Primary Diagnostic Accuracy Studies Research Articles

Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies Using AI (QUADAS-AI): Protocol for a Qualitative Study.

Quality assessment of diagnostic accuracy studies (QUADAS), and more recently QUADAS-2, were developed to aid the evaluation of methodological quality within primary diagnostic accuracy studies. However, its current form, QUADAS-2 does not address the unique considerations raised by artificial intelligence (AI)-centered diagnostic systems. The rapid progression of the AI diagnostics field mandates suitable quality assessment tools to determine the risk of bias and applicability, and subsequently evaluate translational potential for clinical practice. We aim to develop an AI-specific QUADAS (QUADAS-AI) tool that addresses the specific challenges associated with the appraisal of AI diagnostic accuracy studies. This paper describes the processes and methods that will be used to develop QUADAS-AI. The development of QUADAS-AI can be distilled into 3 broad stages. Stage 1-a project organization phase had been undertaken, during which a project team and a steering committee were established. The steering committee consists of a panel of international experts representing diverse stakeholder groups. Following this, the scope of the project was finalized. Stage 2-an item generation process will be completed following (1) a mapping review, (2) a meta-research study, (3) a scoping survey of international experts, and (4) a patient and public involvement and engagement exercise. Candidate items will then be put forward to the international Delphi panel to achieve consensus for inclusion in the revised tool. A modified Delphi consensus methodology involving multiple online rounds and a final consensus meeting will be carried out to refine the tool, following which the initial QUADAS-AI tool will be drafted. A piloting phase will be carried out to identify components that are considered to be either ambiguous or missing. Stage 3-once the steering committee has finalized the QUADAS-AI tool, specific dissemination strategies will be aimed toward academic, policy, regulatory, industry, and public stakeholders, respectively. As of July 2024, the project organization phase, as well as the mapping review and meta-research study, have been completed. We aim to complete the item generation, including the Delphi consensus, and finalize the tool by the end of 2024. Therefore, QUADAS-AI will be able to provide a consensus-derived platform upon which stakeholders may systematically appraise the methodological quality associated with AI diagnostic accuracy studies by the beginning of 2025. AI-driven systems comprise an increasingly significant proportion of research in clinical diagnostics. Through this process, QUADAS-AI will aid the evaluation of studies in this domain in order to identify bias and applicability concerns. As such, QUADAS-AI may form a key part of clinical, governmental, and regulatory evaluation frameworks for AI diagnostic systems globally. DERR1-10.2196/58202.

Diagnostic accuracy of active pulmonary tuberculosis screening during detention admission: a systematic review.

Individuals entering incarceration are at high risk for infectious diseases, other ill conditions, and risky behavior. Typically, the status of active pulmonary tuberculosis (PTB) is not known at the time of admission. Early detection and treatment are essential for effective TB control. So far, no study has compared the diagnostic accuracy of various TB screening tools in detention using a network meta-analysis (NMA). We aimed to investigate the diagnostic accuracy of active PTB screening tests upon detention admission. We searched PubMed, Global Index Medicus, the Cochrane Library electronic databases, and grey literature for publications reporting detention TB entry screening in March 2022 and January 2024. Inclusion was non-restrictive regarding time, language, location, reference standards, or screening tests. Eligible study designs comprised comparative, observational, and diagnostic studies. Publications had to report TB screening of individuals entering confinement and provide data for diagnostic accuracy calculations. The QUADAS-2 tool was designed to assess the quality of primary diagnostic accuracy studies. This systematic review was registered with PROSPERO (CRD42022307863) and conducted without external funding. We screened a total of 2,455 records. Despite extensive searching, no studies met our inclusion criteria. However, we identified evidence revealing key differences in screening algorithm application. In conclusion, more diagnostic accuracy data on TB screening algorithms for detention admission worldwide needs to be collected. We recommend that global TB initiatives set up multi-site studies to investigate the diagnostic accuracy of TB screening on admission in low- and high-prevalence criminal justice systems. Further network meta-analyses of these studies could inform policymakers and public health experts to establish or fine-tune TB control in detention settings.

Machine Learning Approaches to Intracranial Pressure Prediction in Patients with Traumatic Brain Injury: A Systematic Review

Purpose: Intracranial pressure (ICP) monitoring is a “gold standard” monitoring modality for severe traumatic brain injury (TBI) patients. The capacity to predict ICP crises could further minimise the rate of secondary brain injury and improve the outcomes of TBI patients by facilitating timely intervention prior to a potential crisis. This systematic review sought (i) to identify the most efficacious approaches to the prediction of ICP crises within TBI patients, (ii) to access the clinical suitability of existing predictive models and (iii) to suggest potential areas for future research. Methods: Peer-reviewed primary diagnostic accuracy studies, assessing the performance of ICP crisis prediction methods within TBI patients, were included. The QUADAS-2 tool was used to evaluate the quality of the studies. Results: Three optimal solutions to predicting the ICP crisis were identified: a long short-term memory (LSTM) model, a Gaussian processes (GP) approach and a logistic regression model. These approaches performed with an area under the receiver operating characteristics curve (AUC-ROC) ranging from 0.86 to 0.95. Conclusions: The review highlights the existing disparity of the definition of an ICP crisis and what prediction horizon is the most clinically relevant. Moreover, this review draws attention to the existing lack of focus on the clinical intelligibility of algorithms, the measure of how algorithms improve patient care and how algorithms may raise ethical, legal or social concerns. The review was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022314278).

A systematic review on the use of explainability in deep learning systems for computer aided diagnosis in radiology: Limited use of explainable AI?

ObjectivesThis study aims to contribute to an understanding of the explainability of computer aided diagnosis studies in radiology that use end-to-end deep learning by providing a quantitative overview of methodological choices and by discussing the implications of these choices for their explainability. MethodsA systematic review was executed using the preferred reporting items for systemic reviews and meta-analysis guidelines. Primary diagnostic test accuracy studies using end-to-end deep learning for radiology were identified from the period January 1st, 2016, to January 20th, 2021. Results were synthesized by identifying the explanation goals, measures, and explainable AI techniques. ResultsThis study identified 490 primary diagnostic test accuracy studies using end-to-end deep learning for radiology, of which 179 (37%) used explainable AI. In 147 out of 179 (82%) of studies, explainable AI was used for the goal of model visualization and inspection. Class activation mapping is the most common technique, being used in 117 out of 179 studies (65%). Only 1 study used measures to evaluate the outcome of their explainable AI. ConclusionsA considerable portion of computer aided diagnosis studies provide a form of explainability of their deep learning models for the purpose of model visualization and inspection. The techniques commonly chosen by these studies (class activation mapping, feature activation mapping and t-distributed stochastic neighbor embedding) have potential limitations. Because researchers generally do not measure the quality of their explanations, we are agnostic about how effective these explanations are at addressing the black box issues of deep learning in radiology.

Barriers to reporting guideline adherence in point-of-care ultrasound research: a cross-sectional survey of authors and journal editors

ObjectiveAlthough the literature supporting the use of point-of-care ultrasound (POCUS) continues to grow, incomplete reporting of primary diagnostic accuracy studies has previously been identified as a barrier to translating research...

Understanding diagnostic test accuracy studies and systematic reviews: A primer for medical radiation technologists

Diagnostic test accuracy studies are performed in order to determine the value of a diagnostic test. Primary studies of diagnostic accuracy describe the accuracy of a test calculated using the number of true and false positive and negative cases of a given diagnosis of interest. Systematic reviews of diagnostic test accuracy are performed to identify accuracy of a test but also to investigate where the test might sit in a diagnostic pathway or determine how different tests compare against each other. This introductory discussion paper aims to give the reader an overview of the various features in primary and secondary diagnostic test accuracy study designs for medical imaging professionals.

Blinding practices during acute point-of-care ultrasound research: the BLIND-US meta-research study

ObjectivesPoint-of-care ultrasound (POCUS) diagnostic accuracy research has significant variation in blinding practices. This study characterises the blinding practices during acute POCUS research to determine whether research methodology adequately reflects POCUS...

A description of the primary studies of diagnostic test accuracy indexed on the DiTA database

PEDro (the Physiotherapy Evidence Database) is a widely used, comprehensive, freely available, online database that indexes studies of the effectiveness of physiotherapy interventions. We have recently built another database, called DiTA, on the same platform as PEDro. DiTA provides a comprehensive index of studies of the accuracy of diagnostic tests used by physiotherapists. This study aims to describe the number and scope of such studies. A comprehensive search was conducted for studies of the accuracy of diagnostic tests. The search was conducted on the MEDLINE, EMBASE and CINAHL databases from their inceptions to November 2018. Subsequently, monthly searches have updated the database. To be included on DiTA, studies need to investigate (a) both a pathology and patients that a physiotherapist could assess in clinical practice, and (b) an index test that a physiotherapist would perform themselves rather than one which they would request. To date, the searches have yielded 44,884 titles. Screening has identified 1,419 reports that meet the inclusion criteria. The most frequently studied subdisciplines are "musculoskeletal" (1,050/1,419; 74.0%) and "cardiothoracics" (241; 17.0%); the most frequently studied categories of pathologies are joint pathologies (463; 32.6%) and nervous system pathologies (175; 12.3%); and the most frequently studied body part is the "lower leg or knee" (232; 16.3%). Most studies investigate index tests which are "physical examination" procedures (851; 60.0%); fewer investigate "questions or questionnaires" (420; 29.6%) and "health technologies" (351; 24.7%). There is a rapidly growing body of evidence on the accuracy of diagnostic tests relevant to most physiotherapy subdisciplines. While the volume of evidence is substantial, it is not yet clear how much of the evidence is of good enough quality to support clinical decision-making.

Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation

Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear. To determine the accuracy of smartphone camera applications that diagnose AF. MEDLINE and Embase were searched until January 2019 for studies that assessed the accuracy of any smartphone applications that use the smartphone's camera to measure the amplitude and frequency of the user's fingertip pulse to diagnose AF. Bivariate random-effects meta-analyses were constructed to synthesize data. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) of Diagnostic Test Accuracy Studies reporting guideline. Sensitivity and specificity were measured with bivariate random-effects meta-analysis. To simulate the use of these applications as a screening tool, the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (ie, age ≥65 years and age ≥65 years with hypertension) were modeled. Lastly, the association of methodological limitations with outcomes were analyzed with sensitivity analyses and metaregressions. A total of 10 primary diagnostic accuracy studies, with 3852 participants and 4 applications, were included. The oldest studies were published in 2016 (2 studies [20.0%]), while most studies (4 [40.0%]) were published in 2018. The applications analyzed the pulsewave signal for a mean (range) of 2 (1-5) minutes. The meta-analyzed sensitivity and specificity for all applications combined were 94.2% (95% CI, 92.2%-95.7%) and 95.8% (95% CI, 92.4%-97.7%), respectively. The PPV for smartphone camera applications detecting AF in an asymptomatic population aged 65 years and older was between 19.3% (95% CI, 19.2%-19.4%) and 37.5% (95% CI, 37.4%-37.6%), and the NPV was between 99.8% (95% CI, 99.83%-99.84%) and 99.9% (95% CI, 99.94%-99.95%). The PPV and NPV increased for individuals aged 65 years and older with hypertension (PPV, 20.5% [95% CI, 20.4%-20.6%] to 39.2% [95% CI, 39.1%-39.3%]; NPV, 99.8% [95% CI, 99.8%-99.8%] to 99.9% [95% CI, 99.9%-99.9%]). There were methodological limitations in a number of studies that did not appear to be associated with diagnostic performance, but this could not be definitively excluded given the sparsity of the data. In this study, all smartphone camera applications had relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, suggesting that using these applications in an asymptomatic population may generate a higher number of false-positive than true-positive results. Future research should address the accuracy of these applications when screening other high-risk population groups, their ability to help monitor chronic AF, and, ultimately, their associations with patient-important outcomes.

The Accuracy of Smartphone Camera Apps to Detect Atrial Fibrillation: A Systematic Review, Meta-Analysis, Meta-Regression and Modeling Study

Background: Atrial Fibrillation (AF) affects more than 6 million people in the United States (US) and greatly increases one's risk of stroke. Despite its prevalence, much AF remains undiagnosed. Smartphone applications (apps) have been proposed to aid the detection of subclinical AF and to monitor chronic AF. However, the accuracy of these apps remains unclear. We set out to determine the accuracy of smartphone camera apps to diagnose AF and compare the accuracy of apps. Methods: We searched MEDLINE and EMBASE databases and grey literature for studies that assessed the accuracy of any smartphone camera app to diagnose AF. To synthesize data, we constructed bivariate random-effects meta-analyses to determine the meta-analyzed sensitivity and specificity. Furthermore, we modeled the positive predictive value (PPV) and negative predictive value (NPV) for different population groups (age ≥65 and age ≥65 with hypertension). Lastly, we explored the effect of methodological limitations with sensitivity analyses and meta-regressions. Findings: We analyzed data from 3852 participants across 10 primary diagnostic accuracy studies, which evaluated four different apps. The apps analyzed the pulsewave signal (PPG) for an average of 2 minutes (range: 1 to 5 mins). The meta-analyzed sensitivity and specificity for all apps combined was 94·2% (92·2% to 95·7%) and 95·8% (92·4% to 97·7%) respectively. We found the PPV and NPV for the detection of AF was between 19·3% to 37·5% and 99·8% to 99·9%, respectively, in an asymptomatic population aged ≥65. The PPV increased for people aged ≥65 year old and with hypertension (PPV: 20·5% to 39·2%, NPV: 99·8% to 99·9%)). We found methodological limitations in a number of studies that did not appear to impact diagnostic performance, but an effect could not be definitively excluded given data sparsity. Interpretation: All smartphone camera apps have a relatively high sensitivity and specificity. The modeled NPV was high for all analyses, but the PPV was modest, suggesting that using these apps in an asymptomatic population may generate more false, rather than true, positive results. Future research could address the accuracy of these apps to screen further highrisk population groups, and to monitor chronic AF. Funding Statement: The lead author (JOS) is supported by an unrestricted NIH Fellowship ( T32 post-doctoral Fellowship), however there was no specific funding for this study. Declaration of Interests: The authors stated: None declared. Ethics Approval Statement: The protocol for this systematic review was developed and registered a priori (PROSPERO: CRD42019125253). This systematic review and meta-analysis were conducted in line with the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA).

Shortening self-report mental health symptom measures through optimal test assembly methods: Development and validation of the Patient Health Questionnaire-Depression-4.

The objective of this study was to develop and validate a short form of the Patient Health Questionnaire-9 (PHQ-9), a self-report questionnaire for assessing depressive symptomatology, using objective criteria. Responses on the PHQ-9 were obtained from 7,850 English-speaking participants enrolled in 20 primary diagnostic test accuracy studies. PHQ unidimensionality was verified using confirmatory factor analysis, and an item response theory model was fit. Optimal test assembly (OTA) methods identified a maximally precise short form for each possible length between one and eight items, including and excluding the ninth item. The final short form was selected based on prespecified validity, reliability, and diagnostic accuracy criteria. A four-item short form of the PHQ (PHQ-Dep-4) was selected. The PHQ-Dep-4 had a Cronbach's alpha of 0.805. Sensitivity and specificity of the PHQ-Dep-4 were 0.788 and 0.837, respectively, and were statistically equivalent to the PHQ-9 (sensitivity=0.761, specificity=0.866). The correlation of total scores with the full PHQ-9 was high (r=0.919). The PHQ-Dep-4 is a valid short form with minimal loss of information of scores when compared to the full-length PHQ-9. Although OTA methods have been used to shorten patient-reported outcome measures based on objective, prespecified criteria, further studies are required to validate this general procedure for broader use in health research. Furthermore, due to unexamined heterogeneity, there is a need to replicate the results of this study in different patient populations.

Reporting bias in imaging: higher accuracy is linked to faster publication

The objective of this study was to evaluate whether higher reported accuracy estimates are associated with shorter time to publication among imaging diagnostic accuracy studies. We included primary imaging diagnostic accuracy studies, included in meta-analyses from systematic reviews published in 2015. For each primary study, we extracted accuracy estimates, participant recruitment periods and publication dates. Our primary outcome was the association between Youden's index (sensitivity + specificity - 1, a single measure of diagnostic accuracy) and time to publication. We included 55 systematic reviews and 781 primary studies. Study completion dates were missing for 238 (30%) studies. The median time from completion to publication in the remaining 543 studies was 20 months (IQR 14-29). Youden's index was negatively correlated with time from completion to publication (rho = -0.11, p = 0.009). This association remained significant in multivariable Cox regression analyses after adjusting for seven study characteristics: hazard ratio of publication was 1.09 (95% CI 1.03-1.16, p = 0.004) per unit increase for logit-transformed estimates of Youden's index. When dichotomizing Youden's index by a median split, time from completion to publication was 20 months (IQR 13-33) for studies with a Youden's index below the median, and 19 months (14-27) for studies with a Youden's index above the median (p = 0.104). Imaging diagnostic accuracy studies with higher accuracy estimates were weakly associated with a shorter time to publication. • Higher accuracy estimates are weakly associated with shorter time to publication. • Lag in time to publication remained significant in multivariate Cox regression analyses. • No correlation between accuracy and time from submission to publication was identified.

Cerebrospinal fluid biomarkers for the diagnosis and prognosis of Parkinson’s disease: protocol for a systematic review and individual participant data meta-analysis

IntroductionIdiopathic Parkinson’s disease (PD) is a progressive neurodegenerative disorder related to α-synuclein misfolding and aggregation. For this reason, it belongs to the family of ‘synucleinopathies’, which also includes some other...

Treatment of multiple test readers in diagnostic accuracy systematic reviews-meta-analyses of imaging studies

ObjectiveTo evaluate the handling of multiple readers in imaging diagnostic accuracy systematic reviews-meta-analyses. MethodsSearch was performed for imaging diagnostic accuracy systematic reviews that performed meta-analysis from 2005–2015. Handling of multiple readers was classified as: 1) averaged; 2) ‘best’ reader; 3) ‘most experienced’ reader; 4) each reader counted individually; 5) random; 6) other; 7) not specified. Incidence and reporting of multiple reader data was assessed in primary diagnostic accuracy studies that were included in a random sample of reviews. ResultsOnly 28/296 (9.5%) meta-analyses specified how multiple readers were handled: 7/28 averaged results, 2/28 included the best reader, 14/28 treated each reader as a separate data set, 1/28 randomly selected a reader, 4/28 used other methods. Sample of 27/268 ‘not specified’ reviews generated 442 primary studies. 270/442 (61%) primary studies had multiple readers: 164/442 (37%) reported consensus reading, 87/442 (20%) reported inter-observer variability, 9/442 (2%) reported independent datasets for each reader. 26/27 (96%) meta-analyses contained at least one primary study with multiple readers. ConclusionsReporting how multiple readers were treated in imaging systematic reviews-meta-analyses is uncommon and method used varied widely. This may result from a lack of guidance, unavailability of appropriate statistical methods for handling multiple readers in meta-analysis, and sub-optimal primary study reporting.

Meta-analysis of magnetic resonance imaging accuracy for diagnosis of oral cancer.

ObjectiveTo establish the diagnostic accuracy of magnetic resonance imaging (MRI) as an auxiliary means for the diagnosis of oral cancer through a systematic review and meta-analysis.MethodsAn exhaustive search of publications from 1986 to 2016 was performed of Medline, Embase and Cochrane (and related databases), including grey literature. Primary diagnostic accuracy studies that assessed oral cancer (target condition) using MRI (index test) were included. Diagnostic threshold, sensitivity and meta-regression analyses were performed. A meta-analysis was performed using Meta-DiSc® v. 1.4 software.ResultsA total of 24 primary studies were assessed, comprising 1,403 oral cancer lesions. Nine studies used diffusion-weighted MRI, with a diagnostic odds ratio (DOR) of 30.7 (95% confidence interval [CI]: 12.7–74.3) and area under the curve (AUC) of 0.917 (95% CI: 0.915–0.918); seven studies used dynamic contrast-enhanced MRI, with a DOR of 48.1 (95%CI: 22.4–103.2) and AUC of 0.936 (95% CI: 0.934–0.937); and 13 studies used traditional MRI, with a DOR of 23.9 (95%CI: 13.2–43.3) and AUC of 0.894 (95% CI: 0.894–0.895). Meta-regression analysis indicated that the magnetic field strength may have influenced the heterogeneity of the results obtained (p = 0.0233) using traditional MRI. Sensitivity analysis revealed a discrete reduction of inconsistency in some subgroups.ConclusionThe three types of MRI assessed exhibited satisfactory accuracy compared to biopsy. Considering the relevance of early treatment and screening and that better health care results in improved survival rates and quality of life for oral cancer patients, we suggest the use of MRI as a part of the pre-treatment and monitoring protocol at public health services.

Accuracy of clinical neurological examination in diagnosing lumbo-sacral radiculopathy: a systematic literature review

BackgroundLumbar radiculopathy remains a clinical challenge among primary care clinicians in both assessment and diagnosis. This often leads to misdiagnosis and inappropriate treatment of patients resulting in poor health outcomes, exacerbating this already debilitating condition. This review evaluated 12 primary diagnostic accuracy studies that specifically assessed the performance of various individual and grouped clinical neurological tests in detecting nerve root impingement, as established in the current literature.MethodsEight electronic data bases were searched for relevant articles from inception until July 2016. All primary diagnostic studies which investigated the accuracy of clinical neurological test (s) in diagnosing lumbar radiculopathy among patients with low back and referred leg symptoms were screened for inclusion. Qualifying studies were retrieved and independently assessed for methodological quality using the ‘Quality Assessment of Diagnostic tests Accuracy Studies’ criteria.ResultsA total of 12 studies which investigated standard components of clinical neurological examination of (sensory, motor, tendon reflex and neuro-dynamics) of the lumbo-sacral spine were included. The mean inter-observer agreement on quality assessment by two independent reviewers was fair (k = 0.3 – 0.7).The diagnostic performance of sensory testing using MR imaging as a reference standard demonstrated a sensitivity (confidence interval 95%) 0.61 (0.47-0.73) and a specificity of 0.63 (0.38-0.84). Motor tests sensitivity was poor to moderate, ranging from 0.13 (0.04-0.31) to 0.61 (0.36-0.83). Generally, the diagnostic performance of reflex testing was notably good with specificity ranging from (confidence interval 95%) 0.60 (0.51-0.69) to 0.93 (0.87-0.97) and sensitivity ranging from 0.14 (0.09-0.21) to 0.67 (0.21-0.94). Femoral nerve stretch test had a high sensitivity of (confidence interval 95%) 1.00 (0.40-1.00) and specificity of 0.83 (0.52-0.98) while SLR test recorded a mean sensitivity of 0.84 (0.72-0.92) and specificity of 0.78 (0.67-0.87).ConclusionsThere is a scarcity of studies on the diagnostic accuracy of clinical neurological examination testing. Furthermore there seem to be a disconnect among researchers regarding the diagnostic utility of lower limb neuro-dynamic tests which include the Straight Leg Raise and Femoral Nerve tests for sciatic and femoral nerve respectively. Whether these tests are able to detect the presence of disc herniation and subsequent nerve root compression or hyper-sensitivity of the sacral and femoral plexus due to mechanical irritation still remains debatable.

Accuracy of magnetic resonance imaging in detecting lumbo-sacral nerve root compromise: a systematic literature review.

BackgroundMRI is considered to be the diagnostic tool of choice in diagnosing nerve root compromise among patients presenting with clinical suspicion of lumbo-sacral radiculopathy. There exists controversy among researchers and clinicians regarding the diagnostic utility and accuracy of MRI in detecting nerve root compromise and radiculopathy. This review evaluated 4 primary diagnostic accuracy studies that specifically assessed the accuracy of MRI in detecting nerve root compromise, as established in the current literature.MethodsEight electronic data bases were searched for relevant articles from inception until January 2014. All primary diagnostic studies which investigated the accuracy of MRI in diagnosing nerve root compromise among patients with low back and referred leg symptoms were screened for inclusion. Qualifying studies were retrieved and independently assessed for methodological quality using the ‘Quality Assessment of Diagnostic tests Accuracy Studies’ criteria.ResultsFour studies qualified for inclusion in this review. The sensitivity of MRI in detecting lumbar nerve root compromise was very low at 0.25 (95 % CI) while the specificity was relatively high at 0.92 (95 % CI).ConclusionsThere is lack of sufficient high quality scientific evidence in support or against the use of MRI in diagnosing nerve root compression and radiculopathy. Therefore, clinicians should always correlate the findings of MRI with the patients’ medical history and clinical presentation in clinical decision making.

Time to publication among completed diagnostic accuracy studies: associated with reported accuracy estimates

BackgroundPrevious evaluations have documented that studies evaluating the effectiveness of therapeutic interventions are not always reported, and that those with statistically significant results are published more rapidly than those without. This can lead to reporting bias in systematic reviews and other literature syntheses. We evaluated whether diagnostic accuracy studies that report promising results about the performance of medical tests are also published more rapidly.MethodsWe obtained all primary diagnostic accuracy studies included in meta-analyses of Medline-indexed systematic reviews that were published between September 2011 and January 2012. For each primary study, we extracted estimates of diagnostic accuracy (sensitivity, specificity, Youden’s index), the completion date of participant recruitment, and the publication date. We calculated the time from completion to publication and assessed associations with reported accuracy estimates.ResultsForty-nine systematic reviews were identified, containing 92 meta-analyses and 924 unique primary studies, of which 756 could be included. Study completion dates were missing for 285 (38 %) of these. Median time from completion to publication in the remaining 471 studies was 24 months (IQR 16 to 35). Primary studies that reported higher estimates of sensitivity (Spearman’s rho = −0.14; p = 0.003), specificity (rho = −0.17; p < 0.001), and Youden’s index (rho = −0.22; p < 0.001) had significantly shorter times to publication. When comparing time to publication in studies reporting accuracy estimates above versus below the median, the median number of months was 23 versus 25 for sensitivity (p = 0.046), 22 versus 27 for specificity (p = 0.001), and 22 versus 27 for Youden’s index (p < 0.001). These differential time lags remained significant in multivariable Cox regression analyses with adjustment for other study characteristics, with hazard ratios of publication of 1.06 (95 % CI 1.02 to 1.11; p = 0.007) for logit-transformed estimates of sensitivity, 1.09 (95 % CI 1.04 to 1.14; p < 0.001) for logit-transformed estimates of specificity, and 1.09 (95 % CI 1.03 to 1.14; p = 0.001) for logit-transformed estimates of Youden’s index.ConclusionsTime to publication was significantly shorter for studies reporting higher estimates of diagnostic accuracy compared to those reporting lower estimates. This suggests that searching and analyzing the published literature, rather than all completed studies, can produce a biased view of the performance of medical tests.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-016-0177-4) contains supplementary material, which is available to authorized users.

Diagnostic utility of CSF α-synuclein species in Parkinson's disease: protocol for a systematic review and meta-analysis

IntroductionThe diagnostic criteria currently used for Parkinson's disease (PD) are mainly based on clinical motor symptoms. For these reasons many biomarkers are under investigation to support the diagnosis at the...

Methodological quality of meta-analyses of the diagnostic accuracy of depression screening tools

ObjectiveConcerns have been raised that primary studies of diagnostic accuracy of depression screening tools may exaggerate estimates of accuracy and that this could also influence the results of meta-analyses. No studies, however, have evaluated the quality of meta-analyses of depression screening tools. Our objective was to evaluate the quality of meta-analyses of the diagnostic accuracy of depression screening tools. MethodsWe searched MEDLINE and PsycINFO from January 1, 2005 through March 13, 2016 for recent meta-analyses in any language on the diagnostic accuracy of depression screening tools. Two reviewers independently assessed methodological quality using the AMSTAR tool with appropriate adaptations made for studies of diagnostic test accuracy. ResultsWe identified 21 eligible meta-analyses. The majority provided a list of included studies (100%), included a comprehensive literature search (95%) and assessed risk of bias of included studies (71%). Meta-analyses less consistently included non-published evidence (38%), listed excluded studies (33%), incorporated risk of bias findings into conclusions (33%), and assessed selective cutoff reporting (29%). Meta-analyses rarely reported that duplicate study selection or data extraction occurred (14%), mentioned ‘a priori’ protocols (10%), or reported on conflicts of interest (0%) or funding sources (0%) of primary studies. Only 6 of 21 included meta-analyses complied with at least 7 of 14 adapted AMSTAR items. ConclusionsThe methodological quality of most meta-analyses of the diagnostic test accuracy of depression screening tools is suboptimal. Improving quality will reduce the risk of inaccurate estimates of accuracy and inappropriate inferences.