The method of network meta-analysis of diagnostic test accuracy is in the exploratory stage. We had explored and introduced several methods of network meta-analysis of diagnostic test accuracy before. Based on example, we introduce ANOVA model for performing network meta-analysis of diagnostic test accuracy step-by-step.
The PRISMA-DTA Statement is an expanded checklist of the original PRISMA, which is aimed at improving the reporting quality of the systematic review or meta-analysis of diagnostic test accuracy studies. It was published on JAMA in January 2018. This paper explained it and provided reference for improving the reporting quality of systematic review/meta-analysis of DTA for Chinese authors.
This paper introduced the preferred reporting items for journal and conference abstracts of systematic reviews and meta-analyses of diagnostic test accuracy studies (PRISMA-DTA for abstracts), which was published in BMJ in March 2021. This paper presented the 12 items of checklist, explanations, and examples of complete reporting, to help domestic researchers to report complete and informative abstracts of systematic reviews and meta-analyses of diagnostic test accuracy studies.
With the development of artificial intelligence, machine learning has been widely used in diagnosis of diseases. It is crucial to conduct diagnostic test accuracy studies and evaluate the performance of models reasonably to improve the accuracy of diagnosis. For machine learning-based diagnostic test accuracy studies, this paper introduces the principles of study design in the aspects of target conditions, selection of participants, diagnostic tests, reference standards and ethics.
Machine learning-based diagnostic tests have certain differences of measurement indicators with traditional diagnostic tests. In this paper, we elaborate the definitions, calculation methods and statistical inferences of common measurement indicators of machine learning-based diagnosis models in detail. We hope that this paper will be helpful for clinical researchers to better evaluate machine learning diagnostic models.
Previous methods of grading evidence for systematic reviews of diagnostic test accuracy have generally focused on assessing the certainty (quality) of evidence at the level of diagnostic indicators. When the question is not limited to follow the diagnostic test accuracy results themselves, the grading results may be inaccurate due to the lack of consideration of the downstream effects of the test accuracy in specific settings. To address these challenges, the GRADE working group conducted a series of studies focused on updating methods to explore or simulate important downstream effects of diagnostic test accuracy outcomes within a contextual framework. This paper aimed to introduce advances in the contextual framework of the GRADE approach to rate the certainty of evidence from systematic reviews of single diagnostic test accuracy.
ObjectiveTo systematically review the diagnostic value of optical coherence tomography angiography (OCTA) for primary open-angle glaucoma (POAG). MethodsThe CNKI, WanFang Data, VIP, CBM, PubMed, Embase, Web of Science, and Cochrane Library databases were electronically searched to collect diagnostic test on OCTA for POAG from inception to February 2024. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using Stata 15.0 software. ResultsA total of 12 diagnostic tests involving 993 subjects were included. Meta-analysis results showed that the sensitivity/specificity of OCTA for diagnosing peripapillary vessel density, retinal vessel density, and optic nerve fiber changes in patients with POAG were 0.77/0.92, 0.56/0.92, and 0.85/0.91, respectively, and the AUC of the sROC curve was 0.94, 0.92 and 0.95, respectively. ConclusionOCTA has high diagnostic accuracy for POAG. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.