Systematic evaluation and control measures of factors influencing prehospital delay in patients with acute myocardial infarction

Objective To explore the influencing factors of prehospital delay in patients with acute myocardial infarction, and to identify strategies to reduce pre-hospital delay. Methods The relevant literature was searched by the top ten databases such as Pubmed and Embase, and the 19 articles included in the NOS scale were used only for epidemiological investigation and statistical analysis by RevMan 5.3 software. Results The meta-analysis was used to classify the influencing factors into three categories. ①Social factors:age [OR=1.090, 95% CI (1.060-1.130), P＜0.001], gender [OR=1.180, 95% CI (1.050-1.330), P=0.006], cultural level [OR=1.410，95%CI(1.190-1.670)，P＜0.001], residential area [OR=1.350, 95% CI (0.990-1.840), P=0.060]; ②Clinical factors: history of diabetes [OR=1.380, 95% CI (1.220-1.560), P＜0.001], history of angina [OR=1.370, 95 %CI=1.050-1.770, P=0.020], history of myocardial infarction [OR=1.080, 95% CI (0.640-1.830), P=0.760], PCI history [OR=0.760, 95% CI (0.670-0.860), P＜0.001]; ③Other factors: nocturnal onset [OR=1.630, 95% CI (1.340-1.970), P＜0.001], treatment mode [OR=0.640, 95% CI (0.570-0.710), P＜0.001] The symptoms were not attributed to the heart [OR=3.100, 95% CI (1.620-5.94), P＜0.001 ]. Conclusion Age, female, history of diabetes, history of angina pectoralis, nocturnal morbidity, failure to attribute symptoms to the heart are risk factors. High academic background, PCI history, EMS visit are protective factors. The residential area, history of myocardial infarction and the previous delay of acute myocardial infarction patients are irrelevant. Effective and effective interventions can reduce pre-hospital delays and decrease out-of-hospital mortality.