Performance of Manchester Acute Coronary Syndromes decision rules in acute coronary syndrome: a systematic review and meta-analysis.

BACKGROUND AND IMPORTANCE: Multiple decision-aiding models are available to help physicians identify acute coronary syndrome (ACS) and accelerate the decision-making process in emergency departments (EDs). OBJECTIVE: This study evaluates the diagnostic performance of the Manchester Acute Coronary Syndrome (MACS) rule and its derivations, enhancing the evidence for their clinical use. DESIGN: Systematic review and meta-analysis. SETTINGS AND PARTICIPANTS: Medline, Embase, Scopus, and Web of Science were searched from inception until October 2023 for studies including adult ED patients with suspected cardiac chest pain and inconclusive findings requiring ACS risk-stratification. OUTCOME MEASURES AND ANALYSIS: The predictive value of MACS, Troponin-only MACS (T-MACS), or History and Electrocardiogram-only MACS (HE-MACS) decision aids for diagnosing acute myocardial infarction (AMI) and 30-day major adverse cardiac outcomes (MACEs) among patients admitted to ED with chest pain suspected of ACS. Overall sensitivity and specificity were synthesized using the 'Diagma' package in STATA statistical software. Applicability and risk of bias assessment were performed using the QUADAS-2 tool. MAIN RESULTS: For AMI detection, MACS has a sensitivity of 99% [confidence interval (CI): 97-100], specificity of 19% (CI: 10-32), and AUC of 0.816 (CI: 0.720-0.885). T-MACS shows a sensitivity of 98% (CI: 98-99), specificity of 35% (CI: 29-42), and AUC of 0.859 (CI: 0.824-0.887). HE-MACS exhibits a sensitivity of 99% (CI: 98-100), specificity of 9% (CI: 3-21), and AUC of 0.787 (CI: 0.647-0.882). For MACE detection, MACS demonstrates a sensitivity of 98% (CI: 94-100), specificity of 22% (CI: 10-42), and AUC of 0.804 (CI: 0.659-0.897). T-MACS displays a sensitivity of 96% (CI: 94-98), specificity of 36% (CI: 30-43), and AUC of 0.792 (CI: 0.748-0.830). HE-MACS maintains a sensitivity of 99% (CI: 97-99), specificity of 10% (CI 6-16), and AUC of 0.713 (CI: 0.625-0.787). CONCLUSION: Of all the MACS models, T-MACS displayed the highest overall accuracy due to its high sensitivity and significantly superior specificity. T-MACS exhibits very good diagnostic performance in predicting both AMI and MACE. This makes it a highly promising tool for managing patients with acute chest pain.

Cincinnati Prehospital Stroke Scale (CPSS) as a Screening Tool for Early Identification of Cerebral Large Vessel Occlusions; a Systematic Review and Meta-analysis.

Introduction: Large vessel occlusion (LVO) strokes are associated with worse functional outcomes and higher mortality rates. In the present systematic review and meta-analysis, we evaluated the diagnostic yield of the Cincinnati Prehospital Stroke Scale (CPSS) in detecting LVO. Methods: We performed an extensive systematic search among online databases including Medline, Embase, Web of Science, and Scopus, until July 31st, 2023. We also conducted a manual search on Google and Google scholar, along with citation tracking to supplement the systematic search in retrieving all studies that evaluated the diagnostic accuracy of the CPSS in detecting LVO among patients suspected to stroke. Results: Fourteen studies were included in the present meta-analysis. CPSS showed the sensitivity of 97% (95% CI: 87%-99%) and the specificity of 17% (95% CI: 4%-54%) at the cut-off point of ≥1. The optimal threshold was determined to be ≥2, with a sensitivity of 82% (95% CI: 74%-88%) and specificity of 62% (95% CI: 48%-74%) in detecting LVO. At the highest cut-off point of ≥3, the CPSS had the lowest sensitivity of 60% (95% CI: 51%-69%) and the highest specificity of 81% (95% CI: 71%-88%). Sensitivity analyses showed the robustness of the results regardless of study population, inclusion of hemorrhagic stroke patients, pre-hospital or in-hospital settings, and the definition of LVO. Conclusion: A very low level of evidence demonstrated that CPSS, with a threshold set at ≥2, is a useful tool for identifying LVO stroke and directing patients to CSCs, both in prehospital and in-hospital settings.

Diagnostic and Prognostic Values of S100B versus Neuron Specific Enolase for Traumatic Brain Injury; a Systematic Review and Meta-analysis.

Introduction: Traumataic brain injury (TBI) represents a significant global health burden. This systematic review delves into the comparison of S100B and Neuron-Specific Enolase (NSE) regarding their diagnostic and prognostic accuracy in TBI within the adult population. Methods: Conducted on October 21, 2023, the search identified 24 studies encompassing 6454 adult patients. QUADAS-2 and QUAPAS tools were employed to assess the risk of bias. The analyses aimed to evaluate the diagnostic and prognostic performance of S100B and NSE based on sensitivity, specificity, and area under the curve (AUC). The outcomes were detecting intracranial injury, mortality, and unfavorable outcome. Results: Pooled data analysis tended towards favoring S100B for diagnostic and prognostic purposes. S100B exhibited a diagnostic AUC of 0.74 (95% confidence interval (CI): 0.70-0.78), sensitivity of 80% (95% CI: 63%-90%), and specificity of 59% (95% CI: 45%-72%), outperforming NSE with an AUC of 0.66 (95% CI: 0.61-0.70), sensitivity of 74% (95% CI: 53%-88%), and specificity of 46% (95% CI: 24%-69%). Notably, both biomarkers demonstrated enhanced diagnostic value when blood samples were collected within 12 hours post-injury. The analyses also revealed the excellent diagnostic ability of S100B with a sensitivity of 99% (95% CI: 4%-100%) and a specificity of 76% (95% CI: 51%-91%) in mild TBI patients (AUC = 0.89 [0.86-0.91]). In predicting mortality, S100B showed a sensitivity of 90% (95% CI: 65%-98%) and specificity of 61% (95% CI: 39%-79%), slightly surpassing NSE's performance with a sensitivity of 88% (95% CI: 76%-95%) and specificity of 56% (95% CI: 47%-65%). For predicting unfavorable outcomes, S100B exhibited a sensitivity of 83% (95% CI: 74%-90%) and specificity of 51% (95% CI: 30%-72%), while NSE had a sensitivity of 80% (95% CI: 64%-90%) and specificity of 59% (95% CI: 46%-71%). Conclusion: Although neither biomarker has shown promising diagnostic performance in detecting abnormal computed tomography (CT) findings, they have displayed acceptable outcome prediction capabilities, particularly with regard to mortality.

The value of Coronary Artery Disease - Reporting and Data System (CAD-RADS) in Outcome Prediction of CAD Patients; a Systematic Review and Meta-analysis.

Introduction: Coronary computed tomographic angiography (CCTA) reporting has traditionally been operator-dependent, and no precise classification is broadly used for reporting Coronary Artery Disease (CAD) severity. The Coronary Artery Disease Reporting and Data Systems (CAD-RADS) was introduced to address the inconsistent CCTA reports. This systematic review with meta-analysis aimed to comprehensively appraise all available studies and draw conclusions on the prognostic value of the CAD-RADS classification system in CAD patients. Method: Online databases of PubMed, Embase, Scopus, and Web of Science were searched until September 19th, 2022, for studies on the value of CAD-RADS categorization for outcome prediction of CAD patients. Results: 16 articles were included in this systematic review, 14 of which had assessed the value of CAD-RADS in the prediction of major adverse cardiovascular events (MACE) and 3 articles investigated the outcome of all-cause mortality. Our analysis demonstrated that all original CAD-RADS categories can be a predictor of MACE [Hazard ratios (HR) ranged from 3.39 to 8.63] and all categories, except CAD-RADS 1, can be a predictor of all-cause mortality (HRs ranged from 1.50 to 3.09). Moreover, higher CAD-RADS categories were associated with an increased hazard ratio for unfavorable outcomes among CAD patients (p for MACE = 0.007 and p for all-cause mortality = 0.018). Conclusion: The evidence demonstrated that the CAD-RADS classification system can be used to predict incidence of MACE and all-cause mortality. This indicates that the implementation of CAD-RADS into clinical practice, besides enhancing the communication between physicians and improving patient care, can also guide physicians in risk assessment of the patients and predicting their prognosis.

Prognostic Value of The Leuko-Glycemic Index in Acute Myocardial Infarction; a Systematic Review and Meta-Analysis.

Introduction: In recent years, studies have provided evidence on the prognostic value of the leuko-glycemic index (LGI) in acute myocardial infarction (MI), but there is a lack of consensus. In addition, various reported cut-offs for LGI have raised concern regarding its clinical applicability. So, to conclude, through this systematic review and meta-analysis, we aimed to investigate all available evidence on the prognostic value of LGI in acute MI. Methods: Two independent researchers summarized records available in the four main databases of Medline (Via PubMed), Embase, Scopus, and Web of Science until 15 Sep 2022. Articles studying the prognostic value of the LGI in acute MI were included. Finally, sensitivity, specificity, prognostic odds ratio, and the area under the curve (AUC) for LGI were analyzed and reported. Results: Eleven articles were included (3701 patients, 72.1% male). Based on the analyses, AUC, sensitivity, and specificity for LGI in prediction of mortality following acute MI were 0.77 (95% CI: 0.73 to 0.80), 0.75 (95% CI: 0.62 to 0.84), and 0.66 (95% CI: 0.51 to 0.78), respectively. Positive and negative post-test probability of LGI in prediction of mortality were 21% and 5%, respectively. AUC, sensitivity, and specificity for LGI in prediction of major cardiac complications after acute MI were 0.81 (95% CI: 0.77 to 0.84), 0.84 (95% CI: 0.70 to 0.92), and 0.64 (95% CI: 0.49 to 0.84), respectively. Also, the Positive and negative post-test probability of LGI in this regard were 59% and 13%, respectively. Conclusion: Although the results demonstrated that the LGI could predict mortality and acute cardiac complication after MI, the low post-test probability of LGI in risk stratification of patients raises questions regarding its applicability. Nevertheless, as most of the available studies have been conducted in the Latino/Hispanic population, further evidence is warranted to generalize the validity of this tool to other racial populations.

Can metformin use reduce the risk of stroke in diabetic patients? A systematic review and meta-analysis.

BACKGROUND AND AIM: Stroke and cardiovascular diseases are major causes of death and disability, especially among diabetic patients. Some studies have shown that metformin has been effective in preventing cardiovascular diseases. In this study, we aim to evaluate the effect of metformin on stroke in type 2 diabetic patients. METHODS: A comprehensive search was conducted in Medline, Embase, Scopus, and Web of Science databases from their inception till 1st July 2022. Randomized clinical trials (RCT) and cohort studies were included. Two independent researchers screened the records, extracted the data, and assessed the risk of bias and certainty of evidence. Findings were reported as risk ratio (RR) and 95% confidence interval (CI). All statistical analyses were performed using the STATA 17.0 software package. RESULTS: Analysis of 21 included studies with 1,392,809 patients demonstrated that metformin monotherapy was effective in reducing stroke risk in both RCTs (RR = 0.66, 95% CI: 0.50, 0.87 p = 0.004) and cohort studies (RR = 0.67, 95% CI: 0.55, 0.81, p < 0.0001). However, combined administration of metformin with other antihyperglycemic agents had no significant effect on stroke risk reduction in either the RCTs (RR = 0.92, 95% CI: 0.69, 1.22 p = 0.558) or the cohort studies (RR = 0.79, 95% CI: 0.59, 1.06, p = 0.122). CONCLUSION: Low to moderate level of evidence in RCTs showed that metformin monotherapy could reduce stroke risk in type 2 diabetic patients. However, the preventive effect of metformin in stroke was not observed in patients who received a combination of metformin plus other hypoglycemic agents.

Serum Glial Fibrillary Acidic Protein in Detecting Intracranial Injuries Following Minor Head Trauma; a Systematic Review and Meta-Analysis.

Introduction: Developing novel diagnostic and screening tools for exploring intracranial injuries following minor head trauma is a necessity. This study aimed to evaluate the diagnostic value of serum glial fibrillary acidic protein (GFAP) in detecting intracranial injuries following minor head trauma. Methods: An extensive search was performed in Medline, Embase, Scopus, and Web of Science databases up to the end of April 2022. Human observational studies were chosen, regardless of sex and ethnicity of their participants. Pediatrics studies, report of diagnostic value of GFAP combined with other biomarkers (without reporting the GFAP alone), articles including patients with all trauma severity, defining minor head trauma without intracranial lesions as the outcome of the study, not reporting sensitivity/specificity or any other values essential for computation of true positive, true negative, false positive and false-negative, being performed in the prehospital setting, assessing the prognostic value of GFAP, duplicated reports, preclinical studies, retracted articles, and review papers were excluded. The result was provided as pooled sensitivity, specificity, diagnostic score and diagnostic odds ratio, and area under the summary receiver operating characteristic (SROC) curve with a 95% confidence interval (95% CI). Results: Eventually, 11 related articles were introduced into the meta-analysis. The pooled analysis implies that the area under the SROC curve for serum GFAP level in minor traumatic brain injuries (TBI) was 0.75 (95% CI: 0.71 to 0.78). Sensitivity and specificity of this biomarker in below 100 pg/ml cut-off were 0.83 (95% CI: 0.78 to 0.89) and 0.39 (95% CI: 0.24 to 0.53), respectively. The diagnostic score and diagnostic odds ratio of GFAP in detection of minor TBI were 1.13 (95% CI: 0.53 to 1.74) and 3.11 (95% CI: 1.69 to 5.72), respectively. The level of evidence for the presented results were moderate. Conclusion: The present study's findings demonstrate that serum GFAP can detect intracranial lesions in mild TBI patients. The optimum cut-off of GFAP in detection of TBI was below 100 pg/ml. As a result, implementing serum GFAP may be beneficial in mild TBI diagnosis for preventing unnecessary computed tomography (CT) scans and their related side effects.