Analysis of ECG characteristics of college students in physical examination
Objective To understand the performances of cardiac electrical activities in college students, and to analyze the detection and characteristics of ECG abnormalities among them, providing references for identifying those requiring further examinations and screening high risky population based on ECGs. Methods We collected the ECGs of 1003 college students who had been enrolled in 2022 and had undergone physical examination. Descriptive statistical analysis was performed on the population distribution characteristics of ECGs and ECG parameters by different sexes and different ethnic groups. Results There were 459 cases (45.8％) of normal ECGs while abnormal ones were detected in 544 cases (54.2％) . The detection of abnormal ECGs was as follows:sinus arrhythmia accounted for 36.7％, sinus tachycardia accounted for 2.2％, sinus bradycardia with arrhythmia accounted for 5.5％, premature contraction accounted for 1.8％, bundle branch block accounted for 1.7％, and ST-T changes accounted for 2.5％. The detection rate of abnormal ECGs was higher in females than that in males, and the difference was statistically significant (P<0.01) . Among the male and female college students, the differences of ECG related parameters including P-wave duration, QRS complex duration, QT interval, and RV5 and SV1 amplitudes were all statistically significant (all P<0.05). Conclusion The detection rate of abnormal ECGs in college students' physical examination was relatively high. Their ECG changes may be associated with autonomic nerve dysfunction and the boundary value settings of ECG parameters. Therefore, in diagnosing ECGs of physical examination, careful judgment of whether ECG changes are physiological or pathological is required in case of misdiagnosis, resulting in stress to examinees' family members and wasting of medical resources. ECG related parameters differ less between sexes in college students, and thus the difference could be ignored in actual clinical operation.
万方数据
维普
