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Event Rates and Risk Factors for Recurrent Cardiovascular Events and Mortality in a Contemporary Post Acute Coronary Syndrome Population Representing 239 234 Patients During 2005 to 2018 in the United States

Background Patients with acute coronary syndrome (ACS) are recognized by guidelines as remaining at high risk for adverse outcomes. Evidence from contemporary, representative ACS populations in a clinical practice setting is necessary to identify subgroups and strategies for improving patient outcomes. We aimed to describe event rates and risk factors in an ACS population over prolonged follow‐up for cardiovascular end points. Methods and Results We identified 239 234 patients in the Optum Research Database (57.2% men; mean [standard deviation] age, 69.2 [12.2] years) with evidence of an ACS hospitalization (index ACS) during January 1, 2005 through December 30, 2018. Subgroups were based on index ACS event (myocardial infarction/unstable angina and revascularization status) and the Thrombolysis In Myocardial Infarction Risk Score for Secondary Prevention. The 5‐year event rate for the primary end point representing nonfatal myocardial infarction, nonfatal ischemic stroke, and cardiovascular death was 33.4% (95% CI, 33.1%–33.7%; P<0.001). The risk of experiencing the primary end point was ≈6‐fold higher immediately after discharge (≈40.9% annualized risk) as compared with the period 1+ years after hospitalization (≈6.4% annualized risk). Among subgroups, the 5‐year primary end point event rate was highest for myocardial infarction without revascularization and a Thrombolysis In Myocardial Infarction Risk Score for Secondary Prevention ≥4, at 47.9% (95% CI, 47.3%–48.4%; P<0.001) and 56.7% (95% CI, 55.9%–57.4%; P<0.001), respectively. Conclusions Patients with ACS remain at very high risk of experiencing recurrent cardiovascular events, particularly early after discharge, with identifiable subgroups at multifold higher risk of specific clinical end points.


Definition and Estimation of Instantaneous Hazard Rate Over Time
Sensitivity Analysis of Selecting the First or Last Qualifying ACS as Index

Estimation of Instantaneous Hazard Rate Over Time
Let ( ) denote the survivor function defined as the probability of not experiencing an event of interest by time . Equivalently, the cumulative incidence of events is ( ) = 1 − ( ). Let ( ) denote the size of population that experiences an event of interest during a small timeinterval from to + . The instantaneous hazard rate, ( ), is defined as the instantaneous rate of events in the population that has survived to time . Thus: 11 The implied cumulative incidence of events, ( ), from this expression is: If the hazard rate over time is constant with ( ) = , the expression reduces to ( ) = 1 − exp(− ). If it is time dependent, functional forms representing its time dependence can be utilized. A useful functional form for this purpose is: Where , , and are parameters in the above expression for instantaneous hazard rate. This functional was found to adequately describe the patterns of cardiovascular event rates over time in 22 randomized controlled trials (RCTs) for lipid-lowering therapies. 22 It captures a pattern of an initial elevation of risk (e.g., close to the Index ACS event) that gradually declines over time to a more constant risk (e.g., > 2 years after Index ACS). It also has the flexibility to model a constant risk over time (i.e., = 0). With this functional form, ( ) can be expressed as: The hazard rates over time in Figure 2 of the manuscript represent the curve defined by ( ) = + exp(− ) obtained via the model-estimated values of , , and .

Sensitivity Analysis of Selecting the First or Last Qualifying ACS as Index
For patients with multiple qualifying ACS events, we randomly selected one as the Index ACS.
Patients were followed from that point onwards for CV events. This was done to minimize the potential of bias and to better align the findings with how the risk needed to be interpreted from the study findings.
Here we describe a sensitivity analysis estimating the impact on the primary endpoint of selecting either the first or the last qualifying ACS as the Index ACS. We stratified the study population (N = 239,234) into six mutually exclusive subgroups based on the number of qualifying ACS events and whether the first or last was chosen as the Index ACS. The following The subgroup with only one qualifying ACS was more likely to be less comorbid (as these patients had a smaller number of ACS events) than other subgroups and hence relatively lower risk. Also, these patients did not have another ACS event (i.e., nonfatal MI) during the follow-up and were only at risk for nonfatal ischemic stroke or CV death when considering the primary composite endpoint. Together, these factors resulted in a relatively lower 5-year event rate of 22.7% in this subgroup. Subgroups 2 and 4 represent patients with more than one qualifying ACS in which the first event was chosen as the Index ACS. In these subgroups, the patients had at least one additional ACS event during follow-up, which explains the relatively high 5-year event rate of 81.3% and 87.8%, respectively. Compared to subgroups 2 and 4, the opposite was true for subgroups 3 and 5. These subgroups, in which the last qualifying ACS event was chosen as the Index ACS, had relatively lower 5-year event rates of 36.8% and 50.5%, respectively. This finding is intuitive since patients in subgroups 3 and 5 did not have another ACS event (i.e., nonfatal MI) during the follow-up (by definition). The monotonic increase in risk in subgroups 1, 3, and 5 is reflective of the history of a progressively larger number of prior ACS events.
To determine the potential range of 5-year event rates depending on the ACS event selected, we performed two additional weighted Kaplan-Meier (KM) analyses where the subgroups described above were used to construct weights to estimate the outcome if the first or last qualifying ACS event during the identification period was selected as the Index ACS event. Results are summarized in the figure below.
• Subgroups 2 and 3 in the previous table are comparable as they were divided into these groups randomly. This means that a counterfactual situation where in both subgroups 2 and 3 the first qualifying ACS event was selected as the Index ACS can be estimated by reweighing the observations such that the weight of subgroup 3 is transferred to subgroup 2. In other words, by reweighing observations in subgroup 3 as 0, and subgroups 2 as 2 (= (6.4 + 6.4) / 6.4).
• Similarly, a counterfactual situation where in subgroups 4, 5, and 6 the first qualifying ACS event was selected as the Index ACS can be estimated by reweighing the observations such that the weight of subgroups 5 and 6 is transferred to subgroup 4. In other words, by reweighing observations in subgroups 5 and 6 as 0, and subgroup 4 as 3.4 (= (1.5 + 1.5 + 2.1) / 1.5).
Thus, the KM event rate for the primary composite endpoint over time for a scenario representing selection of the first qualifying ACS event as the Index ACS for all patients can be estimated by reweighing the observations in subgroups 1, 2, and 4 as 1, 2, and 3.4, respectively, and reweighting the observations in remaining subgroups as 0. The estimated KM event rate for the primary composite endpoint over time for the scenario representing selection of the last qualifying ACS event as the Index ACS for all patients can be generated in a similar manner.

Figure S1. Schematic of the Study and Patient Identification Period
Shown is a schematic of the study and patient identification period utilized to identify patients with an ACS hospitalization between January 1, 2005 and December 30, 2018 in the Optum Research Database. For individuals experiencing multiple qualifying ACS hospitalizations, one event was randomly selected (subsequently referred to as "Index ACS") which helped ensure representativeness. The index date was the date of hospital discharge from Index ACS and defined the initiation of follow-up. The twelve months immediately prior to the index date were defined as the baseline period and utilized to identify pre-existing cardiovascular and noncardiovascular conditions using medical claims.
ACS, acute coronary syndrome. Blue bars representing scenarios A and B (medication supply via filled prescription (Rx) on or within 30 days prior to the index date) define the patient as being treated as of the index date.
The red bar representing scenario C (medication supply via filled Rx more than 30 days prior to the index date) defines the patient as not being treated as of the index date.

Individual Endpoints
Shown is the cumulative incidence of the secondary endpoint (first occurrence of: nonfatal MI, nonfatal ischemic stroke, CV death, nonfatal UA requiring hospitalization, or nonfatal elective coronary revascularization) and individual endpoints (represented by components of the

Risk Score for Secondary Prevention
Shown is the cumulative incidence of the secondary endpoint (first occurrence of: nonfatal MI, nonfatal ischemic stroke, CV death, nonfatal UA hospitalization, or nonfatal elective coronary revascularization) and individual endpoints (represented by components of the composite) by TRS 2 o P categories. Below each graph is the number of patients in each TRS 2 o P category who are at risk of experiencing the corresponding endpoint.

Risk Score for Secondary Prevention and Use of Revascularization
Shown is the cumulative incidence of the secondary endpoint (first occurrence of: nonfatal MI, nonfatal ischemic stroke, CV death, nonfatal UA requiring hospitalization, or nonfatal elective coronary revascularization) and individual endpoints (represented by components of the composite) by TRS 2 o P categories and use of revascularization. Below each graph is the number of patients in each TRS 2 o P category with or without revascularization who are at risk of experiencing the corresponding endpoint.