Eight studies [10,11,12,13,14,15,16,17] were identified that matched the search criteria. All studies were published between 2008 and 2022 and had a retrospective cohort design with data based on hospital discharge documents (S3, S4, S5, S7; see Table 1 for study assignment) or national registries (S1, S2, S6, S8). The sample size of AMI ranged from 935 to 71,992. The timespan of data covered in the investigations ranged from 1 to 25 years. Six studies were conducted in Europe (S1, S2, S4, S5, S6, S8), one in the US (S3), and one in Iran [S7]. All studies compared the AMI incidence rate (IR) during the observed period, i.e., after the DST time shift, with the AMI incidence rate in the control periods. Two studies did not report the size of the total patient group, but instead provided the number of patients with AMI among cases and controls (S1, S2), from which we derived the total sample size. Five studies (S1, S2, S3, S4, S5) calculated the incidence rate ratio (IRR) of the whole post-transitional week for the spring and autumn time transition, respectively. The IRR is the ratio of the AMI incidence rate in the observed period following the transition to the average of the AMI incidence rates during the control weeks. As a methodological variation, study S6 calculated the risk ratio of AMI for the post-transitional week compared to control periods instead of the IRR, and S7 calculated the IRR, but only reported statistical levels of significance, and no numerical values. Even though S8 did not mention IRR directly, it did calculate the ratio of observed to expected events (O/E).

For the spring shift, six of the seven studies (S1, S2, S3, S4, S5, S6) with numerical IRR data reported an IRR or equivalent ratio calculation of > 1.0 after the transition into DST, thus indicating an increase in the AMI IR in the post-transitional period compared to control periods. Four of those studies reported statistically significant IR values: 1.051 (S1), 1.039 (S2), 1.17 (S3), and 1.15 (S4); S8 was the only study with an O/E of < 1.0.

For the autumn shift, only three of seven studies that included data on weekly incidence rates reported an increase (S4, S6, S8). The AMI IRR or odds ratio in the post-transitional week in S4 and S6 was 1.19 and 1.025, respectively, and S8 reported an O/E ratio of 1.06. The increase in the incidence rate of AMI was only statistically significant in two of the three studies (S4, S8), one of which compared the AMI IR in the post-transitional week to that of all non-transitional weeks (S4). In all other studies, the IRRs were unremarkable, suggesting AMI incidence rates in the post-transitional weeks similar to those of the control periods.

Six studies (S1, S3, S4, S6, S7, S8) reported IRR data for the individual weekdays of the post-transitional week. Amongst them, S5 had partial numerical information on the AMI IR, and S8, the most recent study, calculated the ratio of the number of observed to expected events (O/E) for every day of the week. For the spring shift, four studies reported the highest IRR on the first 3 days of the post-transitional week: Sunday (S3), Monday (S4, S6), and Tuesday (S1); S8 reported the highest O/E ratio on the fourth day, and two other studies reported the highest IRR on the fifth (S5) and last day (S7).

For the autumn shift, five studies reported the highest daily IRRs on the last three days of the post-transitional week: Thursday (S4), Friday (S1, S6, S7), and Saturday (S3); S5 reported the highest IRR on Wednesday. In S8 the highest O/E ratio was on the second day after the transition.

Control periods and observed periods differed between studies. All studies analyzed the AMI incidence rates of the 7 individual days, or of the first week after DST transitions (also called the post-transitional week), with the exception of S8, which observed the AMI incidence during the 2 weeks following the transition. Meanwhile, the control periods varied significantly: four studies (S1, S2, S3, S7) used the corresponding weekdays 2 weeks before and 2 weeks after the post-transitional week as a control period. One study (S4) used two different control periods: i) all (51) non-transitional weeks and ii) 2 weeks before and 2 weeks after. Kirchberger et al. (S6) examined the months around the transition: March and April for the spring transition and September to November for the autumn transition. In one study (S5), the 2 weeks before the post-transitional week and the 2 weeks after the post-transitional week served as a control period. The most recent study by Rodriguez-Cortez et al. (S8) used the 2 weeks before the transition as a control period.

Furthermore, two studies used advanced statistical modeling to infer the post-transitional AMI IRR values. Kirchberger et al. (S6) applied a time series model and an excess model, and controlled for confounding factors such as temperature and humidity. S8 employed natural visibility graphs to observe dynamic patterns exhibited by the variables.

With the exception of one study (S7), all studies reported statistically significant findings in different subgroups, which are discussed as observed in the spring and autumn shift. The following subsection focuses on age, sex, and cardiac medications, which were relevant modifiers in all studies. All other relevant modifying variables are listed in Table 2.

For the spring shift, Janszky et al. (S1) reported a more pronounced elevation in AMI in patients with low cholesterol and triglycerides and among those taking aspirin or calcium-channel blockers. In contrast, Kirchberger et al. (S6) found beta-blockers and calcium-channel blockers to be associated with lower risk in the spring transition, whereas patients with ACE inhibitors had an increased risk. In Jiddou et al. (S3), more significant use of calcium channel blockers was found on the Sunday after the transition in the study group compared with that in the control group. Results from Janszky et al. (S2) suggested that the effect of the spring transition on AMI rate was generally larger in women and in the population aged under 65 years. However, Culic (S4) reported that patients whose AMI occurred during the observation period were significantly more likely to be male. In addition, nonsignificant trends toward a lower likelihood of beta-blocker, aspirin, or calcium antagonist use were also present. While Jiddou et al. (S3) reported a significantly greater incidence of non-ST-elevation myocardial infarction (NSTEMI) after the transition to DST in the spring study group compared with the control group, proportions of ST-elevation myocardial infarction (STEMI) patients were similar between the study week and control weeks in Sipila et al. (S5) and Rodrigues et al. (S8).

For the autumn shift, Janszky et al. (S1) reported that patients with hyperlipidemia and those taking calcium channel blockers had a lower than expected risk following the transition. The incidence of AMI was also lower among patients taking calcium channel blockers in Culic (S4). While Janszky et al. (S2) suggest that the autumn effect was more pronounced in men than in women, the female sex was significantly predictive of AMI in Culic (S4). Culic (S4) reports several risk-lowering trends in patients with AMI during the transition week such as use of beta-blocker, aspirin, or calcium antagonist. In contrast to the spring transition, no significant difference was found in the autumn incidence of NSTEMI versus STEMI by Jiddou et al. (S3). S8, however, reported an increased risk of NSTEMI compared with STEMI in the autumn shift.