Study Design and Population
The CCC-ACS project is an ongoing nationwide registry jointly initiated by the American Heart Association and the Chinese Society of Cardiology from 2014, aiming to improve the quality of care for ACS patients in China. Detailed information on the study design and methodology has been published previously.11 The CCC-ACS project was approved by the institutional review board of Beijing Anzhen Hospital, Capital Medical University, with a waiver for informed consent. This study is registered at the following URL: https://clinicaltrial.gov (unique identifier: NCT02306616).
In the present analysis, we included STEMI patients who underwent PCI during hospitalization and had no GDMT contraindications. GDMT was defined as the combination of the following three medications within 24 hours of STEMI onset: ACEI/ARB, β-blocker and statin. Non-GDMT was defined as at least one GDMT component was not used. Contraindications to statin include: active liver disease; persistent transaminase elevation of unknown cause; hypersensitivity to statin, myositis, myalgia, and rhabdomyolysis. Contraindications to β-blocker include: cardiogenic shock or unstable decompensated heart failure; sick-sinus syndrome (providing no permanent pacemaker), atrioventricular block of second and third degree; symptomatic bradycardia; hypotension and asthma. Contraindications to ACEI/ARB include: anuria renal failure with hyperkalemia, bilateral renal artery stenosis, isolated kidney with renal artery stenosis, pregnant and lactating women.
Study Covariates
The following variables were treated as covariates for multivariable adjustment and propensity score matching: demographics (age, gender), previous history (hypertension, diabetes, dyslipidemia, smoking, MI, PCI, coronary artery bypass grafting, heart failure, atrial fibrillation, renal failure, ischemic stroke, hemorrhagic stroke, chronic obstructive pulmonary disease, peripheral vascular disease), on-admission clinical characteristics [peak levels of creatine kinase MB (CKMB) isoform, Killip class, serum levels of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and triglycerides (TG), levels of systolic and diastolic blood pressure (SBP and DBP), heart rate, estimated glomerular filtration rate (eGFR) and baseline hemoglobin), pre-hospital medications (pre-hospital thrombolysis, aspirin, P2Y12 inhibitors, statins, β-blockers, ACEIs/ARBs, aldosterone antagonists and oral anticoagulants)], in-hospital medications [dual antiplatelet therapy (DAPT) status, aldosterone antagonists, oral anticoagulants, glycoprotein IIb/IIIa inhibitors and perioperative anticoagulants (unfractionated heparin, low molecular weight heparin (LMWH) and others)] and PCI-related characteristics [time from symptom onset to admission, PCI types (primary PCI <12 hours after symptom onset, primary PCI ≥12 hours after symptom onset, rescue PCI and elective PCI) and radial route for PCI or not. eGFR was calculated according the equation by Chronic Kidney Disease Epidemiology Collaboration.12 The status of dual antiplatelet therapy (DAPT) within the first 24 hours was defined as the following four categories: non-DAPT (single antiplatelet therapy), non-loading DAPT (DAPT was not in loading dose), single-loading DAPT (one of DAPT in loading dose), and both-loading DAPT (DAPT both in loading dose). The loading dose of aspirin was defined as ≥150 mg. The loading dose of P2Y12 receptor inhibitor was defined as ≥300 mg for clopidogrel and ≥180 mg for ticagrelor. The definition of the above study variables is listed in Supplemental Table 1.
Study Outcomes
The CCC-ACS project routinely collected information concerning bleeding data as a part of in-hospital outcomes, which included: fatal bleeding, hemorrhagic stroke, bleeding in vital organs/locations (intracranial, spinal canal, retroperitoneal, pericardial, and intra-ocular with compromised vision), bleeding requiring clinical intervention (requiring pressors, surgery or intravenous vasoactive agents), hemoglobin drop related to bleed (the admission level minus the nadir level), and bleeding requiring blood transfusion and total amount of transfusion. Based on these information, we defined a composite of major bleeds using the following three major bleeding definitions: (1) Bleeding Academic Research Consortium (BARC) type 3b-3c and type 5, which is defined as a hemoglobin drop of ≥5 g/dL or cardiac tamponade or bleeding requiring surgical intervention or bleeding requiring intravenous vasoactive agents (type 3b), intracranial hemorrhage (type 3c), or fatal bleeding (type 5), respectively;13 (2) Thrombolysis In Myocardial Infarction (TIMI) major bleeding, which is defined as intracranial hemorrhage or clinically overt bleeding associated with a hemoglobin drop of ≥5 g/dL, or fatal bleeding;14 (3) PLATelet inhibition and patient Outcomes (PLATO) life threatening bleeding, which is defined as fatal bleeding, intracranial bleeding, intraoperative bleeding with cardiac tamponade, severe hypotension, hypovolemic shock due to bleeding and requiring either vasopressor or surgery, a hemoglobin drop of ≥5 g/dL, or the need for transfusion >4 U of whole blood or packed red blood cells.15 Coronary artery bypass grafting related bleeding was excluded.
We also examined the association between GDMT status and ischemic events and all cause in-hospital mortality. Ischemic events were defined as the occurrence of re-infarction, in-stent thrombosis (angiographically confirmed) and ischemic stroke.
Statistical Analysis
Continuous data with normal distribution are presented as means and standard deviations. Nonparametric continuous data are presented as medians with interquartile ranges and categorical data are presented as number and percentage. We used propensity score-matching to balance the differences in patient demographics, medical history and pre-admission and in-hospital management strategies between GDMT and non-GDMT patients. We developed a non-parsimonious multivariable logistic regression model to estimate a propensity score for GDMT status (yes/no) as the dependent variable. Between-group imbalances were considered to be ideal if the absolute standardized difference (ASD) for a given covariate was less than 10%16 (Stata command “stddiff”). Then, a propensity score matching of a maximal ratio of 1-to-2, without replacement, with a caliper width of 0.02 was performed (Stata command “calipmatch”). The risk of in-hospital bleeding, ischemic events, and mortality in the matched groups was assessed using a logistic regression model on the matched pairs.
For variables with missing values, we imputed the missing data using the sequential regression multiple imputation method by IVEware (version 0.2; Survey Research Center, University of Michigan, Ann Arbor, MI) as previously described.17 It should be noted that, for BMI, the imputation was not performed due to a high missing rate (>25%). Therefore, BMI-related subgroup analysis and sensitivity analysis are not based on the full data set.
We performed the following interaction tests and subgroup analyses based on matched population, including age (<65 years and ≥65 years), sex, BMI (<28 kg/m2 and ≥28 kg/m2), eGFR (<60 mL/min/1.73m2 and ≥60 mL/min/1.73m2), DAPT status (full loading or not) and Killip class (>Class I vs. Class I).
Finally, we performed the following sensitivity analyses based on the matching cohort of 1-to-2: (1) excluding patients receiving DAPT with both in loading dose; (2) excluding patients died within 48 hours of admission; (3) excluding patients with Killip Class IV; (4) excluding patients receiving unfractionated heparin; (5) excluding patients who had previous history of hemorrhagic stroke. Additionally, a propensity score-matched with a maximal matching ratio of 1-to-3, and inverse probability weighting based on multivariate logistic regression (Stata command “teffects ipw”) were used as sensitivity analysis to validate the primary findings. We used Stata version 15.1 (StataCorp, College Station, TX) for analysis. A two-tailed P <0.05 was considered statistically significant.