The growing burden of chronic kidney disease (CKD), with its associated morbidity and mortality, is recognized as a major public health problem globally and causing substantial load on health care systems. The current framework for the definition and staging of CKD, based on eGFR levels or presence of kidney damage, is useful for clinical classification of patients, but identifies a huge number of people as having CKD which is too many to target for intervention. The ability to identify a subset of patients, at high risk for adverse outcomes, would be useful to inform clinical management. The current staging system applies static definitions of kidney function that fail to capture the dynamic nature of the kidney disease over time. Now-a-days, it is possible to capture multiple measurements of different laboratory test results for an individual including eGFR values. A new possibility for identifying individuals at higher risk of adverse outcomes is being explored through assessment and consideration of the rate of change in kidney function over time, and this approach will be feasible in the current context of digitalization of health record keeping system. On the basis of the existing evidence, this paper summarizes important findings that support the concept of dynamic changes in kidney function over time, and discusses how the magnitude of these changes affect the future adverse outcomes of kidney disease, particularly the End Stage Renal Disease (ESRD), CVD and mortality.
Keywords: Adverse outcome; Chronic kidney disease; Renal function change.