ZHONG Yanhua, SANG Shengmin, DING Hongsheng. Value of serum uric acid combined with Gensini score in predicting prognosis of patients with acute ST segment elevation myocardial infarction[J]. Journal of Clinical Medicine in Practice, 2021, 25(23): 82-85. DOI: 10.7619/jcmp.20212716
Citation: ZHONG Yanhua, SANG Shengmin, DING Hongsheng. Value of serum uric acid combined with Gensini score in predicting prognosis of patients with acute ST segment elevation myocardial infarction[J]. Journal of Clinical Medicine in Practice, 2021, 25(23): 82-85. DOI: 10.7619/jcmp.20212716

Value of serum uric acid combined with Gensini score in predicting prognosis of patients with acute ST segment elevation myocardial infarction

  •   Objective  To investigate the value of serum uric acid combined with Gensini score in predicting prognosis of patients with acute ST segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI).
      Methods  A total of 408 STEMI patients treated with PCI were selected as study objects, among whom 64 cases with Major Adverse Cardiovascular Events (MACE) were included in observation group, and 344 cases without MACE were included in the control group. Coronary artery lesions, serum uric acid, Gensini score and other biochemical indexes were compared between the two groups.
      Results  The average age, the proportion of patients with history of diabetes, fasting blood glucose, serum uric acid and Gensini scores in the observation group were significantly higher than those in the control group (P < 0.05). Age, history of diabetes, serum uric acid and Gensini score were independent influencing factors of MACE after PCI (P < 0.05). The sensitivity and specificity of serum uric acid and Gensini score in combination in predicting MACE were significantly higher than those of serum uric acid and Gensini score(P < 0.05).
      Conclusion  Serum uric acid and Gensini score are closely related to the prognosis of STEMI patients after PCI. The combination of the two indexes can effectively predict the risk of MACE.
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