Revisão sistemática e meta-análise da acurácia diagnóstica do Supradesnivelamento do segmento ST para diagnóstico de oclusão coronária aguda / Systematic review and meta-analysis of the diagnostic accuracy of ST segment elevation for diagnosing acute coronary occlusion

OBJETIVO: Avaliar a sensibilidade diagnóstica e a especificidade do supradesnivelamento do segmento ST em um ECG de 12 derivações na detecção de oclusão coronária aguda em qualquer artéria coronariana, desafiando o atual paradigma IAMCSST-IAMSSST. MÉTODOS: Estudos do MEDLINE e Scopus (2012-2023) comparando achados de ECG com angiogramas coronários foram revisados e analisados sistematicamente seguindo as diretrizes PRISMA-DTA. O risco de viés foi avaliado pelo QUADAS-2. SELEÇÃO DE ESTUDOS: Os estudos incluídos focaram em pacientes com síndrome coronária aguda e forneceram dados que permitiram a construção de tabelas de contingência para cálculo de sensibilidade e especificidade, excluindo aqueles com condições não-SCA, critérios desatualizados de STEMI ou foco específico em bloqueios de ramo ou artérias coronárias específicas. Os dados foram extraídos sistematicamente e as estimativas de precisão dos testes agrupadas foram calculadas usando o software MetaDTA, empregando análises bivariadas para variação intra e inter-estudos. Os desfechos primários medidos foram a sensibilidade e especificidade do supradesnivelamento do segmento ST na detecção de OCA. RESULTADOS: Três estudos com 23704 participantes foram incluídos. A sensibilidade agrupada do supradesnivelamento do segmento ST para detecção de OCA foi de 43,6% (IC 95%: 34,7%-52,9%), indicando que mais da metade dos casos de OCA pode não apresentar critérios de supradesnivelamento do segmento ST. A especificidade foi de 96,5% (IC 95%: 91,2%-98,7%). Uma análise adicional usando a estratégia OMI-NOMI mostrou sensibilidade melhorada (78,1%, IC 95%: 62,7%-88,3%) mantendo especificidade semelhante (94,4%, IC 95%: 88,6%-97,3%). CONCLUSÃO: Os achados revelam uma lacuna diagnóstica significativa no atual paradigma IAMCSST-IAMSSST, com mais da metade dos casos de OCA potencialmente ausentes de supradesnivelamento do segmento ST. A estratégia OMI-NOMI poderia oferecer uma abordagem diagnóstica aprimorada. A alta heterogeneidade e o número limitado de estudos exigem interpretação cautelosa e mais pesquisas em ambientes diversos.

Systematic review and meta-analysis of diagnostic test accuracy of ST-segment elevation for acute coronary occlusion

OBJECTIVE: To evaluate the diagnostic sensitivity and specificity of ST-segment elevation on a 12­lead ECG in detecting ACO across any coronary artery, challenging the current STEMI-NSTEMI paradigm. METHODS: Studies from MEDLINE and Scopus (2012-2023) comparing ECG findings with coronary angiograms were systematically reviewed and analyzed following PRISMA-DTA guidelines. QUADAS-2 assessed the risk of bias. STUDY SELECTION: Studies included focused on AMI patients and provided data enabling the construction of contingency tables for sensitivity and specificity calculation, excluding those with non-ACS conditions, outdated STEMI criteria, or a specific focus on bundle branch blocks or other complex diagnoses. Data were extracted systematically and pooled test accuracy estimates were computed using MetaDTA software, employing bivariate analyses for within- and between-study variation. The primary outcomes measured were the sensitivity and specificity of ST-segment elevation in detecting ACO. RESULTS: Three studies with 23,704 participants were included. The pooled sensitivity of ST-segment elevation for detecting ACO was 43.6% (95% CI: 34.7%-52.9%), indicating that over half of ACO cases may not exhibit ST-segment elevation criteria. The specificity was 96.5% (95% CI: 91.2%-98.7%). Additional analysis using the OMI-NOMI strategy showed improved sensitivity (78.1%, 95% CI: 62.7%-88.3%) while maintaining similar specificity (94.4%, 95% CI: 88.6%-97.3%). CONCLUSION: The findings reveal a significant diagnostic gap in the current STEMI-NSTEMI paradigm, with over half of ACO cases potentially lacking ST-segment elevation. The OMI-NOMI strategy could offer an improved diagnostic approach. The high heterogeneity and limited number of studies necessitate cautious interpretation and further research in diverse settings.

Assessing the Impact of Sample Heterogeneity on Transcriptome Analysis of Human Diseases Using MDP Webtool.

Transcriptome analyses have increased our understanding of the molecular mechanisms underlying human diseases. Most approaches aim to identify significant genes by comparing their expression values between healthy subjects and a group of patients with a certain disease. Given that studies normally contain few samples, the heterogeneity among individuals caused by environmental factors or undetected illnesses can impact gene expression analyses. We present a systematic analysis of sample heterogeneity in a variety of gene expression studies relating to inflammatory and infectious diseases and show that novel immunological insights may arise once heterogeneity is addressed. The perturbation score of samples is quantified using nonperturbed subjects (i.e., healthy subjects) as a reference group. Such a score allows us to detect outlying samples and subgroups of diseased patients and even assess the molecular perturbation of single cells infected with viruses. We also show how removal of outlying samples can improve the "signal" of the disease and impact detection of differentially expressed genes. The method is made available via the mdp Bioconductor R package and as a user-friendly webtool, webMDP, available at http://mdp.sysbio.tools.

webCEMiTool: Co-expression Modular Analysis Made Easy.

Co-expression analysis has been widely used to elucidate the functional architecture of genes under different biological processes. Such analysis, however, requires substantial knowledge about programming languages and/or bioinformatics skills. We present webCEMiTool, a unique online tool that performs comprehensive modular analyses in a fully automated manner. The webCEMiTool not only identifies co-expression gene modules but also performs several functional analyses on them. In addition, webCEMiTool integrates transcriptomic data with interactome information (i.e., protein-protein interactions) and identifies potential hubs on each network. The tool generates user-friendly html reports that allow users to search for specific genes in each module, as well as check if a module contains genes overrepresented in specific pathways or altered in a specific sample phenotype. We used webCEMiTool to perform a modular analysis of single-cell RNA-seq data of human cells infected with either Zika virus or dengue virus.