Prognostic models for chronic kidney disease: a systematic review and external validation.

BACKGROUND: Accurate risk prediction is needed in order to provide personalized healthcare for chronic kidney disease (CKD) patients. An overload of prognosis studies is being published, ranging from individual biomarker studies to full prediction studies. We aim to systematically appraise published prognosis studies investigating multiple biomarkers and their role in risk predictions. Our primary objective was to investigate if the prognostic models that are reported in the literature were of sufficient quality and to externally validate them. METHODS: We undertook a systematic review and appraised the quality of studies reporting multivariable prognosis models for end-stage renal disease (ESRD), cardiovascular (CV) events and mortality in CKD patients. We subsequently externally validated these models in a randomized trial that included patients from a broad CKD population. RESULTS: We identified 91 papers describing 36 multivariable models for prognosis of ESRD, 50 for CV events, 46 for mortality and 17 for a composite outcome. Most studies were deemed of moderate quality. Moreover, they often adopted different definitions for the primary outcome and rarely reported full model equations (21% of the included studies). External validation was performed in the Multifactorial Approach and Superior Treatment Efficacy in Renal Patients with the Aid of Nurse Practitioners trial (n = 788, with 160 events for ESRD, 79 for CV and 102 for mortality). The 24 models that reported full model equations showed a great variability in their performance, although calibration remained fairly adequate for most models, except when predicting mortality (calibration slope >1.5). CONCLUSIONS: This review shows that there is an abundance of multivariable prognosis models for the CKD population. Most studies were considered of moderate quality, and they were reported and analysed in such a manner that their results cannot directly be used in follow-up research or in clinical practice.

Performance of creatinine-based equations for estimating glomerular filtration rate changes over time.

BACKGROUND: Glomerular filtration rate (GFR) is commonly used to monitor chronic kidney disease (CKD) progression, but its validity for evaluating kidney function changes over time has not been comprehensively evaluated. We assessed the performance of creatinine-based equations for estimating GFR slope according to patient characteristics and specific CKD diagnosis. METHODS: In the NephroTest cohort study, we measured GFR 5324 times by chromium 51-labeled ethylenediamine tetraacetic acid renal clearance in 1955 adult patients with CKD Stages 1-4 referred to nephrologists (Stages 1-2, 19%) and simultaneously estimated GFR with both the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) equations for isotope dilution mass spectrometry traceable creatinine; absolute and relative GFR slopes were calculated using a linear mixed model. RESULTS: Over a median follow-up of 3.4 [interquartile range (IQR) 2.0-5.6] years, the decline in mean absolute and relative measured GFR (mGFR) and CKD-EPI and MDRD estimated GFR (eGFR) was 1.6 ± 1.2, 1.5 ± 1.4 and 1.3 ± 1.3 mL/min/1.73 m2/year and 5.9 ± 5.3, 5.3 ± 5.3 and 4.8 ± 5.2%/year, respectively; 52% and 55% of the patients had MDRD and CKD-EPI eGFR slopes within 30% of mGFR slopes. Both equations tended to overestimate the GFR slope in the youngest patients and underestimate it in the oldest, thus producing inverse associations between age and mGFR versus eGFR slope. Other patient characteristics and specific CKD diagnoses had little effect on the performance of the equations in estimating associations. CONCLUSIONS: This study shows little bias, but poor precision in GFR slope estimation for both MDRD and CKD-EPI equations. Importantly, bias strongly varied with age, possibly due to variations in muscle mass over time, with implications for clinical care and research.

Statistical significance versus clinical relevance.

In March this year, the American Statistical Association (ASA) posted a statement on the correct use of P-values, in response to a growing concern that the P-value is commonly misused and misinterpreted. We aim to translate these warnings given by the ASA into a language more easily understood by clinicians and researchers without a deep background in statistics. Moreover, we intend to illustrate the limitations of P-values, even when used and interpreted correctly, and bring more attention to the clinical relevance of study findings using two recently reported studies as examples. We argue that P-values are often misinterpreted. A common mistake is saying that P < 0.05 means that the null hypothesis is false, and P ≥0.05 means that the null hypothesis is true. The correct interpretation of a P-value of 0.05 is that if the null hypothesis were indeed true, a similar or more extreme result would occur 5% of the times upon repeating the study in a similar sample. In other words, the P-value informs about the likelihood of the data given the null hypothesis and not the other way around. A possible alternative related to the P-value is the confidence interval (CI). It provides more information on the magnitude of an effect and the imprecision with which that effect was estimated. However, there is no magic bullet to replace P-values and stop erroneous interpretation of scientific results. Scientists and readers alike should make themselves familiar with the correct, nuanced interpretation of statistical tests, P-values and CIs.

Worldwide Disparity in the Relation Between CKD Prevalence and Kidney Failure Risk.

INTRODUCTION: The incidence of kidney replacement therapy (KRT) for kidney failure varies internationally much more than chronic kidney disease (CKD) prevalence. This ecologic study investigated the relation of CKD prevalence to KRT and mortality risks by world region. METHODS: We used data from Global Burden of Disease and KRT registries worldwide with linear models to estimate the percentages of variance in KRT incidence and all-cause mortality explained by age-adjusted prevalence of CKD stages 3 to 5, overall and by gender, in 61 countries classified in 3 regions: high income (n = 28), Eastern and Central Europe (n = 15), and other (n = 18). RESULTS: The incidence of KRT ranged from 89 to 378 per million population in high-income regions, 32 to 222 per million population in Central and Eastern Europe, and 22 to 493 per million population in the other region; age-adjusted CKD prevalence ranged from 5.5% to 10.4%, 7.6% to 13.7%, and 7.4% to 13.1%, respectively. The relation between these indicators was positive in high-income countries, negative in Central and Eastern Europe, and null in the other region. Age-adjusted CKD prevalence explained 40% of the variance in KRT incidence (P < 0.001) in high-income countries. The explained variance of age-adjusted mortality was close to 0 in high-income countries and positive at 19% (P = 0.10) in Central and Eastern Europe and at 11% (P = 0.17) in the other region. Results were consistent by gender. CONCLUSION: This study raises awareness on the significant part of the gaps in KRT incidence across countries not explained by the number of individuals with CKD, even in high-income countries where access to KRT is not limited.