Myocardial contrast echocardiography predicts major adverse cardiovascular and cerebrovascular events in the population after percutaneous coronary intervention—a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Existing studies demonstrated that myocardial contrast echocardiography (MCE), which provides residual myocardial viability (MV) information, is an effective long-term prognostic tool. However, the specific prognostic value of microvascular perfusion (MVP) parameters detected by contemporary intravenous MCE (IV-MCE) remains to be fully elucidated. Moreover, there is ongoing debate regarding the optimal quantitative diagnostic indicator measured by IV-MCE, including A, β, and myocardial blood flow (MBF), for major adverse cardiovascular and cerebrovascular events (MACCEs). This study aims to identify the most effective IV-MCE parameter for predicting MACCEs through a comprehensive meta-analysis. Methods: We conducted a comprehensive search for retrospective or prospective cohort studies written in English and Chinese that evaluated the prognostic value of IV-MCE in patients with coronary artery disease (CAD) after percutaneous coronary intervention (PCI). PubMed, Embase, Web of Science, Cochrane, SinoMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (CSTJ), and Wanfang were searched until March 20, 2025. The primary outcome was the diagnostic efficacy of myocardial perfusion score index (MPSI), A, β, and MBF for MACCEs. Secondary outcomes included associations between abnormal MVP, microvascular obstruction (MVO), MPSI, β, MBF and MACCEs occurrence. Summary receiver operating characteristic (SROC) curves and hazard ratios (HRs) were used to assess diagnostic performance and analyze associations by Stata 15.0. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The study protocol was prospectively registered in the PROSPERO database (CRD42024524641). Results: =69.5%, 83.9%, and 95.0%) was observed in abnormal MVP, MPSI, and β across studies, and publication bias was identified in all five studies. The area under the curve (AUC) (95% CI) for MPSI, A, β, and MBF in diagnosing MACCEs was 0.84 (0.80-0.87), 0.83 (0.80-0.86), 0.84 (0.80-0.87), and 0.73 (0.69-0.77), respectively. Deeks' funnel plots further confirmed that there was no significant publication bias in the results for these four studies. Conclusions: The evidence supported that both qualitative and quantitative parameters of IV-MCE can provide moderate predictive power for MACCEs occurrence after PCI, with MPSI and β showing the highest diagnostic performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".