Myocardial contrast echocardiography predicts major adverse cardiovascular and cerebrovascular events in the population after percutaneous coronary intervention—a systematic review and meta-analysis

Introduction

As the recommended treatment for acute ST-segment elevation myocardial infarction (STEMI), percutaneous coronary intervention (PCI) has become one of the most commonly performed procedures worldwide (1). Despite successful restoration of patency in the epicardial coronary arteries through PCI, up to 50% of STEMI patients still fail to achieve optimal myocardial reperfusion due to long-term ischemia-induced microvascular dysfunction. This significantly diminishes the therapeutic benefits of PCI. This phenomenon is commonly referred to as “no-reflow” (NR) (2). In 1992, a study by Ito et al. first demonstrated that intracoronary myocardial contrast echocardiography (MCE) is a reliable imaging technique for detecting NR (3).

In recent years, the development of second-generation contrast agent microbubbles, which have a smaller diameter (<8 microns) and a more stable polymer inert gas core, has allowed them to pass unimpeded through the pulmonary and systemic capillary beds (4). The assessment of microvascular perfusion (MVP) by MCE has evolved from the binary qualitative analysis (presence or absence) in the era of Ito to the current parameter-quantitative analysis. Using the functional relationship model between pulse interval (PI) and video intensity (VI) proposed by Wei et al., we can now obtain a series of important microcirculatory hemodynamic parameters, such as myocardial blood flow (MBF), myocardial blood volume (MBV)/plateau video signal intensity, and microvascular velocity/curve rise slope (β) (5). In imaging technology, advancements such as pulse inversion Doppler (PID), power modulation (PM), and contrast pulse sequence (CPS) imaging have further improved the signal-to-noise ratio based on harmonic imaging technology (6). This enables ultrasound physicians to simultaneously observe wall thickening and myocardial perfusion in real-time under an extremely low mechanical index (MI) imaging mode.

Previous research by Wang et al. established the prognostic utility of MCE (7). However, their study did not distinguish between intracoronary and intravenous MCE (IV-MCE). IV-MCE, a noninvasive imaging technique that has gained prominence in recent years, provides comprehensive myocardial perfusion assessment through both qualitative and quantitative parameters. Despite its potential, the application of quantitative MCE in clinical settings remains limited due to the complexity and time-consuming nature of the current semi-automated analysis software. This limitation has resulted in a relatively small number of related studies (8,9). We hypothesize that both qualitative and quantitative parameters derived from IV-MCE can offer significant prognostic value. Moreover, given the current lack of consensus on optimal quantitative parameters, including A, β, and MBF, for diagnosing major adverse cardiovascular and cerebrovascular events (MACCEs) (10,11), our study aimed to fill this gap through a meta-analysis. We hope that our findings will provide robust evidence for the clinical utility of quantitative MCE and facilitate its broader adoption. We present this article in accordance with the PRISMA reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-664/rc).

Methods

Search strategy

This study protocol has been prospectively registered in the PROSPERO database (CRD42024524641). Two researchers (X.W. and Y.Y.) conducted a comprehensive search of eight Chinese and English databases, including PubMed, Embase, Web of Science, Cochrane, SinoMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (CSTJ), and Wanfang, for original studies published up to March 20, 2025. No restrictions were applied regarding region or language. We searched the published literature using various combinations of the following keywords in both Chinese and English: “percutaneous coronary intervention”, “myocardial enhanced ultrasound”, “percutaneous coronary intervention”, and “myocardial contrast echocardiography”, among others. In order to find any more qualifying studies, we also manually went through the reference lists of pertinent publications. The specific search strategy was detailed in Appendix 1.

Inclusion criteria

In this meta-analysis, we followed the PICOS (population, intervention, comparison, outcome, study design) framework to define the inclusion criteria as follows: (I) patients aged >18 years with coronary artery disease (CAD) who underwent PCI; (II) all patients underwent IV-MCE after PCI, with a complete MCE scoring system definition; (III) the gold standard was the electronic medical record system documenting MACCEs, including hard events (all-cause death, non-fatal myocardial infarction, heart failure, stroke) and soft events (the development of typical angina, hospitalization for arrhythmia, chest pain, unstable angina to rule out myocardial infarction, coronary revascularization, transient ischemic attack). If a patient experienced multiple events, including death, then death was recorded as the primary event; (IV) observational studies, including prospective or retrospective cohort studies. Studies were excluded if: (I) studies in which CAD patients did not undergo PCI; (II) studies where MCE was not performed after PCI; (III) follow-up duration of fewer than three months; (IV) duplicate data from previous studies; (V) studies with inappropriate formats, including animal experiments, systematic reviews, letters, guidelines, case reports, and meta-analyses; (VI) studies written in languages other than Chinese and English

Study selection and data extraction

Two independent researchers (X.W. and Y.Y.) first imported the retrieved articles into EndNote X9 for deduplication. After deduplication, screening of the articles based on titles and abstracts was conducted. Full-text articles were then selected for further screening, with the final eligible studies determined accordingly. We extracted the following demographic information from each original study: author, year of publication, region, institution, study population (sample size, age, and sex of participants), and duration of follow-up. Additionally, we collected technical details of the MCE, including contrast agent type, injection method, and the definition standards of the MCE scoring system. A third researcher (L.C.) was consulted to settle any disputes that arose between the two researchers during the data extraction process.

Quality assessment

Two researchers (X.W. and Y.Y.) independently used the Newcastle-Ottawa scale (NOS) to assess study quality, with scores of 7–9 indicating high quality and 4–7 indicating moderate quality. The two researchers cross-checked their results, and a third researcher (L.C.) resolved any inconsistencies. For the included studies, the same two reviewers independently used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool to evaluate the quality and applicability of diagnostic accuracy studies. This tool included four risk-of-bias domains and three applicability domains. Bias was deemed low if all important questions in a domain were answered “yes”, high if any were answered “no”, and unclear if information was lacking. The two researchers discussed discrepancies to reach a consensus, with L.C. making the final decision if needed. Study quality assessments and graphical representations were completed using RevMan 5.4.

Statistical analysis

The study assessed the diagnostic capability of myocardial perfusion score index (MPSI), A, β, and MBF for MACCEs as the primary outcome. Secondary outcomes involved comparing these parameters between the MACCEs and the non-MACCEs groups. In addition, we also analyzed the association between abnormal MVP, microvascular obstruction (MVO), MPSI, β, MBF, and the occurrence of MACCEs. Heterogeneity was assessed using Cochran’s Q test and Higgins I², with P<0.1 or I²>50% indicating significant heterogeneity, leading to a random-effects model; otherwise, a fixed-effects model was used. Subgroup analyses were conducted to explore high heterogeneity. Diagnostic accuracy was analyzed using Stata (v.15.0) and Meta-Disc 1.4, employing a bivariate mixed-effects model. Forest plots calculated pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic SEN (DS), and diagnostic odds ratio (DOR), with higher DS and DOR indicating better performance. Area under the curve (AUC) values from the summary receiver operating characteristic (SROC) curve classified diagnostic performance as low (0.5–0.7), moderate (0.7–0.9), or high (0.9–1.0). The Spearman correlation coefficient determined threshold effects, with P>0.05 indicating none. Publication bias was assessed using a Deeks funnel plot, with P>0.05 suggesting no bias. For inter-group differences, standardized mean difference (SMD) and 95% confidence interval (CI) were used. Associations were analyzed using hazard ratios (HRs)/odds ratios (ORs) and 95% CIs via the “metan” package in Stata 15.0. Funnel plots and the Egger method evaluated publication bias, with the trim-and-fill method addressing significant bias. P<0.05 indicated statistical significance.

Results

Search and screening process

Based on searches using keywords, we initially identified 4,829 articles. Additionally, we hand-searched the reference lists of all retrieved articles, which yielded no further eligible studies. According to predetermined criteria, we excluded irrelevant articles, leaving 175 studies for full-text review. Among these, 12 studies had a sample size of fewer than 20 participants, 36 studies received intracoronary MCE after PCI, 27 studies did not provide complete follow-up data, and 84 studies used different definitions for diagnostic criteria. Ultimately, we identified 16 studies (8,10-24) comprising a total of 1,942 patients. A flowchart of the selection process was provided in Figure 1.

Figure 1 PRSIMA flow chart for literature screening. CNKI, China National Knowledge Infrastructure; MCE, myocardial contrast echocardiography; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Basic information of included studies

From a demographic perspective, the participants ranged in age from 28 to 86 years, had an average follow-up period of 18.6 months, and 72.30% were males. The study endpoint was set as the occurrence of MACCEs. In all 16 studies (8,10-24), endpoint events were assessed based on medical records, telephone follow-ups, and regular medical reexaminations. Basic patient information was summarized in Table 1. All studies (8,10-24) included in this meta-analysis utilized IV-MCE. MVP was analyzed by either a qualitative or a quantitative method. The qualitative analysis systems were further divided into a qualitative grading system and a semi-quantitative scoring system. Seven studies (12-17,24) used the qualitative grading system, in which three degrees of perfusion were demonstrated based on the reperfusion filling time or filling extent, including MVP, delayed MVP, and MVO, with the latter two collectively referred to as abnormal MVP. Five studies (10,11,18-20) used the semi-quantitative scoring system. Each study named the semiquantitative prognostic parameter differently, including contrast score index (CSI), perfusion score index (PSI), and MPSI. However, all of them were calculated in the same way. Thus, we referred to the abovementioned index as MPSI in this meta-analysis. Seven studies (8,10,11,19,21-23) used the quantitative system. The specific technical characteristics of each study, such as the number of myocardial segments assessed, the time to perform MCE after PCI, contrast agents, injection method, instrument models, diagnostic index, and the definition of each diagnostic index, were shown in Tables 2-4.

Quality assessment

Two reviewers (X.W. and Y.Y.) appraised the quality of 15 included studies (8,10-15,17-24) using the NOS. All 15 studies received high scores (7–9 points) on the NOS assessment (Table S1). For the study by Lu et al. (16), the two reviewers (X.W. and Y.Y.) assessed the diagnostic accuracy and applicability using the QUADAS-2 tool. After discussing their disagreements, they reached a consensus, concluding that all assessed domains in Lu et al.’s article (16) presented a low risk of bias (Figures S1,S2).

Analysis of differences in MCE semi-quantitative and quantitative parameters between the MACCEs and non-MACCEs groups

Four studies (10,11,19,20) reported the difference in MPSI between the MACCEs and non-MACCEs groups. The MPSI was lower in the non-MACCEs group compared to the MACCEs group (SMD: −1.63, 95% CI: −2.04 to −1.23, P<0.001), with considerable heterogeneity observed (I²=54.7%). Similarly, five studies (8,10,11,19,21) reported differences in A between the groups, with higher A values in the non-MACCEs group compared to the MACCEs group (SMD: 0.85, 95% CI: 0.64–1.06, P<0.001), showing no significant heterogeneity (I²=47.9%). Five studies (8,10,11,19,21) reported differences in β, with higher β values in the non-MACCEs group (SMD: 0.95, 95% CI: 0.74–1.16, P<0.001), also showing no significant heterogeneity (I²=8%). Four studies (8,10,11,21) reported differences in MBF, with higher MBF values in the non-MACCEs group (SMD: 1.10, 95% CI: 0.87–1.33, P<0.001), showing no significant heterogeneity (I²=27.5%) (Table 5) (Figures S3-S6). Next, SEN analyses were performed on these four pooled results using the leave-one-out method. By sequentially excluding each study, we found that the pooled results of the remaining studies did not differ significantly from the original, indicating that the analysis results were stable. Specific figures can be found in Appendix 2. Lastly, we evaluated publication bias using the funnel plot. Visual inspection of the plot suggested that there might be publication bias in these four results (Figures S7-S10).

Analysis of the association between MCE qualitative grading indicators and MACCEs

Four studies (12,14,17,24) provided data on the association between abnormal MVP and the occurrence of MACCEs, showing a notable correlation (HR: 2.61, 95% CI: 1.42–4.79, P=0.002), with significant heterogeneity observed (I²=69.5%). Another three studies (12,13,15) reported the correlation between MVO and MACCEs occurrence (HR: 4.51, 95% CI: 2.30–8.83, P<0.001), with no significant heterogeneity (I²=41.9%) (Table 6) (Figures S11,S12). Next, SEN analyses were performed on the two pooled results using the leave-one-out method. By sequentially excluding each study, we found that the pooled results of the remaining studies did not differ significantly from the original, indicating that the analysis results were stable. Specific figures can be found in Appendix 2. Lastly, we evaluated publication bias using a funnel plot. Visual inspection of the plot suggested that these two studies may have publication bias (Figures S13,S14).

Analysis of the association between MCE semi-quantitative and quantitative parameters and MACCEs

Three studies (10,11,18) provided data on the association between high MPSI values and the occurrence of MACCEs, showing that MPSI was significantly associated with MACCEs (HR: 5.74, 95% CI: 1.41–23.34, P=0.02), with significant heterogeneity (I²=83.9%). Additionally, three studies (10,11,22) reported on the association between low β values and MACCEs, indicating a significant correlation (HR: 7.18, 95% CI: 1.01–51.24, P=0.049), with substantial heterogeneity (I²=95%). Four studies (10,11,22,23) reported on the association between low MBF values and MACCEs, showing a significant correlation (HR: 4.62, 95% CI: 2.42–8.83, P<0.001), with no significant heterogeneity (I²=0) (Table 7) (Figures S15-S17). Next, SEN analyses were performed on the pooled results using the leave-one-out method. By sequentially excluding each study, we found that the pooled results of the remaining studies did not differ significantly from the original, indicating that the analysis results were stable. Specific figures can be found in Appendix 2. Upon visual inspection, all three results showed potential publication bias (Figures S18-S20).

Predictive capability of MCE semi-quantitative and quantitative parameters for MACCEs in patients after PCI

Based on the above statistical results, we summarized the parameters used to predict outcomes, including MPSI, A, β, and MBF. The summary results for each parameter were presented separately. However, only the study by Lu et al. (16) provided data on the SEN and SPE of abnormal MVP as a qualitative grading indicator for predicting MACCEs, with values being 66.7% and 73.9%, respectively. Due to the lack of additional data, a pooled analysis could not be conducted, and the result is presented separately.

Predictive capability of MPSI for post-PCI MACCEs

Five studies (10,11,18-20) described the SEN and SPE of MPSI for diagnosing MACCEs. The pooled SEN was 0.87 (95% CI: 0.74–0.94), with significant heterogeneity (I²=69.47%), while the pooled SPE was 0.73 (95% CI: 0.66–0.80), with no significant heterogeneity (I²=43.71%), as shown in Figure 2. The DOR, PLR, and NLR were 18.57 (95% CI: 9.11–37.83), 3.27 (95% CI: 2.59–4.12), and 0.18 (95% CI: 0.09–0.35), respectively (Figures S21,S22). The pooled AUC of the ROC curve was 0.84 (95% CI: 0.80–0.87), as shown in Figure 3. During the analysis, we found substantial heterogeneity in the pooled SEN of MPSI. To determine the source of this heterogeneity, we conducted a subgroup analysis and found that differences in the contrast agent injection methods might be contributing to the heterogeneity in the pooled SEN of MPSI (Figure S23). After pooling the three studies that used continuous IV infusion, the pooled SEN was 0.92 (95% CI: 0.85–0.99) and the pooled SPE was 0.68 (95% CI: 0.60–0.76). We also generated a Deeks funnel plot to assess for publication bias among the studies for diagnostic outcomes (Figure S24). With a P value of 0.55, there was no evidence of significant publication bias.

Figure 2 Forest plot of pooled sensitivity and specificity of MPSI for diagnosing MACCEs (10,11,18-20). CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; MPSI, Myocardial Perfusion Score Index.

Figure 3 SROC curve of MPSI for diagnosing MACCEs. Reference source corresponding to the numbers 1–5 in the figure (10,11,18-20). AUC, area under the curve; CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; MPSI, Myocardial Perfusion Score Index; SEN, sensitivity; SPE, specificity; SROC, summary receiver operating characteristic.

Predictive capability of A for MACCEs in patients post-PCI

Four studies (10,19,21,22) provided the SEN and SPE of A for diagnosing MACCEs. The pooled SEN was 0.82 (95% CI: 0.70–0.90), with no significant heterogeneity (I²=15.98%). The pooled SPE was 0.69 (95% CI: 0.51–0.83), with significant heterogeneity (I²=74.39%), as shown in Figure 4. The DOR, PLR, and NLR were 10.11 (95% CI: 3.55–28.8), 2.66 (95% CI: 1.53–4.63), and 0.26 (95% CI: 0.14–0.48), respectively (Figures S25,S26). The pooled AUC of the ROC curve was 0.83 (95% CI: 0.80–0.86), as shown in Figure 5. During the analysis, we found substantial heterogeneity in the pooled SPE for a value, but we were unable to identify the source of this heterogeneity. We also generated a Deeks funnel plot to assess for publication bias among the studies for diagnostic outcomes (Figure S27). With a P value of 0.72, there was no evidence of significant publication bias.

Figure 4 Forest plot of pooled sensitivity and specificity of A for diagnosing MACCEs (10,11,18-20). CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events.

Figure 5 SROC curve of A for diagnosing MACCEs. Reference source corresponding to the numbers 1–4 in the figure (10,19,21,22). AUC, area under the curve; CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; SEN, sensitivity; SPE, specificity; SROC, summary receiver operating characteristic.

Predictive capability of β for MACCEs in patients post-PCI

Five studies (10,11,19,21,22) provided the SEN and SPE of β for diagnosing MACCEs. The pooled SEN was 0.83 (95% CI: 0.74–0.89), with no significant heterogeneity (I²=0). The pooled SPE was 0.70 (95% CI: 0.64–0.75), with no significant heterogeneity (I²=0), as shown in Figure 6. The DOR, PLR, and NLR were 11.03 (95% CI: 6.31–19.28), 2.75 (95% CI: 2.25–3.36), and 0.25 (95% CI: 0.16–0.38), respectively (Figures S28,S29). The pooled AUC of the ROC curve was 0.84 (95% CI: 0.80–0.87), as shown in Figure 7. We also generated a Deeks funnel plot to assess for publication bias among the studies for diagnostic outcomes (Figure S30). With a P value of 0.06, there was no evidence of significant publication bias.

Figure 6 Forest plot of pooled sensitivity and specificity of β for diagnosing MACCEs (10,11,19,21,22). CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events.

Figure 7 SROC curve of β for diagnosing MACCEs. Reference source corresponding to the numbers 1–5 in the figure (10,11,19,21,22). AUC, area under the curve; CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; SEN, sensitivity; SPE, specificity; SROC, summary receiver operating characteristic.

Predictive capability of MBF for MACCEs in patients post-PCI

Four studies (10,11,21,22) provided the SEN and SPE of MBF for diagnosing MACCEs. The pooled SEN was 0.77 (95% CI: 0.63–0.87), with no significant heterogeneity (I²=36.09%). The pooled SPE was 0.68 (95% CI: 0.62–0.74), with no significant heterogeneity (I²=0%), as shown in Figure 8. The DOR, PLR, and NLR were 7.3 (95% CI: 3.34–15.97), 2.42 (95% CI: 1.86–3.14), and 0.33 (95% CI: 0.119–0.58), respectively (Figures S31,S32). The pooled AUC of the ROC curve was 0.73 (95% CI: 0.69–0.77), as shown in Figure 9. We also generated a Deeks funnel plot to assess for publication bias among the studies for diagnostic outcomes (Figure S33). With a P value of 0.10, there was no evidence of significant publication bias.

Figure 8 Forest plot of pooled sensitivity and specificity of MBF for diagnosing MACCEs. CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; MBF, myocardial blood flow.

Figure 9 SROC curve of MBF for diagnosing MACCEs. Reference source corresponding to the number 1–4 in the figure (10,11,21,22). AUC, area under the curve; CI, confidence interval; MACCEs, major adverse cardiovascular and cerebrovascular events; MBF, myocardial blood flow; SEN, sensitivity; SPE, specificity; SROC, summary receiver operating characteristic.

Threshold effect analysis

We used Meta-Disc 1.4 to perform a Spearman test on the four studies mentioned above. The resulting P values were 0.09, 0.90, 0.87, and 0.60, all greater than 0.05, demonstrating that the threshold effect was not the source of heterogeneity.

Discussion

This study aims to explore the prognostic value of qualitative and quantitative MVP information provided by IV-MCE for patients with CAD after PCI. Our results showed that higher MPSI or lower β and MBF were associated with the occurrence of MACCEs. Similarly, abnormal MVP and MVO were associated with the occurrence of MACCEs. To further clarify the diagnostic efficacy of the indicators, we extracted the SEN and SPE data of MPSI, A, β, and MBF for the diagnosis of MACCEs and performed a diagnostic meta-analysis. The results of the meta-analysis indicated that MPSI, A, β, and MBF all provided moderate predictive ability for the occurrence of MACCEs, with the AUC of the summary ROC curve being MPSI = β > A > MBF. This indicates that either qualitative or quantitative MCE can provide good prognostic value. Since current clinical guidelines have not yet provided authoritative reference values for MPSI, A, β, and MBF, the diagnostic ROC curves drawn in this study can also offer ideal reference values for these four indicators when they are at their best diagnostic effect, thereby providing a reference for clinicians.

Our meta-analysis noted that MPSI and β had higher predictive value than A and MBF. First, due to the special clinical significance of MPSI and β, we discussed the reasons for the heterogeneity in the meta-analysis of these two parameters. We believe that the heterogeneity in the statistical results of MPSI and β in the association analysis was due to Zhou et al.’s article (11) only including patients who underwent PCI for anterior wall STEMI, while the other studies (10,18,22) included a broader PCI population, including patients with inferior and lateral wall STEMI as well as NSTEMI. Moreover, differences in the contrast agent injection method may be a source of heterogeneity in the combined SEN of MPSI, as bolus injection is more likely to cause acoustic attenuation in the far field compared to continuous IV infusion. Additionally, we believe that the reasons why the semi-quantitative assessment indicator MPSI showed higher predictive value than the quantitative parameters A and MBF may include the following points. First, although quantitative parameters are theoretically more robust in interpreting perfusion results, in practice, quantitative analysis still requires experienced physicians to manually trace the myocardial contour, i.e., the region of interest (ROI). If the ROI covers the ventricular cavity, the measured parameters may not accurately represent the state of myocardial microcirculation. Second, during real-time examination, the size, shape, and position of the myocardial contour change with the cardiac cycle, and motion artifacts can reduce image quality and affect the final fitted function curve (25). Therefore, these may all be possible explanations for the higher predictive value of qualitative MCE assessment.

Currently, patients with NR have two clinical outcomes: spontaneous reversal or persistence, with unclear mechanisms (26). No-reflow may result from: embolization of distal microvessels by unstable plaques during acute phase and surgery, causing microinfarction and releasing inflammatory and vasoconstrictive substances (27,28); ischemic injury leading to cardiomyocyte and endothelial cell death, reduced nitric oxide, myocardial edema, and microvascular dilation dysfunction, followed by microcirculatory obstruction; sudden reperfusion after PCI causing neutrophil influx, oxygen free radical and protease production and endothelial cell connection destruction (29); individual differences in microvascular injury susceptibility, with 1976TC gene polymorphism carriers more prone to NR (30). Given NR’s complex mechanisms, despite its clinical significance, there’s a lack of effective treatments to reduce adverse events. Close follow-up and timely treatment for PCI patients with positive NR may best improve prognosis. This study showed that both qualitative and quantitative analyses have good prognostic value. With its non-invasive, portable, radiation-free advantages, IV-MCE could be a regular follow-up item for NR patients post-reperfusion. Combining MVP data assessed by IV-MCE from persistent NR patients with potential inflammatory parameters like prognostic nutritional index (PNI) and growth differentiation factor-15 (GDF-15), which predict NR, could establish a clinical risk model, enabling more precise individualized management plans for PCI patients (31,32).

This study has limitations. First, reactive hyperemia after reperfusion can last up to 2 days, potentially leading to an underestimation of NR if MCE is performed during this period. Future studies should establish a standardized time point (e.g., 48 hours after PCI). Second, while MCE provides comprehensive prognostic evidence for CAD patients undergoing PCI, our study did not distinguish whether PCI was for ACS or angina. Future research should ensure the homogeneity of the study population. Third, due to all included patients undergoing PCI, death events were rare, so we used a composite endpoint instead. Given the average follow-up of 18.6 months (<2 years), a 5-year follow-up meta-analysis is planned to collect more events and improve our results. Finally, the inclusion of single-center studies with small patient cohorts, coupled with the presence of publication bias, may restrict the generalizability of the findings. Therefore, more multi-center studies with large samples are required to address these limitations.

However, we believe that the results of this study could provide a reference value for clinical research in this field. MVP assessed by MCE can serve as a practical prognostic biomarker after PCI, which is conducive to clinicians precisely stratifying patients and guiding treatment. New deep learning (DL)-based algorithms enable MCE quantitative analysis software to fully automatically track and segment the myocardial ROI, discarding low-quality images (33,34). This reduces MCE examination time to 6–9% of traditional methods (8). We recommend integrating artificial intelligence (AI) into commercial software to enhance the clinical potential of IV-MCE.

Conclusions

Overall, both qualitative and quantitative assessments of IV-MCE can provide moderate predictive power for the occurrence of MACCEs after PCI, with MPSI and β showing the highest diagnostic performance, warranting further exploration. We look forward to combining these parameters with other potential predictive indicators in the future to enhance diagnostic accuracy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-664/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-664/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-664/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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