also had good predictive ability. Individuals and physicians can use these risk models to inform decisions regarding preparation for renal replacement therapy in individuals with advanced CKD.

A systematic review finds prediction models for chronic kidney disease were poorly reported and often developed using inappropriate methods

BACKGROUND

Chronic kidney disease (CKD) is a global health concern that is increasing mainly as the result of increasing incidences of diabetes and hypertension. Furthermore, if left untreated, individuals with CKD may progress to end-stage kidney failure. Identifying individuals with undiagnosed CKD or those who are at an increased risk of developing CKD or progressing to end-stage kidney disease (ESKD) is therefore an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) CKD or end-stage kidney failure in adults.

METHODS

We conducted a systematic search of PubMed database to identify studies published up until September 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident CKD or ESKD. We extracted key information that describes aspects of developing a prediction model, including the study design, data quality, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies, and aspects of performance.

RESULTS

Eleven studies describing the development of 14 prediction models were included. Eight studies reported the development of 11 models to predict incident CKD or ESKD, whereas 3 studies developed models for prevalent CKD. A total of 97 candidate risk predictors were considered, and 43 different risk predictors featured in the 14 prediction models. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in six studies. Missing data were frequently poorly handled and reported with no mention of missing data in four studies; 4 studies explicitly excluded individuals with missing data, and only 2 studies used multiple imputation to replace missing values.

CONCLUSION

We found that prediction models for chronic kidney were often developed using inappropriate methods and were generally poorly reported. Using poor methods can affect the predictive ability of the models, whereas inadequate reporting hinders an objective evaluation of the potential usefulness of the model.

Predicting the risk of chronic kidney disease in the UK: an evaluation of QKidney® scores using a primary care database

BACKGROUND

Chronic kidney disease is a major health concern that, if left untreated, may progress to end-stage kidney failure (ESKF). Identifying individuals at an increased risk of kidney disease and who might benefit from a therapeutic or preventive intervention is an important challenge.

AIM

To evaluate the performance of the QKidney® scores for predicting 5-year risk of developing moderate-severe kidney disease and ESKF in an independent UK cohort of patients from general practice records.

DESIGN AND SETTING

Prospective cohort study to evaluate the performance of two risk scores for kidney disease in 364 practices from the UK, contributing to The Health Improvement Network (THIN) database.

METHOD

Data were obtained from 1.6 million patients registered with a general practice surgery between 1 January 2002 and 1 July 2008, aged 35-74 years, with 43,186 incident cases of moderate-severe kidney disease and 2663 incident cases of ESKF. This is the first recorded evidence of moderate-severe chronic kidney and ESKF as recorded in general practice records.

RESULTS

The results from this independent and external validation of QKidney scores indicate that both scores showed good performance data for both moderate-severe kidney disease and ESKF, on a large cohort of general practice patients. Discrimination and calibration statistics were better for models including serum creatinine; however, there were considerable amounts of missing data for serum creatinine. QKidney scores both with and without serum creatinine were well calibrated.

CONCLUSION

QKidney scores have been shown to be useful tools to predict the 5-year risk of moderate-severe kidney disease and ESKF in the UK.

Risk models to predict chronic kidney disease and its progression: a systematic review

BACKGROUND

Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.

METHODS AND FINDINGS

We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.

CONCLUSIONS

The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored.