Statistical methods for building better biomarkers of chronic kidney disease

Abstract

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research.This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported.We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Contribution of Predictive and Prognostic Biomarkers to Clinical Research on Chronic Kidney Disease

“…It improves the risk prediction of CV events, particularly heart failure regardless of the level of kidney function [55,57,114,115], as well as the risk prediction of microvascular events (nephropathy or retinopathy) in diabetic patients and the risk prediction of CKD progression.…”

“…It improves the risk prediction of CV events, particularly heart failure regardless of the level of kidney function [55,57,114,115], as well as microvascular events (nephropathy or retinopathy) in diabetic patients and CKD progression. In the SONAR trial, it was used as a predictive biomarker in order to exclude patients with sodium retention after treatment with atrasentan [94] sST2…”

“…Owing to the importance of improving risk stratification beyond traditional kidney measures and to help clinical decision-making in CKD patients, the evaluation of novel biomarkers acquired great emphasis in clinical research, as witnessed by the growing number of publications on this topic [114]. However, before a biomarker can find full application in clinical practice, several steps need to be satisfied and reported.…”

“…Clinical validity is the next important step and consists of demonstrating that biomarker measurement is associated with a clinical characteristic of interest [115]. The first steps of clinical validation are the proof of concept and prospective validation [114]. Proof of concept means to assess whether biomarker levels differ between subjects who develop the event of interest vs. non-events.…”

Constructing a Model with Binary Response to Some of the Factors Affecting the Incidence of Chronic Kidney Failure

“…The logistic regression analysis is used in epidemiological and medical studies and, through explanatory variables, is determined whether descriptive or quantitative that affect the probability of occurrence of the dependent variable [4]. This research's problem lies in the fact that many people suffer from Chronic kidney diseases and in the late time the number of patients has increased significantly, which drew the researcher's attention in finding some factors that significantly affect the occurrence of this disease [5].…”