Collister David, Ferguson Thomas, Komenda Paul, Tangri Navdeep
Department of Medicine, University of Manitoba, Winnipeg, Canada; Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada; Chronic Disease Innovation Center, Seven Oaks General Hospital, Winnipeg, Canada.
Department of Medicine, University of Manitoba, Winnipeg, Canada; Department of Community Health Sciences, University of Manitoba, Winnipeg, Canada; Chronic Disease Innovation Center, Seven Oaks General Hospital, Winnipeg, Canada.
Semin Nephrol. 2016 Jul;36(4):273-82. doi: 10.1016/j.semnephrol.2016.05.004.
Chronic kidney disease (CKD) is a global public health problem that is associated with excess morbidity, mortality, and health resource utilization. The progression of CKD is defined by a decrease in glomerular filtration rate and leads to a variety of metabolic abnormalities including acidosis, hypertension, anemia, and mineral bone disorder. Lower glomerular filtration rate also bears a strong relationship with an increased risk of cardiovascular events, end-stage renal disease, and death. Patterns of CKD progression include linear and nonlinear trajectories, but kidney function can remain stable for years in some individuals. Addressing modifiable risk factors for the progression of CKD is needed to attenuate its associated morbidity and mortality. Developing effective risk prediction models for CKD progression is critical to identify patients who are more likely to benefit from interventions and more intensive monitoring. Accurate risk-prediction algorithms permit systems to best align health care resources with risk to maximize their effects and efficiency while guiding overall decision making.
慢性肾脏病（CKD）是一个全球性的公共卫生问题，与发病率、死亡率过高以及卫生资源利用相关。CKD的进展以肾小球滤过率降低为特征，并导致包括酸中毒、高血压、贫血和矿物质骨病在内的多种代谢异常。较低的肾小球滤过率还与心血管事件、终末期肾病和死亡风险增加密切相关。CKD进展模式包括线性和非线性轨迹，但在某些个体中，肾功能可多年保持稳定。需要解决CKD进展的可改变风险因素，以减轻其相关的发病率和死亡率。开发有效的CKD进展风险预测模型对于识别更可能从干预措施和更强化监测中获益的患者至关重要。准确的风险预测算法使医疗系统能够根据风险最佳地配置卫生保健资源，以在指导总体决策的同时最大限度地提高其效果和效率。
