What every clinician needs to know about chronic kidney disease: Detection, classification and epidemiology.
Nicholas M SelbyMaarten W TaalPublished in: Diabetes, obesity & metabolism (2024)
Chronic kidney disease (CKD) is a major healthcare challenge, affecting >800 million people worldwide. Implications for population health result from the strong associations of CKD with increased rates of cardiovascular disease, heart failure, progressive CKD leading to kidney failure, acute kidney injury (AKI), and mortality. In addition to a single disease perspective, CKD commonly coexists alongside other long-term conditions, in particular type 2 diabetes and cardiovascular disease. CKD is therefore an important component of multimorbidity that influences individual management and impacts prognosis. CKD is defined by abnormalities of kidney structure or function of any cause with implications for health that are present for longer than 3 months. The diagnosis is usually made on the basis of an abnormal glomerular filtration rate (GFR < 60 mL/min/1.73 m 2 ) and/or the presence of proteinuria (urine albumin to creatinine ratio > 30 mg/g or >3 mg/mmol). GFR is usually estimated from serum creatinine concentration using a variety of validated equations. However, serum creatinine is closely related to muscle mass and may therefore not be an accurate marker of GFR in people with high or low muscle mass (sarcopaenia). Cystatin C is an alternative endogenous marker of GFR that is increasingly being used but also has limitations. An estimate of GFR based on both creatinine and cystatin C is the most accurate. Diagnosis should be followed by classification and risk stratification to guide the development of a risk-based, personalized care plan. Improved detection and widespread implementation of optimal CKD management has the potential to bring major benefits to population health.
Keyphrases
- chronic kidney disease
- end stage renal disease
- healthcare
- cardiovascular disease
- type diabetes
- acute kidney injury
- heart failure
- machine learning
- public health
- multiple sclerosis
- high resolution
- primary care
- cardiovascular events
- left ventricular
- metabolic syndrome
- coronary artery disease
- insulin resistance
- social media
- atrial fibrillation
- skeletal muscle
- peritoneal dialysis