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Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies., (© 2015 American Association for Clinical Chemistry.)

Like any other medical technology or intervention, diagnostic tests should be thoroughly evaluated before their introduction into daily practice. Increasingly, decision makers, physicians, and other users of diagnostic tests request more than simple measures of a test's analytical or technical performance and diagnostic accuracy; they would also like to see testing lead to health benefits. In this last article of our series, we introduce the notion of clinical utility, which expresses--preferably in a quantitative form--to what extent diagnostic testing improves health outcomes relative to the current best alternative, which could be some other form of testing or no testing at all. In most cases, diagnostic tests improve patient outcomes by providing information that can be used to identify patients who will benefit from helpful downstream management actions, such as effective treatment in individuals with positive test results and no treatment for those with negative results. We describe how comparative randomized clinical trials can be used to estimate clinical utility. We contrast the definition of clinical utility with that of the personal utility of tests and markers. We show how diagnostic accuracy can be linked to clinical utility through an appropriate definition of the target condition in diagnostic-accuracy studies., (© 2012 American Association for Clinical Chemistry)

Systematic reviews of diagnostic accuracy studies allow calculation of pooled estimates of accuracy with increased precision and examination of differences in accuracy between tests or subgroups of studies. Recently, several advances have been made in the methods used in performing systematic reviews of diagnostic test accuracy studies, most notably in how to assess the methodological quality of primary diagnostic test accuracy studies by use of QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) instrument and how to develop sound statistical models for metaanalysis of the paired measures of test accuracy (bivariate metaregression model of sensitivity and specificity). This article provides an overview of the different steps within a diagnostic systematic review and highlights these advances, illustrated with empirical data. The potential benefits of some recent developments in the areas of network metaanalysis and individual patient data metaanalysis for diagnostic tests are also discussed., (© 2012 American Association for Clinical Chemistry)

In practice, the diagnostic workup usually starts with a patient with particular symptoms or signs, who is suspected of having a particular target disease. In a sequence of steps, an array of diagnostic information is commonly documented. The diagnostic information conveyed by different results from patient history, physical examination, and subsequent testing is to varying extents overlapping and thus mutually dependent. This implies that the diagnostic potential of a test or biomarker is conditional on the information obtained from previous tests. A key question about the accuracy of a diagnostic test/biomarker is whether that test improves the diagnostic workup beyond already available diagnostic test results. This second report in a series of 4 gives an overview of several methods to quantify the added value of a new diagnostic test or biomarker, including the area under the ROC curve, net reclassification improvement, integrated discrimination improvement, predictiveness curve, and decision curve analysis. Each of these methods is illustrated with the use of empirical data. We reiterate that reporting on the relative increase in discrimination and disease classification is relevant to obtain insight into the incremental value of a diagnostic test or biomarker. We also recommend the use of decision-analytic measures to express the accuracy of an entire diagnostic workup in an informative way., (© 2012 American Association for Clinical Chemistry)

Background: In recent years, increasing focus has been directed to the methodology for evaluating (new) tests or biomarkers. A key step in the evaluation of a diagnostic test is the investigation into its accuracy., Content: We reviewed the literature on how to assess the accuracy of diagnostic tests. Accuracy refers to the amount of agreement between the results of the test under evaluation (index test) and the results of a reference standard or test. The generally recommended approach is to use a prospective cohort design in patients who are suspected of having the disease of interest, in which each individual undergoes the index and same reference standard tests. This approach presents several challenges, including the problems that can arise with the verification of the index test results by the preferred reference standard test, the choice of cutoff value in case of a continuous index test result, and the determination of how to translate accuracy results to recommendations for clinical use. This first in a series of 4 reports presents an overview of the designs of single-test accuracy studies and the concepts of specificity, sensitivity, posterior probabilities (i.e., predictive values) for the presence of target disease, ROC curves, and likelihood ratios, all illustrated with empirical data from a study on the diagnosis of suspected deep venous thrombosis. Limitations of the concept of the diagnostic accuracy for a single test are also highlighted., Conclusions: The prospective cohort design in patients suspected of having the disease of interest is the optimal approach to estimate the accuracy of a diagnostic test. However, the accuracy of a diagnostic index test is not constant but varies across different clinical contexts, disease spectrums, and even patient subgroups.

Background: Optimal cutoff values for tests results involving continuous variables are often derived in a data-driven way. This approach, however, may lead to overly optimistic measures of diagnostic accuracy. We evaluated the magnitude of the bias in sensitivity and specificity associated with data-driven selection of cutoff values and examined potential solutions to reduce this bias., Methods: Different sample sizes, distributions, and prevalences were used in a simulation study. We compared data-driven estimates of accuracy based on the Youden index with the true values and calculated the median bias. Three alternative approaches (assuming a specific distribution, leave-one-out, smoothed ROC curve) were examined for their ability to reduce this bias., Results: The magnitude of bias caused by data-driven optimization of cutoff values was inversely related to sample size. If the true values for sensitivity and specificity are both 84%, the estimates in studies with a sample size of 40 will be approximately 90%. If the sample size increases to 200, the estimates will be 86%. The distribution of the test results had little impact on the amount of bias when sample size was held constant. More robust methods of optimizing cutoff values were less prone to bias, but the performance deteriorated if the underlying assumptions were not met., Conclusions: Data-driven selection of the optimal cutoff value can lead to overly optimistic estimates of sensitivity and specificity, especially in small studies. Alternative methods can reduce this bias, but finding robust estimates for cutoff values and accuracy requires considerable sample sizes.

Background: In some diagnostic accuracy studies, the test results of a series of patients with an established diagnosis are compared with those of a control group. Such case-control designs are intuitively appealing, but they have also been criticized for leading to inflated estimates of accuracy., Methods: We discuss similarities and differences between diagnostic and etiologic case-control studies, as well as the mechanisms that can lead to variation in estimates of diagnostic accuracy in studies with separate sampling schemes ("gates") for diseased (cases) and nondiseased individuals (controls)., Results: Diagnostic accuracy studies are cross-sectional and descriptive in nature. Etiologic case-control studies aim to quantify the effect of potential causal exposures on disease occurrence, which inherently involves a time window between exposure and disease occurrence. Researchers and readers should be aware of spectrum effects in diagnostic case-control studies as a result of the restricted sampling of cases and/or controls, which can lead to changes in estimates of diagnostic accuracy. These spectrum effects may be advantageous in the early investigation of a new diagnostic test, but for an overall evaluation of the clinical performance of a test, case-control studies should closely mimic cross-sectional diagnostic studies., Conclusions: As the accuracy of a test is likely to vary across subgroups of patients, researchers and clinicians might carefully consider the potential for spectrum effects in all designs and analyses, particularly in diagnostic accuracy studies with differential sampling schemes for diseased (cases) and nondiseased individuals (controls).

Background: Carpal tunnel syndrome (CTS) is known as a repetitive motion disorder, but the role of other diseases in the development or prognosis of CTS is uncertain. We reviewed the literature to determine whether there is evidence for an increased prevalence of specific conditions in CTS patients and whether this evidence would support laboratory screening for these conditions., Methods: Medline, Embase, and Cochrane Controlled Trial Register were searched for key words related to CTS and associated diseases. Relevant articles were selected according to specific criteria. Sources of bias and heterogeneity attributable to differences in study design and in patient selection were investigated by subgroup analysis., Results: After an initial search, we limited ourselves to three potentially important conditions: diabetes mellitus (DM), hypothyroidism (HT), and rheumatoid arthritis (RA). We identified nine articles with a total of 4908 CTS patients and 7671 controls that met our selection criteria. The nine studies were heterogeneous with respect to clinical and methodologic factors. In general, the prevalence of concurrent diseases was higher in CTS patients than in controls: the pooled odds ratios were 2.2 (95% confidence interval, 1.5-3.1) for DM, 1.4 (1.0-2.0) for HT, and 2.2 (1.4-3.4) for RA. Studies of lower methodologic quality reported, on average, higher odds ratios. Only one study provided information about whether the diagnosis of the concurrent condition was already made at the time of the CTS diagnosis., Conclusions: We found evidence that the prevalences of DM, HT, and RA are higher in CTS patients, but only one study specifically addressed the issue of the prevalence of nonmanifest cases of the concurrent condition. At present, there is insufficient evidence for routine laboratory screening for concurrent conditions in all newly diagnosed CTS patients.

The quality of reporting of studies of diagnostic accuracy is less than optimal. Complete and accurate reporting is necessary to enable readers to assess the potential for bias in the study and to evaluate the generalisability of the results. A group of scientists and editors has developed the STARD (Standards for Reporting of Diagnostic Accuracy) statement to improve the reporting the quality of reporting of studies of diagnostic accuracy. The statement consists of a checklist of 25 items and flow diagram that authors can use to ensure that all relevant information is present. This explanatory document aims to facilitate the use, understanding and dissemination of the checklist. The document contains a clarification of the meaning, rationale and optimal use of each item on the checklist, as well as a short summary of the available evidence on bias and applicability. The STARD statement, checklist, flowchart and this explanation and elaboration document should be useful resources to improve reporting of diagnostic accuracy studies. Complete and informative reporting can only lead to better decisions in healthcare.

Background: To comprehend the results of diagnostic accuracy studies, readers must understand the design, conduct, analysis, and results of such studies. That goal can be achieved only through complete transparency from authors., Objective: To improve the accuracy and completeness of reporting of studies of diagnostic accuracy to allow readers to assess the potential for bias in the study and to evaluate its generalisability., Methods: The Standards for Reporting of Diagnostic Accuracy (STARD) steering committee searched the literature to identify publications on the appropriate conduct and reporting of diagnostic studies and extracted potential items into an extensive list. Researchers, editors, and members of professional organisations shortened this list during a two-day consensus meeting with the goal of developing a checklist and a generic flow diagram for studies of diagnostic accuracy., Results: The search for published guidelines on diagnostic research yielded 33 previously published checklists, from which we extracted a list of 75 potential items. The consensus meeting shortened the list to 25 items, using evidence on bias whenever available. A prototypical flow diagram provides information about the method of patient recruitment, the order of test execution and the numbers of patients undergoing the test under evaluation, the reference standard or both., Conclusions: Evaluation of research depends on complete and accurate reporting. If medical journals adopt the checklist and the flow diagram, the quality of reporting of studies of diagnostic accuracy should improve to the advantage of clinicians, researchers, reviewers, journals, and the public.