The estimates from meta-analysis for overall number of patients and the number of deaths within 30 days reported an effect size of 0.82 (CI 0.70, 0.96) for symptom onset-balloon time and 0.62 (CI 0.53, 0.74) for door-balloon time. There is an evidence suggesting that mortality occurred less frequently in the short time group than in the long time group (ratio < 1) according to the results of forest plot, although there was no significant difference between transit time (both symptom-onset-to-balloon time and door-balloon time) and short-term mortality. Moreover, there is a comparatively large difference in the transit time of each trial from included studies. However, Brodie 2003 reported extremely similar median time, 234 min and 240 min of short and long time group. One possible explanation may be that a large difference of sample size lead to abnormality of the outcome. There are 1705 patients from short time group, while only 138 patients in long time group. Meanwhile, patients in two groups have different clinical characteristics. Population in short time group were non-shock patients, whereas AMI patients in long time group suffered shock. It is possible that shock patients may experience longer emergency care time on the spot . The bias introduced by follow-up was not considered as an assessment of mortality was conducted within a short-term (30-day).
Both of the estimates of I2 assessed in the meta-analysis would be found to be moderate. I2 = 31% is for symptom onset-balloon time; I2 = 67% is for door-balloon time. The value of I2 was calculated from both the scope of the study and the between study variance . The study including different clinical characteristics, research methods and statistical methods could contribute to the value of I2. Thus, the summary estimation cross the average population have various features, this should be carefully used and discussed.
Deficiencies often existed in studies. The studies involved in this review include different demographic characteristics. Participants in two studies [15, 16] were from Asia, one study was selected from the Middle East . Four studies [17, 19, 20, 21] were conducted in the United States and three studies [18, 26, 27] were from Europe. It is possible that different races have different prognosis for the same disease, which can introduce heterogeneity. Additionally, screening trials include retrospective and prospective observational studies, recall bias is less likely to be avoided in retrospective studies and survivor bias could affect clinical outcomes . It is difficult that to assess the time of onset of symptom sometimes so that it would result in errors in door-balloon time . Different trials that were grouped with different standards and the results could be influenced by variables. For example, in Brodie 2003, patients with shock experienced longer time- to-reperfusion and mortality are also different from patients without shock. A few studies [15, 18] showed the opposite result among mortality with other studies, which was caused possibly by insufficient sample size.
In addition to characteristics mentioned above, a number of others vary in different studies, which can lead to heterogeneity. These situations include differences in inclusion and exclusion criteria, the instruments used and the medical levels of paramedical personnel .
Not all studies had reported median or mean of symptom onset-balloon time and door-balloon time and corresponding mortality directly. Some studies had only recorded the approximate range of the transfer time and the mortality in the time range. For comparison in meta-analysis, we pooled the data of periods of time in these studies according to the median or mean transit time that was mentioned in the context. The time for patients to transfer to hospital that was less than the median/mean time was regarded as “short time group”, otherwise, the group was judged as “long time group”. This kind of grouping may have an impact on the analysis result. Besides, participation in these studies from observational study was not randomised. As a result, clinical characteristics of patients would introduce confounders into differences of mortality of different periods of time so that confounder bias caused by underlying disease is likely to be ineluctable. We limited the search language to English, which would contribute to linguistic bias. Meanwhile, limit number of studies may not be representative.
4.3 Comparison with previous studies
This review showed patients with STEMI are more likely to survival when experiencing shorter transit time, in spite of other variables. Needleman et al 2011 reported that the availability of skilled staff, staffing levels and the number of paramedics are potential factors to contribute to mortality . Kulkarni et al 2013 showed a similar outcome. 30-day mortality rate of patients with acute myocardial infarction in the low density of cardiovascular disease experts was higher than those in high-density areas, which suggested that the outcome of patients with AMI might be influenced by the availability of cardiology specialists in regional care systems . Other factors, including patients’ own condition (sleep deprivation and fatigue , genetic factor) and distance from home to hospital , can result in increased risk of death for AMI.
As for the factors affecting the transit time, availability of cardiologists and cardiac catheterization laboratory staff are considered to be associated with that . The increase in the time interval between getting the electrocardiogram (EGG) and arriving at the catheterization laboratory would lead to the increase in almost all door-balloon time . Delay to make decision to seek care and delay to receive care may be the reason for the increase in the overall transit time [2, 4].