title: Predictive value of ankle-brachial index to all-cause mortality and cardiovascular mortality in Chinese patients with chronic kidney disease.
abstract: BACKGROUND
  To investigate the predictive value of ankle-brachial index (ABI) for all-cause mortality and cardiovascular mortality in Chinese patients with chronic kidney disease (CKD).
  
  
  METHODS
  1563 CKD patients were enrolled in the cohort and were followed up for about 3 years in China. CKD was defined as an eGFR less than 60 ml/min/1.73m(2). 573 participants were diagnosed with PAD using ABI <= 0.90. Their average age was 73.4 ±8.2 years.
  
  
  RESULTS
  During a median follow-up of 38 months, there were 1353 CKD patients with complete data. A total of 313 patients (161 with and 152 without PAD) died during follow-up. 184 deaths (99 with and 85 without PAD) were caused by cardiovascular disease (CVD). All-cause and CVD mortality of CKD patients with PAD was increased 2.2-fold and 2.4-fold compared with CKD patients without PAD. The hazard ratio (HR) of PAD for all-cause and CVD mortality was 2.15 (95 % CI: 1.66 - 2.79) and 2.51 (95 % CI: 1.80 - 3.50) respectively. Mortality of CKD patients significantly increased with decreasing ABI. That of CKD patients with ABI <= 0.4 was the highest (42.9 % and 28.6 %, respectively) in different ABI categories. Relative risks of all-cause and CVD mortality of CKD patients with ABI <= 0.4 were increased 3.479-fold (95 % CI: 2.076 - 5.830) and 4.960-fold (95 % CI: 2.644 - 9.302) respectively compared with those of patients with ABI > 1.0 and <= 1.4. Special models to evaluate the predictive value of ABI to mortality of CKD patients suggested that addition of ABI significantly increased the predictive value of the model for 3-year mortality compared with a model including conventional risk factors alone.
  
  
  CONCLUSIONS
  Low ankle-brachial index can predict increased mortality of chronic kidney disease patients. Addition of ankle-brachial index can significantly improve the prediction of 3-year mortality compared with conventional risk factors alone.
authors.affiliation: Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Wang
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Guo
authors.forename: Jue
authors.initials: J
authors.lastname: Li
authors.forename: Dayi
authors.initials: D
authors.lastname: Hu
authors.forename: Dongdong
authors.initials: D
authors.lastname: Zhao
authors.forename: Heng
authors.initials: H
authors.lastname: Ma
authors.forename: Qingjie
authors.initials: Q
authors.lastname: Mou
authors.forename: Jing
authors.initials: J
authors.lastname: Liu
authors.forename: Yawei
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D055109:Ankle Brachial Index; D044466:Asian People / Q000706:statistics & numerical data*; D002318:Cardiovascular Diseases / Q000208:ethnology / Q000401:mortality* / Q000503:physiopathology; D002423:Cause of Death; D016009:Chi-Square Distribution; D002681:China / Q000453:epidemiology; D002908:Chronic Disease; D005260:Female; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate; D006801:Humans; D007674:Kidney Diseases / Q000208:ethnology / Q000401:mortality* / Q000503:physiopathology; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D058729:Peripheral Arterial Disease / Q000175:diagnosis* / Q000208:ethnology / Q000401:mortality* / Q000503:physiopathology; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors