Details

Autor(en) / Beteiligte

• Xu, Yu
• Li, Mian
• Qin, Guijun
• Lu, Jieli
• Yan, Li
• Xu, Min
• Wang, Tiange
• Zhao, Zhiyun
• Dai, Meng
• Zhang, Di
• Wan, Qin
• Huo, Yanan
• Chen, Lulu
• Shi, Lixin
• Hu, Ruying
• Tang, Xulei
• Su, Qing
• Yu, Xuefeng
• Qin, Yingfen
• Chen, Gang
• Gao, Zhengnan
• Wang, Guixia
• Shen, Feixia
• Luo, Zuojie
• Chen, Li
• Chen, Yuhong
• Zhang, Yinfei
• Liu, Chao
• Wang, Youmin
• Wu, Shengli
• Yang, Tao
• Li, Qiang
• Bi, Yufang
• Zhao, Jiajun
• Mu, Yiming
• Wang, Weiqing
• Ning, Guang

Titel

Cardiovascular Risk Based on ASCVD and KDIGO Categories in Chinese Adults: A Nationwide, Population-Based, Prospective Cohort Study

Ist Teil von

• Journal of the American Society of Nephrology, 2021-04, Vol.32 (4), p.927-937

Ort / Verlag

United States: American Society of Nephrology

Erscheinungsjahr

2021

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• The Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline used eGFR and urinary albumin-creatinine ratio (ACR) to categorize risks for CKD prognosis. The utility of KDIGO's stratification of major CVD risks and predictive ability beyond traditional CVD risk prediction scores are unknown. To evaluate CVD risks on the basis of ACR and eGFR (individually, together, and in combination using the KDIGO risk categories) and with the atherosclerotic cardiovascular disease (ASCVD) score, we studied 115,366 participants in the China Cardiometabolic Disease and Cancer Cohort study. Participants (aged ≥40 years and without a history of cardiovascular disease) were examined prospectively for major CVD events, including nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death. During 415,111 person-years of follow-up, 2866 major CVD events occurred. Incidence rates and multivariable-adjusted hazard ratios of CVD events increased significantly across the KDIGO risk categories in ASCVD risk strata (all values for log-rank test and most values for trend in Cox regression analysis <0.01). Increases in statistic for CVD risk prediction were 0.01 (0.01 to 0.02) in the overall study population and 0.03 (0.01 to 0.04) in participants with diabetes, after adding eGFR and log(ACR) to a model including the ASCVD risk score. In addition, adding eGFR and log(ACR) to a model with the ASCVD score resulted in significantly improved reclassification of CVD risks (net reclassification improvements, 4.78%; 95% confidence interval, 3.03% to 6.41%). Urinary ACR and eGFR (individually, together, and in combination using KDIGO risk categories) may be important nontraditional risk factors in stratifying and predicting major CVD events in the Chinese population.