Fragmented QRS frequency in patients with cardiac syndrome X

The Diagnostic Value of ECG Characteristics for Vasospastic and Microvascular Angina: A Systematic Review

BACKGROUND

Coronary vascular dysfunction comprises VSA and/or MVA and is more common in women than in men with angina without obstructive coronary artery disease (ANOCA). Invasive coronary function testing is considered the reference test for diagnosis, but its burden on patients is large. We aimed to investigate the potential of electrocardiography (ECG) as noninvasive marker for vasospastic angina (VSA) and microvascular angina (MVA) diagnosis.

METHODS

We systematically screened Pubmed and EMBASE databases for studies reporting on ECG characteristics in ANOCA patients with (a suspicion of) coronary vascular dysfunction. We assessed study quality using QUADAS-2. We extracted data on diagnostic values of different ECG characteristics and analyzed whether the studies were sex-stratified.

RESULTS

Thirty publications met our criteria, 13 reported on VSA and 17 on MVA. The majority addressed repolarization-related ECG parameters. Only 1 of the 13 VSA papers and 4 of the 17 MVA papers showed diagnostic accuracy measures of the ECG characteristics. The presence of early repolarization, T-wave alternans, and inverted U waves showed of predictive value for VSA diagnosis. The QTc interval was predictive for MVA diagnosis in all six studies reporting on QTc interval. Sex-stratified results were reported in only 5 of the 30 studies and 3 of those observed sex-based differences.

CONCLUSIONS

ECG features are not widely evaluated in diagnostic studies for VSA and MVA. Those features predictive for VSA and MVA diagnosis mostly point to repolarization abnormalities and may contribute to noninvasive risk stratification.

Integrated analysis of microarray data to identify the genes critical for the rupture of intracranial aneurysm

Intracranial aneurysm (IA) is a localized dilation of the blood vessel. The present study was designed to explore the mechanisms of rupture of IA. GSE13353 (including 11 ruptured and 8 unruptured IA samples) and GSE15629 (including 8 ruptured and 6 unruptured IA samples) were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) identified using limma and MetaDE packages were merged, and a protein-protein interaction (PPI) network analysis was performed using Cytoscape software. Pathway enrichment analysis was performed for the nodes of the PPI network using the fisher algorithm. The 100 most prominent genes in the network were designated candidate genes and a hierarchical clustering analysis was performed. The tune.svm function of e1071 package was used to construct a support vector machine (SVM) classifier, and the Candidate Cancer Gene Database was applied to analyze the characterization of gene-associated cancer. Furthermore, the genes involved in the SVM classifier were assessed via principal component analysis (PCA). In the ruptured samples, 1,292 DEGs and 1,029 DEGs separately were identified by limma and MetaDE packages. The 100 most prominent genes in the network included fibronectin 1 (FN1), amyloid β (A4) precursor protein (APP), nuclear RNA export factor 1 (NXF1) and signal transducer and activator of transcription 3 (STAT3). Pathway enrichment analysis identified that toll-like receptor 3 (TLR3) was enriched in the Toll-like receptor signaling pathway. A total of 15 genes (including FN1) were used to construct the SVM classifier. NXF1 was identified to be associated with Nervous System Cancer. PCA revealed that APP, NXF1 and STAT3 were the 3 principal components. TLR3, FN1, APP, NXF1 and STAT3 may affect the rupture of IA.