An analysis reveals differences between pragmatic and explanatory diagnostic accuracy studies

Objectives: The objective of this study was to clarify a difference between two approaches while evaluating the diagnostic accuracy of medical tests, labeled here as “pragmatic” vs. “explanatory” studies. Methods: Using the definitions and characteristics described by Schwartz and Lellouch for randomized trials of interventions, and Schwartz' more general distinction between a pragmatic and an explanatory approach in medical research, we define a similar continuum for diagnostic accuracy studies. Explanatory studies aim to better understand the behavior of a test; pragmatic ones are done to support recommendations or decisions about using the test in clinical practice. Results: Pragmatic test accuracy studies differ from explanatory test accuracy studies in several ways. The difference in aims has implications for key elements of study design, such as the study eligibility criteria, the recruitment of patients, the reference standard, and the choice of the statistical analysis. Explanatory accuracy studies are often designed to test a hypothesis. They are typically selective in recruitment, may include “healthy controls,” with a small sample size, often recruited at a single center. They ignore testing failures in the analysis and more often present their results as ROC curves. By contrast, pragmatic studies are designed to guide decision making. They ideally will recruit a single, large, and representative group of patients at multiple sites and will more often present their results as estimates of sensitivity and specificity or predictive values at a prespecified threshold. Conclusion: Distinguishing between a pragmatic and an explanatory approach can help in the design, analysis, and interpretation of diagnostic accuracy studies. It can clarify debates about the appropriateness of design features to the study purpose and about the validity and applicability of study findings

COVID-19–Related Fatalities and Intensive-Care-Unit Admissions by Age Groups in Europe: A Meta-Analysis

Objectives: Precise international estimates of the age breakdown of COVID-19–related deaths and intensive-care-unit (ICU) admissions are lacking. We evaluated the distribution of COVID-19–related fatalities and ICU admissions by age groups in Europe. Materials and methods: On April 6, 2020, we systematically reviewed official COVID-19–related data from 32 European countries. We included countries that provided data regarding more than 10 COVID-19–related deaths stratified by age according to pre-specified age groups (i.e., <40, 40–69, ≥70 years). We used random-effects meta-analysis to summarize the data. Results: Thirteen European countries were included in the review, for a total of 31,864 COVID-19–related deaths (range: 27–14,381 per country). In the main meta-analysis (including data from Germany, Hungary, Italy, The Netherlands, Portugal, Spain, Switzerland; 21,522 COVID-19–related fatalities), the summary proportions of individuals <40, 40–69, and ≥70 years old among all COVID-19–related deaths were 0.1% (0.0–0.2; I 2 28.6%), 13.0% (10.8–15.4; I 2 91.5%), and 86.6% (84.2–88.9; I 2 91.5%), respectively. ICU data were available for four countries (France, Greece, Spain, Sweden). The summary proportions of individuals around <40–50, around 40–69, and around ≥60–70 years old among all COVID-19–related ICU admissions were 5.4% (3.4–7.8; I 2 89.0%), 52.6% (41.8–63.3; I 2 98.1%), and 41.8% (32.0–51.9; I 2 99%), respectively. Conclusions: People under 40 years old represent a small fraction of most severe COVID-19 cases in Europe. These results may help health authorities respond to public concerns and guide future physical distancing and mitigation strategies. Specific measures to protect older people should be considered

Searching practices and inclusion of unpublished studies in systematic reviews of diagnostic accuracy

Introduction: Many diagnostic accuracy studies are never reported in full in a peer-reviewed journal. Searching for unpublished studies may avoid bias due to selective publication, enrich the power of systematic reviews, and thereby help to reduce research waste. We assessed searching practices among recent systematic reviews of diagnostic accuracy. Methods: We extracted data from 100 non-Cochrane systematic reviews of diagnostic accuracy indexed in MEDLINE and published between October 2017 and January 2018 and from all 100 Cochrane systematic reviews of diagnostic accuracy published by December 2018, irrespective of whether meta-analysis had been performed. Results: Non-Cochrane and Cochrane reviews searched a median of 4 (IQR 3-5) and 6 (IQR 5-9) databases, respectively; most often MEDLINE/PubMed (n = 100 and n = 100) and EMBASE (n = 81 and n = 100). Additional efforts to identify studies beyond searching bibliographic databases were performed in 76 and 98 reviews, most often through screening reference lists (n = 71 and n = 96), review/guideline articles (n = 18 and n = 52), or citing articles (n = 3 and n = 42). Specific sources of unpublished studies were searched in 22 and 68 reviews, for example, conference proceedings (n = 4 and n = 18), databases only containing conference abstracts (n = 2 and n = 33), or trial registries (n = 12 and n = 39). At least one unpublished study was included in 17 and 23 reviews. Overall, 39 of 2082 studies (1.9%) included in non-Cochrane reviews were unpublished, and 64 of 2780 studies (2.3%) in Cochrane reviews, most often conference abstracts (97/103). Conclusion: Searching practices vary considerably across systematic reviews of diagnostic accuracy. Unpublished studies are a minimal fraction of the evidence included in recent reviews

Add-on bone scintigraphy after negative radiological skeletal survey for the diagnosis of skeletal injury in children suspected of physical abuse: a systematic review and meta-analysis

Objective(s) To systematically assess the extent to which bone scintigraphy (BS) could improve the detection rate of skeletal injury in children suspected of physical abuse with an initial negative radiological skeletal survey (RSS). Study design We searched MEDLINE and Web of Science for series of ≥20 children suspected of physical abuse who underwent RSS and add-on BS. We assessed the risk of bias and the heterogeneity and performed random-effects meta-analyses. Results After screening 1140 unique search results, we reviewed 51 full-text articles, and included 7 studies (783 children, mostly ≤3 years old). All studies were of either high or unclear risk of bias. Substantial heterogeneity was observed in meta-analyses. The summary detection rate of skeletal injury with RSS alone was 52% (95% CI 37 to 68). The summary absolute increase in detection rate with add-on BS was 10 percentage points (95% CI 6 to 15); the summary relative detection rate was 1.19 (95% CI 1.13 to 1.25); the summary number of children with a negative RSS who needed to undergo a BS to detect one additional child with skeletal injury (number needed to test) was 3 (95% CI 2 to 7). Conclusions From the available evidence, add-on BS in young children suspected of physical abuse with a negative RSS might allow for a clinically significant improvement of the detection rate of children with skeletal injury, for a limited number of BS procedures required. The quality of the reviewed evidence was low, pointing to the need for high-quality studies in this field

Stard 2015 guidelines for reporting diagnostic accuracy studies: Explanation and elaboration. translation to russian

Diagnostic accuracy studies are, like other clinical studies, at risk of bias due to shortcomings in design and conduct, and the results of a diagnostic accuracy study may not apply to other patient groups and settings. Readers of study reports need to be informed about study design and conduct, in sufficient detail to judge the trustworthiness and applicability of the study findings. The STARD statement (Standards for Reporting of Diagnostic Accuracy Studies) was developed to improve the completeness and transparency of reports of diagnostic accuracy studies. STARD contains a list of essential items that can be used as a checklist, by authors, reviewers and other readers, to ensure that a report of a diagnostic accuracy study contains the necessary information. STARD was recently updated. All updated STARD materials, including the checklist, are available at http://www.equator-network.org/reporting-guidelines/stard. Here, we present the STARD 2015 explanation and elaboration document. Through commented examples of appropriate reporting, we clarify the rationale for each of the 30 items on the STARD 2015 checklist, and describe what is expected from authors in developing sufficiently informative study reports. This article is the reprint with Russian translation edited by Dr. Ruslan Saygitov. The original that can be observed here: Cohen JF, Korevaar DA, Altman DG, et al. STARD 2015 guidelines for reporting diagnostic accuracy studies: Explanation and elaboration. BMJ Open 2016;6:e012799. doi: 10.1136/bmjopen-2016-012799

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