Publication bias in diagnostic imaging: conference abstracts with positive conclusions are more likely to be published

Objective: To evaluate whether imaging diagnostic test accuracy conference abstracts with positive conclusions or titles are more likely to reach full-text publication than those with negative (or neutral) conclusions or titles. Methods: Diagnostic accuracy research abstracts were included if they were presented at the 2011 or 2012 Radiological Society of North America conference. Full-text publication status at 5 years post conference abstract submission was determined. Conclusion and title positivity of conference abstracts were extracted, as well as potential confounding factors. The associations of conclusion and title positivity with publication status at 5 years post conference abstract submission were assessed using a multivariable logistic regression model. Conditional odds ratios were calculated to express the strength of associations, adjusting for the confounders. Results: In total, 282/400 (71%) of included conference abstracts reached full-text publication. A total of 246 out of 337 (74%) conference abstracts with positive conclusions resulted in full-text publications, compared with 26/48 (54%) with neutral conclusions and 5/15 (33%) with negative conclusions. In multivariable logistic regression, conclusion positivity was significantly associated with full-text publication (odds ratio 3.6; 95% CI 1.9–6.7 for conference abstracts with positive conclusions, compared with those with non-positive conclusions); this did not apply to title positivity (odds ratio 1.2; 95% CI 0.47–3.0). Conclusion: Imaging conference abstracts with positive conclusions were more likely to be published as full-text articles. Title positivity was not associated with publication. This preferential publication pattern may lead to an overrepresentation of positive studies in the literature. An overrepresentation of positive studies may contribute to inflated estimates of test accuracy and has the potential to adversely influence patient care. Key Points: • Imaging diagnostic test accuracy conference abstracts with positive conclusions were more likely to be reported as full-text articles than those with non-positive conclusions. • The majority (75%) of imaging diagnostic test accuracy conference abstracts with positive conclusions were published, compared with only 53% and 33% with neutral and negative conclusions, respectively. • Conclusion positivity remained associated with the full-text publication of conference abstracts when controlling for multiple potential confounding variables

Thoracic imaging tests for the diagnosis of COVID-19

Background: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. Objectives: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. Search methods: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. Selection criteria: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. Data collection and analysis: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). Main results: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. Authors' conclusions: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices