1.Clinical Analysis of 63 Cases of Myocardial Bridge
Gangjun ZONG ; Xiao WANG ; Gangyong WU ; Yang XIA ; Li ZHANG ; Manqing CHEN ; Jingkai CHEN
Journal of Medical Research 2006;0(06):-
Objective To evaluate the significance of myocardial bridge and find a reasonable diagnosis and treatment strategy.Methods Sixty-three myocardial bridge patients and sixty-three patients with negative results of coronary artery angiography were reviewed.The clinical data of symptoms,electrocardiogram,exercise tests,coronary artery angiography,therapeutics and the serum levels of C-reactive protein(CRP)were analyzed.Results The symptoms of chest distress and chest pain were found in myocardial bridge patients.Myocardial consumption of oxygen augmentation causes the symptoms of aggravation.Positive results of electrocardiogram and exercise tests in many of myocardial bridge patients were examined.There were no relationship with severity of myocardial bridge artery stenosis.Most of myocardial bridge were discovered in anterior descending branch.At present,the main treatment of myocardial bridge was drug therapeutics.After treatment,the serum levels of CRP was significantly decreased.Conclusion Myocardial bridge was anatomy abnormality with important clinical significance.The serum levels of CRP can be used to evalue the therapeutic efficacy of myocardial bridge.
2.Correlation between serum growth differentiation factor 11 level and severity of coronary artery disease in patients with acute myocardial infarction
Baida XU ; Ke CHEN ; Yehong LIU ; Wentao SU ; Ting YE ; Gangyong WU ; Gangjun ZONG
Chinese Journal of Cardiology 2024;52(3):286-292
Objective:To investigate the correlation between serum growth differentiation factor 11 (GDF11) level and coronary artery lesions in patients with ST-segment elevation myocardial infarction (STEMI), and the predictive efficacy of nomogram risk prediction model based on GDF11 combined with traditional risk factors on the occurrence of STEMI.Methods:This study was a retrospective cross-sectional study. Patients hospitalized in the Department of Cardiology of the 904th Hospital of Joint Logistic Support Force of People′s Liberation Army of China from 2016 to 2018 were selected and divided into control group and STEMI group. The demographic data, blood lipid level, laboratory indicators of blood and GDF11 level were collected. Logistic regression analysis screened out independent correlated factors for the occurrence of STEMI. Spearman correlation analysis clarified the correlation of each indicator with the SYNTAX or Gensini scores. A nomogram risk prediction model for the risk of STEMI occurrence and the receiver operating characteristic curve was used to compare the prediction efficiency of each model.Results:A total of 367 patients were enrolled, divided into control group ( n=172) and STEMI group ( n=195), age (66.5±11.8), male 222 (60.49%). The serum GDF11 level of STEMI group was significantly lower than that of the control group (36.20 (16.60, 70.75) μg/L vs. 85.00 (53.93, 117.10) μg/L, P<0.001). The results of multivariate logistic regression analysis showed serum GDF11( OR=0.98, 95% CI: 0.97-0.99) and traditional independent risk factors such as smoking, diabetes, C-reactive protein, homocysteine, lipoprotein (a) and apolipoprotein A1/B were independent correlate factors for the occurrence of STEMI ( P<0.05). Spearman correlation analysis showed that serum GDF11 was negatively correlated with SYNTAX score and Gensini score ( P<0.05). The nomogram model constructed by serum GDF11 combined with traditional independent risk factors (AUC=0.85, 95% CI: 0.81-0.89) had better predictive value for the occurrence of STEMI than the traditional nomogram model constructed by independent risk factors(AUC=0.80, 95% CI:0.75-0.84) or serum GDF11 (AUC=0.76, 95% CI: 0.72-0.81), all P<0.01. Conclusions:Serum GDF11 is an independent correlate factor in the occurrence of STEMI and is negatively correlated with the severity of coronary artery lesions in patients with STEMI. The nomogram model constructed based on GDF11 combined with traditional risk factors can be a good predictor for the occurrence of STEMI.