Literature DB >> 24848783

Mean platelet volume on admission improves risk prediction in patients with acute coronary syndromes.

Xiaowei Niu1, Cuiling Yang2, Yiming Zhang1, Hengliang Zhang1, Yali Yao3.   

Abstract

Our aim was to evaluate the incremental predictive value of adding mean platelet volume (MPV) to the Global Registry of Acute Coronary Events (GRACE) risk score. The MPV and GRACE score were determined on admission in 509 consecutive patients with acute coronary syndrome (ACS). Six-month mortality or nonfatal myocardial infarction (MI) was the study end point. Overall, 61 (12%) patients reached the combined end point. Cox multivariate analysis showed that an elevated MPV was an independent predictor of 6-month mortality or MI in patients with ACS. The addition of MPV to the GRACE model improved its global fit and discriminatory capacity. The new model including MPV allowed adequate reclassification of 16% of the patients. In conclusion, the inclusion of MPV into the GRACE risk score could allow improved risk classification, thereby refining risk stratification of patients with ACS.
© The Author(s) 2014.

Entities:  

Keywords:  GRACE score; acute coronary syndrome; mean platelet volume; risk prediction

Mesh:

Year:  2014        PMID: 24848783     DOI: 10.1177/0003319714536024

Source DB:  PubMed          Journal:  Angiology        ISSN: 0003-3197            Impact factor:   3.619


  7 in total

1.  [Regression analysis of red cell distribution width and mean platelet volume in patients with acute myocardial infarction].

Authors:  Qi Liang; Xin-Jun Lei; Hong-Bing Li; Yang-Rong Yin; Jie Ren; Li-Hong Fan; Xin Huang; Zu-Yi Yuan
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2017-08-20

2.  Risk stratification based on components of the complete blood count in patients with acute coronary syndrome: A classification and regression tree analysis.

Authors:  Xiaowei Niu; Guoyong Liu; Lichao Huo; Jingjing Zhang; Ming Bai; Yu Peng; Zheng Zhang
Journal:  Sci Rep       Date:  2018-02-12       Impact factor: 4.379

3.  Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.

Authors:  Konrad Pieszko; Jarosław Hiczkiewicz; Paweł Budzianowski; Janusz Rzeźniczak; Jan Budzianowski; Jerzy Błaszczyński; Roman Słowiński; Paweł Burchardt
Journal:  J Transl Med       Date:  2018-12-03       Impact factor: 5.531

4.  Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Authors:  Konrad Pieszko; Jarosław Hiczkiewicz; Paweł Budzianowski; Jan Budzianowski; Janusz Rzeźniczak; Karolina Pieszko; Paweł Burchardt
Journal:  Dis Markers       Date:  2019-01-30       Impact factor: 3.434

5.  Mean platelet volume and coronary plaque vulnerability: an optical coherence tomography study in patients with non-ST-elevation acute coronary syndrome.

Authors:  Jun Wang; Xing Li; Jun Pu; Siyu Jin; Lu Jia; Xiaomei Li; Fen Liu; Yining Yang
Journal:  BMC Cardiovasc Disord       Date:  2019-05-29       Impact factor: 2.298

Review 6.  The Role of Hematological Indices in Patients with Acute Coronary Syndrome.

Authors:  Jan Budzianowski; Konrad Pieszko; Paweł Burchardt; Janusz Rzeźniczak; Jarosław Hiczkiewicz
Journal:  Dis Markers       Date:  2017-10-03       Impact factor: 3.434

7.  Relationship between White Blood Count to Mean Platelet Volume Ratio and Clinical Outcomes and Severity of Coronary Artery Disease in Patients Undergoing Primary Percutaneous Coronary Intervention.

Authors:  Altekin Refik Emre; Kilinc Ali Yasar; Yanikoglu Atakan; Cicekcibasi Orhan; Kucuk Murathan
Journal:  Cardiovasc Ther       Date:  2020-08-13       Impact factor: 3.023

  7 in total

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