The quality of diagnostic accuracy studies since the STARD statement - Has it improved?

OBJECTIVE: To assess whether the quality of reporting of diagnostic accuracy studies has improved since the publication of the Standards for the Reporting of Diagnostic Accuracy studies (STARD statement). METHODS: The quality of reporting of diagnostic accuracy studies published in 12 medical journals in 2000 (pre-STARD) and 2004 (post-STARD) was evaluated by two reviewers independently. For each article, the number of reported STARD items was counted (range 0 to 25). Differences in completeness of reporting between articles published in 2000 and 2004 were analyzed, using multilevel analyses. RESULTS: We included 124 articles published in 2000 and 141 articles published in 2004. Mean number of reported STARD items was 11.9 (range 3.5 to 19.5) in 2000 and 13.6 (range 4.0 to 21.0) in 2004, an increase of 1.81 items (95% CI: 0.61 to 3.01). Articles published in 2004 reported the following significantly more often: methods for calculating test reproducibility of the index test (16% vs 35%); distribution of the severity of disease and other diagnoses (23% vs 53%); estimates of variability of diagnostic accuracy between subgroups (39% vs 60%); and a flow diagram (2% vs 12%). CONCLUSIONS: The quality of reporting of diagnostic accuracy studies has improved slightly over time, without a more pronounced effect in journals that adopted the STARD statement. As there is still room for improvement, editors should mention the use of the STARD statement as a requirement in their guidelines for authors, and instruct reviewers to check the STARD items. Authors should include a flow diagram in their manuscrip

Current methods for development of rapid reviews about diagnostic tests: an international survey

Background Rapid reviews (RRs) have emerged as an efficient alternative to time-consuming systematic reviews—they can help meet the demand for accelerated evidence synthesis to inform decision-making in healthcare. The synthesis of diagnostic evidence has important methodological challenges. Here, we performed an international survey to identify the current practice of producing RRs for diagnostic tests. Methods We developed and administered an online survey inviting institutions that perform RRs of diagnostic tests from all over the world. Results All participants (N = 25) reported the implementation of one or more methods to define the scope of the RR; however, only one strategy (defining a structured question) was used by ≥90% of participants. All participants used at least one methodological shortcut including the use of a previous review as a starting point (92%) and the use of limits on the search (96%). Parallelization and automation of review tasks were not extensively used (48 and 20%, respectively). Conclusion Our survey indicates a greater use of shortcuts and limits for conducting diagnostic test RRs versus the results of a recent scoping review analyzing published RRs. Several shortcuts are used without knowing how their implementation affects the results of the evidence synthesis in the setting of diagnostic test reviews. Thus, a structured evaluation of the challenges and implications of the adoption of these RR methods is warranted

Challenges of rapid reviews for diagnostic test accuracy questions: a protocol for an international survey and expert consultation

Background: Assessment of diagnostic tests, broadly defined as any element that aids in the collection of additional information for further clarification of a patient’s health status, has increasingly become a critical issue in health policy and decision-making. Diagnostic evidence, including the accuracy of a medical test for a target condition, is commonly appraised using standard systematic review methodology. Owing to the considerable time and resources required to conduct these, rapid reviews have emerged as a pragmatic alternative by tailoring methods according to the decision maker’s circumstances. However, it is not known if streamlining methodological aspects has an impact on the validity of evidence synthesis. Furthermore, due to the particular nature and complexity of the appraisal of diagnostic accuracy, there is need for detailed guidance on how to conduct rapid reviews of diagnostic tests. In this study, we aim to identify the methods currently used by rapid review developers to synthesize evidence on diagnostic test accuracy, as well as to analyze potential shortcomings and challenges related to these methods. Methods: We will carry out a two-fold approach: (1) an international survey of professionals working in organizations that develop rapid reviews of diagnostic tests, in terms of the methods and resources used by these agencies when conducting rapid reviews, and (2) semi-structured interviews with senior-level individuals to further explore and validate the findings from the survey and to identify challenges in conducting rapid reviews. We will use STATA 15.0 for quantitative analyses and framework analysis for qualitative analyses. We will ensure protection of data during all stages. Discussion: The main result of this research will be a map of methods and resources currently used for conducting rapid reviews of diagnostic test accuracy, as well as methodological shortcomings and potential solutions in diagnostic knowledge synthesis that require further research

A non-randomized risk-adjusted comparison of lenalidomide plus R-CHOP versus R-CHOP for MYC-rearranged DLBCL patients

Patients with MYC rearranged (MYC-R) diffuse large B-cell lymphoma (DLBCL) have a poor prognosis. Previously, we demonstrated in a single-arm phase II trial (HOVON-130) that addition of lenalidomide to R-CHOP (R2CHOP) is well-tolerated and yields similar complete metabolic remission rates as more intensive chemotherapy regimens in literature. In parallel with this single-arm interventional trial, a prospective observational screening cohort (HOVON-900) was open in which we identified all newly diagnosed MYC-R DLBCL patients in the Netherlands. Eligible patients from the observational cohort that were not included in the interventional trial served as control group in the present risk-adjusted comparison. R2CHOP treated patients from the interventional trial (n = 77) were younger than patients in the R-CHOP control cohort (n = 56) (median age 63 versus 70 years, p = 0.018) and they were more likely to have a lower WHO performance score (p = 0.013). We adjusted for differences at baseline using 1:1 matching, multivariable analysis, and weighting using the propensity score to reduce treatment-selection bias. These analyses consistently showed improved outcome after R2CHOP with HRs of 0.53, 0.51, and 0.59, respectively, for OS, and 0.53, 0.59, and 0.60 for PFS. Thus, this non-randomized risk-adjusted comparison supports R2CHOP as an additional treatment option for MYCR DLBCL patients.Immunobiology of allogeneic stem cell transplantation and immunotherapy of hematological disease

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings