Zhikai Yu1, Jiachuan Xiong1, Ke Yang1, Yinhui Huang1, Ting He1, Yanlin Yu1, Jinghong Zhao2. 1. Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Kidney Center of PLA, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, People's Republic of China. 2. Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Kidney Center of PLA, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, People's Republic of China. zhaojh@tmmu.edu.cn.
Abstract
PURPOSE: Previous studies have indicated that platelet indices are related to the pathogenesis of cardiovascular diseases (CVD). However, it is unclear which platelet-related indicators are associated with CVD events in patients with chronic kidney disease (CKD) without dialysis. METHODS: We performed a single-center prospective cohort study involved 1391 CKD patients to explore the relationship between platelet indices and CVD events in CKD patients. A nomogram was generated to predict CVD-free survival after 3 and 5 years of follow-up in terms of the fitted Cox regression model. And the time-dependent receiver-operating characteristic (ROC) curves were applied to evaluate the prediction accuracy of platelet indices on CVD events. RESULTS: During a median follow-up of 3.41 years, 211 (15.2%) patients experienced CVD events. Results showed that platelet counts (PLT), plateletcrit (PCT), platelet-large cell ratio (P-LCR), and platelet distribution width (PDW) among 5 platelet indices were significantly lower in advanced CKD stages. Cox regression model showed that PLT, PDW, and PCT were associated with CVD events. However, after multivariable-adjusted, low level of PLT, hazard ratio (HR) 0.994 and 95% confidence interval (95% CI 0.989-1.000, p = 0.04), and PDW, HR 0.936 (95% CI 0.878-0.998, p = 0.044) predicted CVD events. The area under the ROC curve (AUC) of platelet indices assessed by time-dependent ROC curve analysis showed that only PLT and PDW were significant for predicting CVD events for 5 years. CONCLUSIONS: We demonstrated that PLT and PDW among 5 platelet indices were independently associated with CVD events in patients with CKD.
PURPOSE: Previous studies have indicated that platelet indices are related to the pathogenesis of cardiovascular diseases (CVD). However, it is unclear which platelet-related indicators are associated with CVD events in patients with chronic kidney disease (CKD) without dialysis. METHODS: We performed a single-center prospective cohort study involved 1391 CKDpatients to explore the relationship between platelet indices and CVD events in CKDpatients. A nomogram was generated to predict CVD-free survival after 3 and 5 years of follow-up in terms of the fitted Cox regression model. And the time-dependent receiver-operating characteristic (ROC) curves were applied to evaluate the prediction accuracy of platelet indices on CVD events. RESULTS: During a median follow-up of 3.41 years, 211 (15.2%) patients experienced CVD events. Results showed that platelet counts (PLT), plateletcrit (PCT), platelet-large cell ratio (P-LCR), and platelet distribution width (PDW) among 5 platelet indices were significantly lower in advanced CKD stages. Cox regression model showed that PLT, PDW, and PCT were associated with CVD events. However, after multivariable-adjusted, low level of PLT, hazard ratio (HR) 0.994 and 95% confidence interval (95% CI 0.989-1.000, p = 0.04), and PDW, HR 0.936 (95% CI 0.878-0.998, p = 0.044) predicted CVD events. The area under the ROC curve (AUC) of platelet indices assessed by time-dependent ROC curve analysis showed that only PLT and PDW were significant for predicting CVD events for 5 years. CONCLUSIONS: We demonstrated that PLT and PDW among 5 platelet indices were independently associated with CVD events in patients with CKD.
Entities:
Keywords:
Cardiovascular events; Chronic kidney disease; Outcome; Platelet indices
Authors: S G Chu; R C Becker; P B Berger; D L Bhatt; J W Eikelboom; B Konkle; E R Mohler; M P Reilly; J S Berger Journal: J Thromb Haemost Date: 2009-08-19 Impact factor: 5.824