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Introduction: Hepatocellular carcinoma (HCC) and liver cirrhosis (LC) stand as the primary causes of global mortality. Given their profound impact, the development of highly sensitive and specific circulating diagnostic markers becomes imperative to effectively identify and differentiate between cirrhosis and HCC. Accurate diagnosis is paramount in guiding appropriate therapeutic interventions. Hence, this study aimed to evaluate the potential of microRNAs (miRNAs) in discerning between HCC and LC. Methods: This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, with the protocol officially registered on PROSPERO under the reference number CRD42023417494. A thorough search across multiple databases like PubMed, Embase, Scopus, Wiley Online Library, and Science Direct was conducted to identify relevant studies published from January 1, 2018, to August 10, 2023. The included studies underwent methodological quality assessment using the Quality Assessment of Diagnostic Accuracy Studies 2 (QADAS-2) tool. The synthesis of pooled sensitivity, specificity, and other relevant diagnostic parameters employed a random-effects model and was conducted using Stata 14.0. Heterogeneity was assessed using I2 and Cochrane Q, with subsequent subgroup analysis and meta-regression performed to identify potential sources of observed heterogeneity. A sensitivity analysis was performed to assess the resilience of the findings. Furthermore, Deeks' funnel plot was employed to evaluate publication bias. Results: In this meta-analysis, we included fifteen publications, encompassing 787 HCC patients and 784 LC patients. The combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) values of miRNAs in differentiating HCC from LC were 0.84 (95% CI: 0.78-0.88), 0.79 (95% CI: 0.73-0.84), 3.9 (95% CI: 3.0-5.2), 0.21 (95% CI: 0.14-0.29), 19.44 (95% CI: 11-34), and 0.88 (95% CI: 0.85-0.91), respectively. The results of the subgroup analysis revealed that upregulated miRNA levels and miRNA assessments specifically for individuals of European descent exhibited superior diagnostic performance. Conclusion: The results of this study suggested that circulating miRNAs, especially those that are upregulated, have the potential to function as robust and promising biomarkers in the differentiation of HCC from LC. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023475954.
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Introduction: Hepatocellular carcinoma (HCC), the most common type of liver cancer, is a major global health problem, ranking as the third leading cause of cancer-related death worldwide. Early identification and diagnosis of HCC requires the discovery of reliable biomarkers. Therefore, the study aimed to assess the diagnostic accuracy of miRNAs for HCC. The protocol was registered on PROSPERO website with the registration number CRD42023417494. Method: A literature search was conducted in PubMed, Scopus, Embase, Wiley Online Library, and Science Direct databases to identify pertinent articles published between 2018 and 30 July 2023. Stata 17.0 software was employed to determine the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic ratio (DOR), and area under the curve (AUC) for evaluating the accuracy of miRNAs in diagnosing HCC. The assessment of heterogeneity among studies involved the use of the Cochran-Q test and I2 statistic tests. Due to the observed significant heterogeneity, the random-effect model was chosen. Subgroup analysis and meta-regression analysis were also undertaken to explore potential sources contributing to heterogeneity. Deeks' funnel plot was used to assess publication bias. In addition, Fagan's nomogram and likelihood ratio scattergram were utilized to assess the clinical validity of miRNAs for HCC. Result: Twenty-four articles were included, involving 1,668 individuals diagnosed with HCC and 1,236 healthy individuals. The findings revealed pooled sensitivity of 0.84 (95% CI: 0.80-0.88), specificity of 0.81 (95% CI: 0.77-0.84), PLR of 4.36 (95% CI: 3.59-5.30), NLR of 0.19 (95% CI: 0.15-0.25), DOR of 22.47 (95% CI: 14.47-32.64), and an AUC of 0.89 (95% CI: 0.86-0.91) for the diagnosis of HCC using miRNAs. Furthermore, results from the subgroup analysis demonstrated that superior diagnostic performance was observed when utilizing plasma miRNAs, a large sample size (≥100), and miRNA panels. Conclusion: Hence, circulating miRNAs demonstrate substantial diagnostic utility for HCC and can serve as effective non-invasive biomarkers for the condition. Additionally, miRNA panels, miRNAs derived from plasma, and miRNAs evaluated in larger sample sizes (≥100) demonstrate enhanced diagnostic efficacy for HCC diagnosis. Nevertheless, a large pool of prospective studies and multi-center research will be required to confirm our findings in the near future.
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BACKGROUND: Currently, the use of clinical laboratory tests is growing at a promising rate and about 80% of the clinical decisions made are based on the laboratory test results. Therefore, it is a major task to achieve quality service. This study was conducted to assess the magnitude of errors in the total testing process of Clinical Chemistry Laboratory and to evaluate analytical quality control using sigma metrics. METHODS: A cross-sectional study was conducted at Dessie Comprehensive Specialized Hospital Clinical Chemistry Laboratory, Northeast Ethiopia, from 10 February 2020 to 10 June 2020. All Clinical Chemistry Laboratory test requests with their respective samples, external quality control and all daily internal quality control data during the study period were included in the study. Data were collected using a prepared checklist and analyzed using SPSS version 21. RESULTS: A total of 4719 blood samples with their test requests were included in the study. Out of 145,383 quality indicators, an error rate of 22,301 (15.3%) was identified in the total testing process. Of the total errors, 76.3% were pre-analytical, 2.1% were analytical and 21.6% were post-analytical errors (p<0.0001). Of the total 14 analytes in the sigma metric evaluation, except ALP, all routine clinical chemistry tests were below the standard (<3). In multivariate logistic regression, the location of patients in the inpatient department was significantly associated with the specimen rejection ((AOR=1.837, 95% CI (1.288-2.618), p=0.001). CONCLUSION: The study found a higher frequency of errors in the total testing process in the Clinical Chemistry Laboratory and almost all test parameters had an unsatisfactory sigma metric value.
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BACKGROUND: Clinical laboratory testing is a highly complex process involving a different procedure. Laboratory errors may occur at any stage of the test process, but most errors occur during extra-analytical phases. The magnitude of clinical laboratory errors, in particular extra-analytical errors, was inconsistent in different studies. METHODS: A systematic review and meta-analysis were conducted based on the Preferred Reporting Items for Systematic Review and Meta-Analysis guidelines. The extracted data were entered into a Microsoft Excel spreadsheet and transferred to STATA version 11 for the analysis. Random effect model was used to estimate pooled prevalence of extra-clinical laboratory errors and I2 statistic was used to assess heterogeneity between the studies. Funnel plot analysis and Egger weighted regression test were performed to detect the publication bias. Egger weighted regression test with P-value <0.05 was considered to be a statistically significant publication bias. RESULTS: A total of 1,381 studies were searched, 19 were included in this systematic review and meta-analysis. A total of 621,507 pre-analytical and 51,859 post-analytical outcomes of quality indicators were reported. A total of 145,515 samples were assessed for rejection and 62,513 laboratory requests were evaluated for incompleteness. The pooled prevalence of pre-analytical and post-analytical errors in Africa was 17.5% (95% Cl: 11.55, 23.45) and 10.99% (95% Cl: 5.30, 16.67) respectively. The pooled prevalence of specimen rejection and laboratory request forms incompleteness in Africa was 2.0% (95% Cl: 0.86, 3.14) and 7.55% (95% Cl: 2.30, 12.80) respectively. CONCLUSION: The study found high prevalence of pre- and of post-analytical clinical laboratory errors in Africa. In addition, the study showed that the standard completion of the laboratory request forms was poor and there were significant numbers of specimen rejections. Therefore, clinical laboratories should ensure compliance with standard operating procedures, the laboratory information system, the cooperation of the entire staff and the targeted training of sample collectors.
