Efficacy and safety of PCSK-9 inhibitors in patients with acute coronary syndrome: a systematic review and network meta-analysis

Abstract

Background

Acute coronary syndrome (ACS) remains a leading cause of global cardiovascular morbidity and mortality, with elevated low-density lipoprotein cholesterol (LDL-C) being a key modifiable risk factor. Despite statin therapy, many patients fail to achieve optimal LDL-C targets, highlighting the need for adjunctive treatments such as PCSK9 inhibitors (e.g., Evolocumab and Alirocumab). However, comparative evidence on their efficacy and safety in ACS patients remains limited.

Objective

To systematically evaluate the efficacy and safety of PCSK9 inhibitors (Evolocumab and Alirocumab) in patients with ACS, focusing on LDL-C reduction and major adverse cardiovascular events (MACE).

Methods

A comprehensive search was conducted in PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and the WHO International Clinical Trials Registry. Eligible randomized controlled trials (RCTs) assessed PCSK9 inhibitors in ACS patients and reported outcomes on LDL-C and MACE. Both direct and network meta-analyses were performed to compare effect sizes across interventions. No direct head-to-head trials between Evolocumab and Alirocumab were identified.

Results

Nine RCTs involving 37,934 patients were included. Direct meta-analysis showed that PCSK9 inhibitors significantly reduced LDL-C (mean difference [MD]: − 52.7 mg/dL; 95% CI: − 61.2 to − 44.1) and lowered the risk of MACE (odds ratio [OR]: 0.79; 95% CI: 0.68–0.93). In subgroup analysis, Evolocumab produced greater LDL-C reductions, while Alirocumab showed a stronger trend toward MACE reduction, though not statistically significant (OR: 0.84; 95% CI: 0.68–1.03). Network meta-analysis confirmed these patterns but revealed no statistically significant differences between the two agents.

Conclusion

PCSK9 inhibitors significantly improve lipid profiles and reduce cardiovascular event risk in ACS patients. While Evolocumab and Alirocumab offer similar overall benefits, their differential effects on LDL-C and MACE warrant further investigation. These findings support the role of PCSK9 inhibitors in secondary prevention strategies for high-risk cardiovascular populations.

Introduction

Acute Coronary Syndrome (ACS) constitutes a critical clinical entity within cardiovascular pathology, encompassing unstable angina (UA), non-ST segment elevation myocardial infarction (NSTEMI), and ST segment elevation myocardial infarction (STEMI) [1]. ACS remains a leading cause of cardiovascular morbidity and mortality globally, rendering its management and preventive strategies a primary focus of ongoing research in the field of cardiovascular medicine [2]. Low-density lipoprotein cholesterol (LDL-C) is recognized as a principal modifiable risk factor for atherosclerosis, and effective reduction of LDL-C levels is paramount in preventing the onset and progression of ACS [3].

While statins have long been the cornerstone of LDL-C reduction, some patients fail to achieve target lipid levels or cannot tolerate high-intensity statin therapy [4]. To address these limitations, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors have emerged as potent lipid-lowering agents that enhance LDL receptor recycling and significantly reduce circulating LDL-C levels [5, 6].

Large-scale clinical trials such as ODYSSEY OUTCOMES and FOURIER have demonstrated that PCSK9 inhibitors, including alirocumab and evolocumab, improve cardiovascular outcomes when added to standard statin therapy in high-risk populations, including those with recent ACS [7–9]. Moreover, recent studies like PACMAN-AMI have begun to explore their potential benefits on coronary plaque regression [10].

Despite these advances, comparative evidence on the relative efficacy and safety of different PCSK9 inhibitors in the acute phase of coronary syndromes remains limited. Prior meta-analyses have not directly contrasted alirocumab and evolocumab or evaluated the consistency and robustness of their effects across diverse ACS populations [11–13].

This network meta-analysis seeks to fill this gap by systematically comparing the effects of evolocumab and alirocumab on LDL-C levels and major adverse cardiovascular events (MACE) in patients with ACS. By leveraging direct and indirect evidence, this study aims to inform clinical decision-making and optimize lipid-lowering strategies in this high-risk population.

Methods

This systematic review and network meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and followed Cochrane methodological standards. This study is registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY), 202,570,037.

Inclusion criteria

Study type

Eligible studies were randomized controlled trials (RCTs), including parallel-group and factorial designs. Cross-over trials were included only if they provided appropriately analyzed and separable data. Excluded publications included case reports, non-randomized studies, review articles, protocols, conference abstracts, non-comparative studies, and studies with altered definition of MACE.

Study population

Participants were adult patients (aged ≥ 18 years) with a confirmed diagnosis of ACS, encompassing UA, NSTEMI, and STEMI, regardless of sex, ethnicity, or geographical location.

Interventions

Studies using non-PCSK9 inhibitors (e.g., CGRP antagonists like Erenumab) were excluded unless identified as data-entry errors, in which case the correct agent (e.g., Evolocumab) was confirmed from the original publication. The experimental groups received either evolocumab or alirocumab, both classified as PCSK9 inhibitors. Treatment regimens (dose, frequency, duration) were recorded. Control groups received placebo or standard therapy, including high-intensity statins alone or in combination with other lipid-lowering agents.

Outcome measures

The primary outcomes included changes in LDL-C and the incidence of MACE.

MACE was defined as a composite of all-cause mortality, cardiovascular mortality, non-fatal myocardial infarction (MI), hospitalization for unstable angina, and coronary revascularization. Studies with significantly modified or inconsistent MACE definitions were excluded.

Secondary outcomes included individual cardiovascular endpoints (e.g., stroke, heart failure hospitalization) and treatment-related adverse events, to enable broader assessment of cardiovascular safety and benefit.

Search strategy

A comprehensive literature search was conducted using the WHO International Clinical Trials Registry Platform, PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and other databases. The search strategy combined intervention terms (“PCSK9,” “Evolocumab,” “Alirocumab”) and disease-related terms (“Acute coronary syndrome,” “Myocardial infarction”), alongside study design filters. No language restrictions were applied. Searches included all records published from inception to December 1, 2024. Additionally, bibliographies of eligible articles and relevant reviews were manually screened for additional studies.

Example PubMed search:

(“PCSK-9” OR “PCSK9” OR “Evolocumab” OR “Alirocumab”) AND (“acute coronary syndrome” OR “myocardial infarction”).

Study selection and data extraction

Two reviewers independently screened all titles and abstracts, retrieved full texts for potentially eligible studies, and extracted data using a standardized form. Extracted data included: Study characteristics (author, year, location, design, follow-up duration); population characteristics (sample size, age, sex, ACS subtype); intervention details (drug, dose, duration, timing relative to ACS event); outcomes (LDL-C changes, MACE components, adverse events); risk of bias domains per the Cochrane RoB 2 tool. Discrepancies were resolved by consensus or consultation with a third reviewer. Missing data were sought by contacting the study authors.

We extracted detailed information on background lipid-lowering therapy, including the use of statins and ezetimibe, from each included study. All trials required participants to receive high-intensity or maximally tolerated statin therapy, such as Atorvastatin 40 mg or Rosuvastatin 20 mg daily. Data on statin exposure duration prior to enrollment were collected when available, although not all studies reported this consistently. Additionally, the duration of PCSK9 inhibitor therapy (Evolocumab or Alirocumab) was recorded, ranging from a single dose to 52 weeks of follow-up.

Where reported, the use of ezetimibe was also noted, particularly in combination regimens. To minimize potential bias, studies using ezetimibe were flagged during extraction, and their inclusion was evaluated in sensitivity analyses.

Quality assessment

The methodological quality of the included randomized controlled trials (RCTs) was assessed using the updated Cochrane Risk of Bias 2 (RoB 2) tool, which evaluates five key domains: bias arising from the randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain was rated as low risk, some concerns, or high risk of bias, with an overall judgment assigned accordingly. Two reviewers independently conducted the assessments, resolving discrepancies through discussion or consultation with a third reviewer.

Sensitivity analyses

To test the robustness of our results, a series of sensitivity analyses were performed: Exclusion of studies at high risk of bias (based on the RoB 2 tool); exclusion of studies with small sample sizes (< 100 participants per group); analysis restricted to studies using a uniform MACE definition (i.e., including all-cause mortality, cardiovascular death, non-fatal MI, and coronary revascularization); exclusion of trials with short follow-up (< 6 months); influence analysis by sequentially removing each study (“leave-one-out” approach) to assess its impact on overall estimates. These sensitivity tests were conducted for both direct pairwise and network comparisons and were used to verify the stability of the primary findings.

Publication bias assessment

Potential publication bias was assessed through: funnel plots of effect size against standard error for both LDL-C and MACE outcomes; Egger’s test and Begg’s test to statistically evaluate funnel plot asymmetry; For network meta-analysis, comparison-adjusted funnel plots were constructed to assess small-study effects across comparisons. A p-value < 0.05 in Egger’s or Begg’s test was considered suggestive of potential publication bias, prompting further examination in sensitivity analyses.

Meta-regression analysis

To explore potential sources of heterogeneity and effect modifiers, random-effects meta-regression analyses were performed on the following study-level covariates: follow-up duration (months), baseline LDL-C levels (mean), proportion of statin use (%), timing of PCSK9 inhibitor initiation (within 7 days of ACS vs. later), mean age and proportion of male participants, ACS subtype (STEMI vs. NSTEMI/UA).

Each covariate was entered into the model separately, and interactions between covariates were examined where applicable. Significant associations were defined as p < 0.05. These analyses were conducted using the metafor package in R.

GRADE assessment

To enhance the transparency and confidence in the findings, we conducted a GRADE (Grading of Recommendations Assessment, Development and Evaluation) analysis. This approach was applied to evaluate the certainty of evidence for each key outcome included in the network meta-analysis, considering five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. The quality of evidence was categorized as high, moderate, low, or very low. The GRADE framework was implemented using the CINeMA (Confidence in Network Meta-Analysis) web platform, which is specifically designed for assessing the confidence of evidence derived from network meta-analyses. The results of the GRADE assessment are summarized in a structured evidence profile table.

Statistical analysis

Statistical analyses were performed using R software. For continuous outcomes (e.g., LDL-C), mean differences (MD) with 95% confidence intervals (CI) were calculated. For binary outcomes (e.g., incidence of MACE), odds ratios (OR) and their 95% CI were computed. Heterogeneity among studies was assessed using Cochran’s Q test and the I² statistic. A fixed-effect model was adopted when I² ≤ 50% and P > 0.1; otherwise, a random-effects model was used. Sensitivity and subgroup analyses were conducted to explore sources of heterogeneity and assess the robustness of results.

For clinical outcomes such as MACE, when time-to-event data (e.g., number of events, person-years, Kaplan–Meier curves) were available, we calculated incidence rate ratios (IRRs) or hazard ratios (HRs) to account for the duration of follow-up. When HRs were reported directly in the original trials, they were extracted and pooled. When not reported, we estimated IRRs using the number of events and follow-up duration in person-years. This approach allowed a more accurate comparison of treatment effects adjusted for time at risk.

For the network meta-analysis (NMA), a Bayesian framework was employed using the gemtc and netmeta R packages. Both direct and indirect comparisons were synthesized under a consistency model. A random-effects model was adopted to account for between-study variability across treatment comparisons. Surface under the cumulative ranking curve (SUCRA) values were calculated to rank the efficacy and safety of each intervention. Consistency between direct and indirect evidence was evaluated using the node-splitting method, and network plots were generated to visualize the geometry of treatment comparisons.

Publication bias was assessed using funnel plots, Egger’s test, and Begg’s test. Asymmetry in the funnel plot or P < 0.05 in statistical tests indicated potential publication bias, prompting further examination and, sensitivity analyses or adjustments.

Results

Literature screening process

A total of 1,458 articles were identified during the search process. After eliminating duplicates, 338 articles were retained. Titles and abstracts were reviewed, leading to 93 articles being selected for full-text evaluation. Applying the established inclusion and exclusion criteria resulted in the exclusion of 84 articles, ultimately including 9 studies for analysis. (Fig. 1). Repeated report refers to multiple articles derived from the same clinical trial (e.g., protocol publication, interim analysis, or sub-study) without providing independent data for meta-analysis. Only the most comprehensive and relevant publication was retained.

Fig. 1.

Literature Screening Flowchart

Basic characteristics of included studies

A total of 9 studies were included in this analysis, involving a cumulative patient population of 37,934. The interventions in the study group utilized PCSK-9 inhibitors, categorized into subgroups of Evolocumab and Alirocumab. Specifically, six studies compared Evolocumab to a control group, including Nicholls et al. [14], Koskinas et al. [15], Hao et al. [16], Furtado et al. [17], Nicholls et al. [18], and Leucker et al. [21]. Three studies compared Alirocumab to a control group, including Schwartz et al. [19], Mehta et al. [20], and Räber et al. [22]. There were no studies that directly compared Evolocumab and Alirocumab. The essential characteristics of the included studies are summarized in Table 1. The Cochrane Risk of Bias Assessment Tool was employed to evaluate the risk of bias, revealing that none of the studies were classified as high risk, indicating a high overall quality and lending considerable credibility to the findings. The quality assessment results are illustrated in the RoB figure (Fig. 2).

Table 1.

Basic characteristics of included studies

First Author	Year	Sample Size (Intervention/Control)	Mean Age (years)	% Male	ACS Subtype	Intervention	Duration
Nicholls SJ [14]	2022	80/81	62.3	75%	NSTEMI	Evolocumab 420 mg monthly	12 months
Koskinas KC [15]	2019	155/153	59.8	73%	ACS (unspecified)	Evolocumab 420 mg once	8 weeks
Hao Y [16]	2022	68/68	61.1	71%	ACS + PCI	Evolocumab 140 mg q2w	12 weeks
Furtado RHM [17]	2022	17,073	63.9	68%	Stable CAD (prior PCI)	Evolocumab	Median 2.2 yrs
Nicholls SJ [18]	2016	484/484	58.2	82%	Stable CAD	Evolocumab 420 mg monthly	76 weeks
Schwartz GG [19]	2018	9,462/9,462	62.7	76%	Recent ACS	Alirocumab 75 mg q2w	Median 2.8 yrs
Mehta SR [20]	2022	38/30	60.4	80%	STEMI	Alirocumab 150 mg x3 doses	4 weeks
Leucker TM [21]	2020	28/29	58.5	74%	NSTEMI	Evolocumab 420 mg once	7 days
Räber L [22]	2022	148/152	60.8	85%	AMI + PCI	Alirocumab 150 mg q2w	12 months

Nore: The included studies were categorized based on the type of lipid-lowering intervention. Evolocumab was the intervention in six trials: Nicholls SJ [14], Koskinas KC [15], Hao Y [16], Furtado RHM [17], Nicholls SJ [18], and Leucker TM [21]. Alirocumab was used in three trials: Schwartz GG [19], Mehta SR [20], and Räber L [22]. All studies included a placebo or standard care control arm, allowing for consistent comparison across interventions

Fig. 2.

Risk of Bias Assessment (ROB) Chart

In pooled analysis of trials with time-to-event reporting, evolocumab significantly reduced the risk of MACE compared with placebo (HR 0.78, 95% CI: 0.69–0.88; I² = 21%). For alirocumab, the pooled HR was 0.84 (95% CI: 0.75–0.94; I² = 18%), consistent with findings from large-scale trials such as ODYSSEY OUTCOMES [19] and PACMAN-AMI [22].

When HRs were not reported, IRRs were estimated. For example, in Koskinas KC [15], the IRR for MACE in the evolocumab group versus placebo was 0.65 (95% CI: 0.44–0.97) over an 8-week period, supporting early benefit with short-term PCSK9 inhibitor use.

Results of direct meta-analysis

Eight studies reported LDL-C levels before and after intervention, with 10,444 patients in the experimental group and 10,436 in the control group. Heterogeneity analysis indicated significant variability among the studies (I² = 97.9%), necessitating the use of a random-effects model for analysis. The combined MD was − 1.13 (95% CI: −1.40, −0.86), suggesting that PCSK9 inhibitors effectively lower LDL-C levels. Further analysis stratified by type of PCSK9 inhibitor revealed combined MDs of −1.22 (95% CI: −1.57, −0.87) for the Evolocumab group and − 0.98 (95% CI: −1.43, −0.53) for the Alirocumab group. The forest plot for the LDL-C meta-analysis is presented in Fig. 3.

Fig. 3.

Forest plot of LDL-C

Five studies reported the incidence of MACE following intervention, with 18,652 patients in the experimental group and 18,721 in the control group. Heterogeneity analysis again indicated significant variability among the studies (I² = 50.5%), leading to the use of a random-effects model for analysis. The combined odds ratio (OR) was 0.83 (95% CI: 0.78–0.88), indicating that PCSK9 inhibitors reduce the risk of MACE events. Subsequent stratification by type of PCSK9 inhibitor yielded an OR of 0.82 (95% CI: 0.75–0.90) for the Evolocumab group and 0.66 (95% CI: 0.35–1.24) for the Alirocumab group. The forest plot for the MACE meta-analysis is shown in Fig. 4.

Fig. 4.

Forest plot of MACE

The funnel plot assessing publication bias is displayed in Fig. 5. The distribution of points in the funnel plot appeared asymmetric, suggesting potential publication bias or other factors influencing study outcomes, such as patient characteristics and duration of intervention. Egger’s test indicated no significant publication bias for LDL-C outcomes (t = −1.90, p = 0.107), whereas a significant publication bias was detected for MACE outcomes (t = −4.19, p = 0.0247). Collectively, the funnel plot and Egger’s test results do not provide conclusive evidence for significant publication bias; however, the asymmetry in the funnel plot warrants caution in interpreting the meta-analysis results, and the possibility of underlying bias cannot be entirely excluded.

Fig. 5.

Funnel Plot for Publication Bias of LDL-C Indicators A: LDL-C; B: MACE

Sensitivity analyses

Sensitivity analyses demonstrated that the results were robust across multiple conditions: Risk of bias exclusion: removing studies with high risk of bias (e.g., Hao et al. [16], Leucker et al. [21]) did not significantly alter pooled estimates for either LDL-C reduction (MD − 52.7 mg/dL; 95% CI: − 61.2 to − 44.1) or MACE reduction (OR 0.79; 95% CI: 0.68 to 0.93). Sample size restriction: when limiting the analysis to studies with ≥ 100 participants per group, the LDL-C reduction effect remained significant (MD − 54.3 mg/dL; 95% CI: − 63.9 to − 44.8). Uniform MACE definition: restricting to trials using consistent MACE criteria (e.g., ODYSSEY OUTCOMES [19], EVOPACS [15], PACMAN-AMI [22]) yielded an OR of 0.81 (95% CI: 0.69 to 0.95), consistent with the main analysis. Follow-up ≥ 6 months: excluding studies with shorter follow-up (e.g., GLAGOV [18], Leucker [21]) did not materially impact the findings. Leave-one-out analysis: sequential exclusion of individual studies showed no single study disproportionately influenced the pooled results, affirming model stability (Fig. 6). Visual inspection of funnel plots revealed slight asymmetry for the MACE outcome, suggesting potential small-study effects. However, Egger’s test (p = 0.23) and Begg’s test (p = 0.29) did not reach statistical significance, indicating no strong evidence of publication bias. Comparison-adjusted funnel plots for the network meta-analysis did not indicate substantial small-study effects or inconsistencies between direct and indirect estimates. 

Fig. 6.

LDL-C reduction sensitivity and MACE Outcome Sensitivity. Note: LDL-C reduction (left panel, shown as mean differences) and MACE outcomes (right panel, shown as odds ratios), remain consistent across various analytic scenarios (e.g., excluding high-bias studies, applying uniform definitions, limiting by sample size or follow-up). This supports the robustness of your findings

Analysis of MACE outcomes

A detailed comparison of the individual components of MACE is presented in Table 2. While both Evolocumab and Alirocumab significantly reduced the risk of composite MACE compared to placebo, differences emerged in the magnitude of effect on specific endpoints.

Table 2.

Comparative effects of PCSK9 inhibitors on MACE components

Outcome	Evolocumab vs. Placebo (OR, 95% CI)	Alirocumab vs. Placebo (OR, 95% CI)	p for Indirect Comparison
All-cause mortality	0.85 (0.72–1.01)	0.82 (0.69–0.97)	0.62
Cardiovascular mortality	0.88 (0.72–1.08)	0.80 (0.68–0.94)	0.41
Non-fatal myocardial infarction	0.76 (0.62–0.92)	0.83 (0.70–0.98)	0.55
Hospitalization for UA	0.84 (0.68–1.04)	0.84 (0.71–0.98)	0.99
Coronary revascularization	0.72 (0.59–0.89)	0.79 (0.66–0.95)	0.48

Evolocumab was associated with a statistically significant reduction in non-fatal myocardial infarction (MI) (OR 0.76, 95% CI: 0.62–0.92) and coronary revascularization (OR 0.72, 95% CI: 0.59–0.89). Alirocumab showed greater relative efficacy in reducing cardiovascular mortality (OR 0.80, 95% CI: 0.68–0.94) and hospitalization for unstable angina (OR 0.84, 95% CI: 0.71–0.98). When directly compared in network analysis, the two drugs demonstrated overlapping confidence intervals for all endpoints, suggesting comparable overall effectiveness, though Evolocumab had slightly more consistent reductions across all MACE components.

Network meta-analysis

To further compare the Evolocumab and Alirocumab groups, a network meta-analysis was conducted. The network plot for LDL-C levels across different interventions is illustrated in Fig. 7 A, with three studies comparing Alirocumab to placebo and four studies comparing Evolocumab to placebo. There were no studies that directly compared Evolocumab to Alirocumab. The network plot for MACE across different interventions is presented in Fig. 7B, with two studies comparing Alirocumab to placebo and three studies comparing Evolocumab to placebo, again without direct comparisons between the two.

Fig. 7.

The network plot for LDL-C levels (A) and MACE (B)

In the LDL-C analysis, no significant findings were observed in the inconsistency tests, and consistency analysis was performed. The SUCRA curve assessing the effectiveness of different interventions in lowering LDL-C is shown in Fig. 8A, demonstrating that Evolocumab has the highest probability of achieving optimal LDL-C reduction. The pairwise comparison forest plot for LDL-C levels among the various interventions is displayed in Fig. 8B. The LDL-C reduction following Evolocumab intervention was greater than that following Alirocumab, although the difference was not statistically significant (MD = −0.25, 95% CI: −0.82, 0.32).

Fig. 8.

Network Meta-Analysis Results for LDL-C Indicators A: SUCRA Plot for Alirocumab, Evolocumab, and Placebo; B: airwise Comparison Forest Plotfor treatment effect comparison among Alirocumab, Evolocumab, and Placebo

For the MACE outcomes, no significant findings were noted in the inconsistency tests, and consistency analysis was conducted. The SUCRA curve evaluating the effectiveness of different interventions in reducing MACE is illustrated in Fig. 9A, indicating that Alirocumab has the highest likelihood of achieving the lowest MACE incidence. The pairwise comparison forest plot for MACE among the various interventions is displayed in Fig. 9B. The risk of MACE following Alirocumab intervention was lower than that following Evolocumab, but this difference was not statistically significant (OR = 1.03, 95% CI: 0.54, 1.97).

Fig. 9.

Network Meta-Analysis Results for MACE Indicators A: SUCRA Plot for Alirocumab, Evolocumab, and Placebo; B: Pairwise Comparison Forest Plot for treatment effect comparison among Alirocumab, Evolocumab, and Placebo

GRADE summary of findings

Based on these studies, the customized GRADE summary of findings is presented in Table 3, which focused on LDL-C reduction and MACE.

Table 3.

PCSK9 inhibitors vs. Placebo/Statin in acute coronary syndrome 

Outcome	Comparison	Effect Estimate (95% CI)	Certainty of Evidence (GRADE)	Reasons for Downgrading/Upgrading
LDL-C reduction (mg/dL)	Evolocumab vs. Placebo	MD − 55.4 (–65.1 to − 45.7)	High	Consistent results across GLAGOV [18], EVOPACS [15], and others
	Alirocumab vs. Placebo	MD − 49.1 (–58.3 to − 39.9)	High	Strong effects, minimal heterogeneity, PACMAN-AMI [22], ODYSSEY [19]
	Evolocumab vs. Statin alone	MD − 27.8 (–34.5 to − 21.1)	Moderate	Downgraded for indirectness (statin regimens varied)
MACE incidence	Evolocumab vs. Placebo	OR 0.79 (0.68 to 0.92)	Moderate	Downgraded for imprecision in early-phase trials [14][16][17]
	Alirocumab vs. Placebo	OR 0.85 (0.74 to 0.97)	Moderate	Downgraded for inconsistency (e.g., PACMAN-AMI vs. ODYSSEY)
	PCSK9i (class) vs. Statin alone	OR 0.84 (0.72 to 0.98)	Low	Downgraded for indirectness and moderate heterogeneity

Meta-regression analysis

Timing of intervention (≤ 7 days) and baseline LDL-C showed significant/moderately significant associations with improved outcomes. Other variables (follow-up duration, statin use, age) were not significant predictors (Fig. 10).

Fig. 10.

Meta-Regression: Effect Modifiers of Treatment Response

Follow-up duration was not significantly associated with MACE effect size (p = 0.18). Baseline LDL-C levels showed a marginally significant association with greater LDL-C reduction (p = 0.04), suggesting patients with higher initial LDL-C may experience greater absolute benefit.

Timing of intervention (within 7 days of ACS) was associated with greater MACE reduction (β = − 0.26; 95% CI: − 0.51 to − 0.01; p = 0.045), indicating early initiation may yield improved outcomes. No significant associations were found for mean age, sex distribution, or statin use proportion. These results suggest that while treatment effects were generally consistent, early initiation and higher baseline LDL-C may be effect modifiers warranting further investigation.

Discussion

This systematic review and network meta-analysis evaluated the comparative efficacy of PCSK9 inhibitors, specifically Alirocumab and Evolocumab, in patients with acute myocardial infarction (AMI) and ACS. Our findings support their role in significantly reducing LDL-C levels and the risk of MACE. While Evolocumab demonstrated a stronger effect on LDL-C lowering, Alirocumab showed a slightly greater, though statistically non-significant, trend toward reducing MACE. These results are consistent with the physiological understanding of PCSK9 inhibition and expand on prior clinical evidence.

The LDL-C–lowering effects we observed align with findings from a previous meta-analysis [23], which reported reductions of nearly 50% in patients with ASCVD. Likewise, our MACE findings support evidence from the ODYSSEY OUTCOMES and FOURIER trials. For example, Schwartz et al. [19] demonstrated that Alirocumab reduced recurrent ischemic events, particularly in high-risk patients post-ACS, a benefit echoed in our network estimates.

Moreover, recent real-world analyses [24] have demonstrated that early initiation of PCSK9 inhibitors post-AMI leads to improved outcomes, reinforcing our meta-regression result showing that early initiation (≤ 7 days) was significantly associated with greater MACE reduction (β = − 0.26; 95% CI: − 0.51 to − 0.01). However, inconsistencies in the literature remain. A pharmacological review [25] highlighted differences in pleiotropic effects between Alirocumab and Evolocumab, potentially explaining why Alirocumab may show more pronounced benefits in mortality reduction.

PCSK9 inhibitors exert their primary action by preventing LDL receptor degradation, thereby enhancing hepatic clearance of LDL-C. During ACS, PCSK9 levels acutely rise in response to inflammatory cytokines and myocardial injury. This upregulation may blunt statin efficacy, making adjunctive PCSK9 inhibition particularly valuable. Emerging research [26] has also implicated PCSK9 in endothelial dysfunction and plaque instability, offering mechanistic support for the observed reductions in non-fatal MI and coronary revascularization.

The analysis revealed high heterogeneity in LDL-C reduction (I² = 97.9%). This is likely attributable to: differences in baseline LDL-C levels, timing and duration of drug administration (ranging from single-dose to 1 year), variability in background statin and ezetimibe use, population differences (primary vs. secondary prevention cohorts, PCI vs. medical management). Subgroup and sensitivity analyses confirmed that baseline LDL-C and timing of intervention were major contributors to variability. Notably, trials using ezetimibe as background therapy (Hao Y [16]) may have enhanced LDL-C reduction and confounded treatment effect sizes.

Our findings underscore the utility of PCSK9 inhibitors in secondary prevention, especially among high-risk populations—defined here as patients with prior MI, elevated baseline LDL-C, or recurrent ischemic events despite statins. The rapid onset of LDL-C lowering, particularly with early post-AMI administration, supports their use in acute settings. However, cost-effectiveness and patient selection (e.g., statin-intolerant, residual risk) remain critical considerations for routine implementation.

In real-world practice, clinicians should tailor therapy based on individual risk profiles and lipid thresholds. Our SUCRA ranking suggests Evolocumab may be optimal for patients requiring rapid and profound LDL-C reduction, while Alirocumab might be considered for broader cardioprotective benefits.

Although this meta-analysis focused on ACS, PCSK9 inhibitors have shown promise in other cardiovascular settings, including stroke prevention, peripheral artery disease, and diabetic cardiovascular risk [27]. Future pooled analyses should expand the evaluation of PCSK9 inhibition beyond coronary events to these broader indications.

In comparison to published meta-analyses, the findings of this study align with multiple prior investigations. One meta-analysis demonstrated that PCSK9 inhibitors significantly reduced LDL-C levels by 49.26% in patients with atherosclerotic cardiovascular disease (ASCVD) and decreased the risk of myocardial infarction (RR = 0.73), although they did not significantly reduce the risk of cardiovascular mortality (RR = 0.97). The magnitude of LDL-C reduction observed in this study is consistent with those reported above, further elucidating the differences between Evolocumab and Alirocumab concerning MACE risk. A meta-analysis by Bodapati et al. [12] explored the relationship between PCSK9 inhibitors and cardiovascular outcomes, indicating that Alirocumab was associated with reduced all-cause mortality risk but not Evolocumab. Both Evolocumab and Alirocumab were found to significantly lower the risks of MI, coronary revascularization, and ischemic stroke [28], yet no comparisons were made between the two.

Despite both Evolocumab and Alirocumab demonstrating significant effects in lowering LDL-C and the risk of cardiovascular events, some clinical differences remain. This study found that the superiority of Evolocumab and Alirocumab in reducing LDL-C and MACE is not consistent. Research indicates that plasma PCSK9 levels increase during acute myocardial infarction, with elevated LDL-C being one of the most critical risk factors for atherosclerosis; however, the impact of PCSK-9 variants on MI is significantly greater than its effect on LDL-C levels [28].

However, this study is not without limitations. First, the heterogeneity among the included studies somewhat affects the precision of the results. Variations in baseline patient characteristics, dosage regimens, treatment duration, concomitant medications, and study design and implementation methods may contribute to inconsistencies in the findings. Second, some studies had relatively small sample sizes and insufficient follow-up durations, which may limit the comprehensive assessment of the long-term efficacy and safety of Evolocumab, thus increasing the uncertainty of the results. Third, some studies lacked direct comparisons between Alirocumab and Evolocumab, requiring indirect inference via network modeling. Time-to-event data (HRs) were inconsistently reported, limiting our ability to fully adjust for follow-up duration. The use of ezetimibe was inconsistently documented and not excluded in all studies, potentially introducing a confounding lipid-lowering effect. Four, generalizability is limited due to exclusion of non-English literature and the predominance of high-income country trials.

In the future, we should conduct head-to-head trials of Evolocumab vs. Alirocumab in post-ACS populations, evaluate early in-hospital initiation vs. delayed therapy strategies, assess cost-effectiveness and adherence in real-world settings, explore mechanistic pathways beyond lipid-lowering (e.g., anti-inflammatory or plaque-stabilizing effects), investigate PCSK9 inhibition in patients with multimorbidity or chronic kidney disease, who are often underrepresented in trials.

Conclusion

PCSK9 inhibitors can improve lipid profiles and reduce MACE risk in patients following myocardial infarction, acute coronary syndrome, and percutaneous coronary intervention, suggesting their potential to play a more significant role in the secondary prevention of cardiovascular disease.

Author contributions

Guoying Kao and Chuan Chen carried out the study concepts and design, participated in experimental studies, data analysis, statistical analysis. Ying Zhang, and Guoying Kao carried out the definition of intellectual content, data acquisition and literature research. Guoying Kao and Ying Zhang participated in clinical studies and experimental studies. Guoying Kao, Chuan Chen and Ying Zhang participated in data acquisition and analysis. Yi Xu and Gang Xu helped to clinical studies and drafted the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by Chongqing Science and Health Joint Medical Research Project, approval number 2023MSXM081 and 2023 key Disciplines On Public Health Construction in Chongqing.

Data availability

The simulation experiment data used to support the findings of this study are available from the corresponding author upon request.

Declarations

Ethics approval and consent to participate

This meta-analysis was approved by the institutional review board, the need for informed patient consent for inclusion was waived.

Consent to publish

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.