Mean Platelet Volume Predicts Short-term Prognosis in Young Patients with St-segment Elevation Myocardial Infarction

The prognostic value of mean platelet volume in patients with coronary artery disease: An updated systematic review with meta‐analyses

Background

Mean platelet volume (MPV) is a widely available laboratory index, however its prognostic significance in patients with coronary artery disease (CAD) is still unclear. We intended to investigate and pool the evidence on the prognostic utility of admission MPV in predicting clinical outcomes in patients with CAD.

Methods

PubMed, Web of Science, and Scopus were the major databases used for literature search. The risk of bias was assessed using the quality in prognostic factor studies. We used random‐effects pairwise analysis with the Knapp and Hartung approach supported further with permutation tests and prediction intervals (PIs).

Results

We identified 52 studies with 47,066 patients. A meta‐analysis of nine studies with 14,864 patients demonstrated that one femtoliter increase in MPV values was associated with a rise of 29% in the risk of long‐term mortality (hazard ratio [HR] 1.29, 95% confidence interval [CI] 1.22–1.37) in CAD as a whole. The results were further supported with PIs, permutation tests and leave‐one‐out sensitivity analyses. MPV also demonstrated its stable and significant prognostic utility in predicting long‐term mortality as a linear variable in patients treated with percutaneous coronary intervention (PCI) and presented with acute coronary syndrome (ACS) (HR 1.29, 95% CI 1.20–1.39, and 1.29, 95% CI 1.19–1.39, respectively).

Conclusion

The meta‐analysis found robust evidence on the link between admission MPV and the increased risk of long‐term mortality in patients with CAD patients, as well as in patients who underwent PCI and patients presented with ACS.

The Prognostic Utility of Mean Platelet Volume in Patients With Acute Coronary Syndrome: A Systematic Review With Meta-Analyses

Mean platelet volume (MPV) is a hematological index that is routinely measured in clinical settings. Although many studies have been conducted to investigate the prognostic significance of MPV in acute coronary syndromes (ACS), the findings have been inconsistent. The goal of this study was to systematically review all current evidence on the association between admission MPV and clinical outcomes after ACS. PubMed, Scopus, Web of Science, and other databases were searched. The primary endpoints were major adverse cardiovascular events (MACE) and mortality. We applied a Knapp and Hartung adjustment, prediction interval calculations and permutation tests during pairwise meta-analyses. A one-stage dose-response meta-analysis was also conducted. The meta-analysis consisted of 41 studies with 33443 participants. Mean platelet volume, as a continuous variable, was associated with the risk of long-term mortality (hazard ratio 1.33, 95% CI 1.19-1.48). After conducting permutation tests and calculation of prediction intervals, this association remained significant. The results for MACE were nonsignificant. Linear models were the best fitted models during dose-response meta-analyses, trends for nonlinearity were significant for long-term endpoints. Admission MPV was associated with long-term mortality in ACS patients, with nonlinear associations between MPV levels and long-term clinical outcomes.

From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.

Immature Platelets and Risk of Cardiovascular Events among Patients with Ischemic Heart Disease: A Systematic Review

BACKGROUND

 Immature platelets are larger and may be more thrombogenic than mature platelets. This systematic review included studies on the association between mean platelet volume (MPV), immature platelet count (IPC), and immature platelet fraction (IPF) and the risk of major cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS) or stable coronary artery disease (CAD).

METHODS

 The literature search included studies in PubMed, Embase, Web of Science, and Cochrane Library. The review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Effect estimates that included multivariate adjusted odds ratios, relative risks, or hazard ratios were extracted.

RESULTS

 Forty-two studies were identified. High MPV was positively associated with MACE in 20 of 26 studies of patients with ACS, four of five studies in patients with stable CAD, and in all six studies comprising a combined population with ACS and stable CAD. Using continuous models of MPV in patients with ACS, effect estimates varied from 0.90 (95% confidence interval [CI]: 0.95-1.03) to 1.66 (95% CI: 1.32-2.09). The strength of these associations was broadly similar among patients with stable CAD and in combined populations. Five studies investigated IPC or IPF as exposures and all reported positive associations with MACE among patients with ACS, stable CAD, or in combined populations.

CONCLUSION

 This review demonstrated clear evidence for positive associations between measures of immature platelets and subsequent risk of MACE in acute and stable ischemic heart disease patients.

From CT to artificial intelligence for complex assessment of plaque-associated risk

The recent technological developments in the field of cardiac imaging have established coronary computed tomography angiography (CCTA) as a first-line diagnostic tool in patients with suspected coronary artery disease (CAD). CCTA offers robust information on the overall coronary circulation and luminal stenosis, also providing the ability to assess the composition, morphology, and vulnerability of atherosclerotic plaques. In addition, the perivascular adipose tissue (PVAT) has recently emerged as a marker of increased cardiovascular risk. The addition of PVAT quantification to standard CCTA imaging may provide the ability to extract information on local inflammation, for an individualized approach in coronary risk stratification. The development of image post-processing tools over the past several years allowed CCTA to provide a significant amount of data that can be incorporated into machine learning (ML) applications. ML algorithms that use radiomic features extracted from CCTA are still at an early stage. However, the recent development of artificial intelligence will probably bring major changes in the way we integrate clinical, biological, and imaging information, for a complex risk stratification and individualized therapeutic decision making in patients with CAD. This review aims to present the current evidence on the complex role of CCTA in the detection and quantification of vulnerable plaques and the associated coronary inflammation, also describing the most recent developments in the radiomics-based machine learning approach for complex assessment of plaque-associated risk.