key: cord-030191-tekgcthp
authors: Suchá, Dominika; van Hamersvelt, Robbert W.; van den Hoven, Andor F.; de Jong, Pim A.; Verkooijen, Helena M.
title: Suboptimal Quality and High Risk of Bias in Diagnostic Test Accuracy Studies on Chest Radiography and Computed Tomography in the Acute Setting of the COVID-19 Pandemic: A Systematic Review
date: 2020-07-30
journal: Radiol Cardiothorac Imaging
DOI: 10.1148/ryct.2020200342
sha: 
doc_id: 30191
cord_uid: tekgcthp

PURPOSE: Chest imaging techniques have been implemented for screening and diagnosis of COVID-19 patients, based on experience with other viral pneumonias and a handful of COVID-19 diagnostic test accuracy (DTA) studies. We performed a systematic review to synthesize the literature on DTA of chest radiography (CXR), computed tomography (CT) and ultrasound for diagnosis of COVID-19 in suspected patients in hospital setting and evaluated the extent of suboptimal reporting and risk-of-bias. METHODS: A systematic search was performed (April 26, 2020) in Embase, Pubmed and Cochrane to identify CXR, CT or ultrasound studies in adult patients with suspected COVID-19, using RT-PCR or clinical consensus as reference standard. 2x2 contingency tables were reconstructed and test sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) re-calculated. Reporting quality was evaluated by adherence to STARD and risk-of-bias by QUADAS-2. RESULTS: Thirteen studies were eligible (CT=12, CXR=1, US=0). Re-calculated CT sensitivity and specificity ranged between 0.57-0.97 and 0.37-0.94, respectively, PPV and NPV between 0.59-0.92 and 0.57-0.96, respectively. On average studies complied with only 35% of the STARD-guideline items. No study scored low risk-of-bias for all QUADAS-2 domains (patient selection, index test, reference test, flow and timing). High risk-of-bias in ≥one domain was scored in 10/13 studies (77%). CONCLUSION: Reported CT test accuracy for COVID-19 diagnosis varies substantially. Validity and generalizability of these findings is complicated by poor adherence to reporting guidelines and high risk-of-bias, which are most likely due to the need for urgent publication of findings in the first months of the COVID-19 pandemic.

The protocol of this systematic review and meta-analysis was prospectively published and registered online (PROSPERO, registration number CRD42020177432). This study was conducted according to the preferred reporting items for systematic reviews and meta-analyses diagnostic test accuracy guidelines (PRISMA-DTA) (10) (11) (12) .

We included papers meeting the following criteria: (1) Adults with suspected COVID-19 pneumonia presenting in a hospital setting, including emergency departments, (2) patients undergoing chest imaging including ultrasound, CXR and/or CT for diagnosis of COVID-19 infection, (3) COVID-19 diagnosis confirmed or ruled out by reference test (i.e. RT-PCR or clinical consensus). We included papers reporting on diagnostic test accuracy measures including sensitivity, specificity, positive and negative predictive value (PPV and NPV, respectively), and/or area under the receiver-operating characteristic curve (AUC of ROC) analysis. Parameters beyond test accuracy were beyond the scope of this study. A second aim of this study was to assess risk-of-bias and quality and completeness of reporting, and their potential implications for patient care and treatment decisions.

The online libraries of PubMed, Embase and Cochrane were systematically searched on March 30 and updated on April 26, 2020 using synonyms for COVID-19, CXR, CT, ultrasound and imaging (Appendix-1). No limitations were applied to the search strategy. The online version of the journal 'Radiology: Cardiothoracic Imaging' was separately searched for relevant studies, as this novel journal is not yet indexed by MEDLINE (PubMed) or Embase. Preprint articles were not included.

Titles and abstracts were screened based on predefined criteria by two reviewers (D.S. and R. . We excluded studies: (1) including only confirmed COVID-19 subjects, (2) including only children, (3) focusing only on artificial intelligence algorithms, (4) on animals (5) in non-English I n p r e s s language, (6) those that did not allow for reconstruction of a (partial) 2x2 contingency table (7) case reports (N<10), reviews, conference proceedings and letters. Cross-referencing was performed.

Considering the urgent nature, corresponding authors were not contacted to retrieve missing (outcome) data. A list of excluded studies is presented in Appendix-2.

We extracted data on study design, study subject identification (number of subjects identified, For each study, data was extracted by two researchers individually and crosschecked by a third researcher (D.S., R.H. and A.H.), except for results on index test and reference test, which were obtained by three researchers individually. Discordances were resolved by consensus.

For each study we assessed whether the author reported the purpose of imaging (screening, risk assessment, diagnosis, prognosis, staging, monitoring or surveillance) and role of the imaging test (replacement, triage, add-on, parallel or combined testing) (13) .

Risk-of-bias and applicability was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS)-2 tool 7 based on 14 signaling questions including four risk-of-bias domains (patient selection, index test, reference test, flow and timing) and three applicability concern domains (patient selection, index test, reference test)(Appendix-3). Applicability concerns the degree to which included patients and study setting (domain patient selection), the type of index test used, its conduct and interpretation (domain index test), as well as the target condition as defined by the reference standard (domain reference standard) match the review question. Blinding for index test result was considered not relevant in the assessment of reference test (RT-PCR) riskof-bias, because this quantitative semi-automated method is unlikely to be affected. Risk-of-bias regarding applicability with regard to the target condition (COVID-19) was by default scored as low concern with RT-PCR as reference test. Types of potential bias were assessed according to classifications as previously published by Whiting et al. including bias on verification, incorporation, imperfect reference standard, spectrum, review, disease progression and treatment paradox (14, I n p r e s s 15).

Reporting quality was rated according to the standards for reporting of diagnostic accuracy studies (STARD) 2015 statement checklist (16) . We evaluated all items on the checklist with the exception of: (1) adverse events from performing the index test or reference standard, because this risk is considered negligible, (2) registration number of studies with retrospective design, because we sympathize with the lack of retrospective study registration in this pandemic, (3) handling of missing data on the index test and reference standard for retrospective studies, (4) rationale for choosing reference standard when RT-PCR was used, because a PCR is the unquestioned reference standard for the detection of a viral pneumonia. For those four items we added 'not applicable' to the scoring system. All QUADAS-2 and STARD items (Appendix-3) were rated by two individual readers and cross-checked by a third reader in case of discordance (D.S., R.H. and A.H. respectively).

For each study, overall risk-of-bias and applicability was evaluated according to the grading of recommendations, assessment, development and evaluations (GRADE)-21 framework (13, 17) . Based on the GRADE framework, certainty of evidence for the totality of the diagnostic test accuracy studies was summarized based on concerns regarding study design, risk-of-bias, indirectness and applicability, imprecision in diagnostic accuracy measure estimates (wide confidence intervals), inconsistency (large differences in estimates) and publication bias (13, 17, 18) .

Studies were categorized 'diagnostic test accuracy' (DTA) studies if measures of diagnostic accuracy were reported (at least test sensitivity or specificity); studies not reporting diagnostic accuracy measures were categorized 'non-DTA'. Diagnostic test results were presented (1) as reported by authors, and (2) as re-calculated based on 2x2 frequency statistics as retrieved by our raters. The following diagnostic accuracy measures were re-calculated based on 2x2 tables: sensitivity, specificity, PPV, NPV, accuracy, pre-test probability, positive and negative post-test probability (19) .

As additional analysis, a predefined test interval of 3 days (i.e. RT-PCR ≤3 days after chest imaging) was set as cut-off for appropriate time interval between index test and reference test, to limit the probability of interim infection or cross-transmission between admitted patients. Depending on data availability, test accuracy results were re-calculated restricted to this time interval. In the case that a study would only contain a subgroup of study participants relevant to the review, 2x2 data would be extracted for these patients only. QUADAS-2 results (counts for low-risk, high-risk or unclear) were presented as overall, per domain and per study. Adherence to STARD was analyzed by number and percentage of STARD items reported, and summarized by calculating the proportion of reported I n p r e s s items to the total number of applicable STARD items. STARD in nature is designed for DTA-studies, but also non-DTA studies providing test results were included in this review. Therefore, QUADAS-2 and STARD subgroup analyses were performed for DTA studies.

No formal analysis on small-study effects was performed. Categorical data was presented as number (frequency) and continuous data as mean ± standard deviation (SD) or median [interquartile range; IQR] based on data distribution. Analysis was done using IBM SPSS Statistics version 25 and Microsoft Excel for Mac v16. Statistical significance was set at a level of p <0.05 (assuming two-tailed tests).

Our search yielded a total of 1706 articles ( Figure-1) . Thirteen studies on patients with suspected COVID-19 infection and available diagnostic accuracy data on chest CT and/or CXR performance as index test and RT-PCR or clinical consensus as reference test were included (8, (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) . No studies were found on chest ultrasound performance for COVID-19 diagnosis. No overlapping study populations were identified for the included studies.

Ten studies (20-23, 25-29, 32) were categorized as DTA and three (24, 30, 31) as non-DTA (Table-1) . Study design was cross-sectional in 10/13 (77%) and case-control in 3/13 (23%; all DTA) and only 1/13 studies was designed prospectively (8%). Information on patient comorbidities was reported in 3/13 (23%) studies (Table-2 ). Information on time between symptom onset and clinical presentation was described in 6/13 studies (46%). No study reported the number or percentage of asymptomatic patients. Severity of disease in subjects with confirmed COVID-19 was reported in 3/13 (23%) studies (21, 30, 31) and alternative diagnosis in subjects with rejected COVID-19 was reported in 2/13 (15%%) studies (21, 32) . Seven studies reported proportion of individuals with symptoms (mainly fever, cough and/or dyspnea) and laboratory results (mainly lymphocyte count, white blood cell count and CRP).

A definition of positive index test result was provided in 9/13 studies (69%) ( Table-3 ). Three studies (23, 25, 26) evaluated thick slice (3-5mm) CT in part of patients and two(30, 31) did not report slice thickness. Five studies dichotomized CT index test results into positive and negative, a I n p r e s s threshold was reported in 4/10 (40%) DTA-studies (25, 26, 28, 32) . One study combined baseline and follow up test results within subjects in the same primary test accuracy analysis (25) .

Double PCR swab (nasopharynx & oropharynx) was taken in 1/13 (8%) studies, 6/13 studies (46%) took a single swab and 6/13 (46%) did not report on sampling method for RT-PCR (Table-4 ).

Repeated RT-PCR sampling was performed in 6/13 (48%) studies (20, 21, (27) (28) (29) (30) and was not reported in 4/13 (31%) studies (23) (24) (25) (26) . Time from symptom onset to test and time between index and reference test was reported in two and four studies, respectively.

Disease prevalence (pre-test probability) was reported in 2/10 (20%) DTA studies and measures of precision were given in 8/10 (80%) studies (Table-5 was abandoned as only one small cohort provided data for the preset time interval (27) . The one CXR study reported a sensitivity and specificity of 0.25 and 0.90, respectively with an AUC of 0.58.

We were unable to reliably reconstruct the 2x2 contingency table for this study.

Pooling diagnostic test accuracy results and performing a meta-analysis was considered not justified given the study heterogeneity, QUADAS-2 and STARD results.

The purpose of imaging, i.e. diagnosis of patients with suspected COVID-19 infection was clearly described in 7/13 (54%) studies (20, 22, 23, (26) (27) (28) (29) . Two studies clearly described the role of imaging, as replacement(23) and parallel/combined (32) . Overall, QUADAS-based signaling questions (Appendix-3) were scored as low-risk or concern in 69/182 (38%), unclear in 78/182 (43%) and highrisk or concern in 35/182 (19%). Risk-of-bias was scored low-risk in 24/143 (17%), unclear in 68/143 (48%) and high-risk in 51/143 (36%) questions. Risk-of-bias was highest for patient selection and flow and timing (Figure-2). Bias for index and reference test was unknown for most studies. Not one study scored low risk-of-bias for all 4 QUADAS-2 domains, three studies scored no high-risk in any I n p r e s s domain ( Figure-3 ). High-risk in at least one domain was scored in 10/13 studies (77%) and high-risk in at least two domains in 7/13 studies (54%).

Applicability of studies was scored low concern in 15/39 (38%) unclear in 10/13 (26%) and high concern in 11/13 (28%). Applicability concern was highest for patient selection, scored unclear in 2/13 (15%) and high-risk in 8/13 (62%) studies, respectively, followed by concern regarding index test applicability, scored unclear and high concern in 7/13 (54%) and 3/13 (23%), respectively ( Figure-3) . Two studies (15%) scored low concerns for applicability for all domains, and three studies (20%) scored only low or unclear concerns. High concern for applicability in at least one domain was scored in 8 studies (62%), in at least two domains in 2 studies (15%).

Mean number of reported STARD items was 12/34 (35%) for all studies (Figure- 

Based on the GRADE framework the certainty of evidence for the totality of CT test accuracy studies for COVID-19 diagnosis was rated very low for both sensitivity and specificity estimates ( Table 7) .

Certainty of evidence for CXR was not evaluated within GRADE as only one study was identified. values however, directly depend on disease prevalence (34) . When studies only include COVID-19 confirmed subjects, they will not include information on true negative cases and are therefore prone to low thresholds for test positivity, thereby inflating test sensitivity. An example of such a case is the recently published meta-analysis on CT DTA-studies for COVID-19 diagnosis (35) , as the vast majority of subjects were COVID-19 confirmed patients. Hereby, the accuracy for discrimination between diseased and non-diseased remains unknown. Important other sources of test variation include patient demographics, the severity of disease, index and reference test execution, (predefined or chosen) thresholds, but also those related to initial study design and risk-of-bias.

QUADAS-2 and STARD are efficient and clear tools for authors and readers of diagnostic accuracy studies to evaluate the reported study setting and judge the bias, applicability and expected test performance (16, 36) .

Complete reporting is needed to allow for the assessment of sources of variation and potential bias and judge reported and expected test performance. Bias in patient selection may result in overestimation of diagnostic accuracy. High pretest probability, strict selection of patients with severe disease or high risk (COVID-19 exposure history) will typically result in spectrum bias with increased (or overestimated) test sensitivity. In the current review, majority of studies did not report on more demographics than age and gender, and no information on socio-economic status was provided. Study subjects were typically patients presenting at the ED/hospital with fever and/or exposure history to COVID-19 (area or person), with unknown severity of disease or distribution of I n p r e s s alternative diagnoses. Concerns with regard to generalizability rise with inclusion of patients with very high suspicion and/or exposure history (29) , a previous positive test (not further specified) (22) , the use of patients with another type of viral pneumonia as control group (21, 23) , or, with exclusion of patients with non-infectious lung disease(28), non-pregnant or non-Chinese subjects (30) or if CT was performed within 3 days after symptom onset (26) or CT findings were normal (21, 23-25, 27, 28) .

Six studies excluded patients with normal chest CT findings (or included abnormal CT only) (21, 23-25, 27, 28) and/or did not perform RT-PCR in suspected patients without CT abnormalities (106/204 excluded participants) (27) . This selection may result in overestimation of test performance. Test sensitivity and specificity may also be overestimated with an arbitrary choice of test threshold. As CT and CXR are multilevel tests, a definition of positive (or negative) test is required, but this was lacking in 69% (9/13) included studies. One study defined a positive test cutoff when 3/8 readers scoring CXR 'positive' (25) . For most, if not all, studies it was unclear how indeterminate results were handled. Higher sensitivity and (possibly) lower specificity may also be driven by verification bias (14, 15, 37) . An example of partial verification bias occurred in the study in which RT-PCR was performed in patients with abnormal CT findings only (27) . Differential verification bias may have occurred if the use of different (more invasive) swab specimens (e.g. nasopharynx versus bronchoalvealar lavage) (28, 29) or different reference test analysis methods or PCR kits (20, 21) was driven by CT findings, though this is unclear. In addition, various rates of RT-PCR sensitivity have been reported, suggesting a potential imperfect gold standard (7, 8) . Bias may be introduced if RT-PCR sensitivity is in fact lower, resulting in misdiagnosis and underestimation of index test performance.

Proper reporting of the reference test method and analysis is therefore required. However, studies typically did not provide information on type of RT-PCR test kit, specification or certification and/or whether single or multiple swabs were taken. In these cases, it was unclear whether study subjects received the same reference test. Since information on RT-PCR was insufficient, generalizability cannot be assessed. Also, no report was made on treatment or other clinical intervention between index test and RT-PCR, thus potential treatment paradox is unclear. Disease progression bias may potentially have been introduced considering the relatively large reported time intervals (up to 8 days) between CT and RT-PCR. Especially in early COVID-19, most suspected COVID-19 patients were admitted to fever clinic departments and cross infection may have occurred within this time interval. No study reported on blinding for index test result, though with (semi-) automated analyses this may be considered irrelevant. Access to RT-PCR results when reading the index test will, however, induce unacceptable review bias and overestimate test performance.

Blinding for RT-PCR results was unclear in 4 DTA-studies (22, 24, 25, 27) . Other general concerns for generalizability of the index test not yet assessed include the use of thick slice CT (23, 25, 26) . CT performance also highly depends on readers' experience and rater reproducibility, though results on intrarater (32) or interrater (26, 32) performance were rather scarce.

Our systematic review has several limitations. We attempted to identify all published articles by including a non-indexed journal, though more DTA-studies may be reported in other non-indexed journals we are unfamiliar with. Only a small number of studies were eligible for inclusion. Five non-English articles were excluded for language restrictions given the short time frame and urge of this review, though likely similar bias and study weakness would have been encountered. A metaanalysis was not performed due to the low reporting quality. In the setting of prompt diagnosis, we sought to provide additional diagnostic test results for patients who received CT and RT-PCR tests within the predefined time interval of 3 days. Only one study allowed for re-calculation of CT performance within the predefined time interval (27) . Interpretation of the QUADAS-2 and STARD items is subjective; for this multiple readers assessed the items individually.

To conclude, certainty of evidence was rated very low for both sensitivity and specificity estimates of CT for COVID-19 diagnosis in patients with clinical suspicion. Reported test accuracy of CT for diagnosis of COVID-19 infection varies substantially, from rather poor to excellent. Validity and generalizability of these findings is complicated by poor adherence to reporting guidelines and high risk of bias, which are most likely due to the need for urgent publication of findings in the first months of the covid-19 pandemic.

The authors have no potential conflict of interest and received no funding for this work. (43) . and COVID-19 features: GGO, consolidation, crazy-paving, air bronchogram, cavitation, pulmonary nodule, lymphadenopathy, pleural effusion, pulmonary atelectasis, pleural thickening and lesion distribution $5 multiple GGOs, bilateral/multifocal involvement, peripheral distribution and, at a later stage, crazy paving, consolidation and reversed halo sign AI= artificial intellgence; CT=computed tomography; CXR=chest radiography; GGO=ground glass opacity; NA=not applicable; NR=not reported I n p r e s s Explanations a. Multiple studies with high concern for bias in the QUADAS-2 domains patient selection, flow and timing. b. Multiple studies with high concern for applicability in the QUADAS-2 domains patient selection and index test. This table was created through the online GRADE Pro tool at http://www.gradepro.org. diagnostic test accuracy studies (right). The different STARD items concern the following sections in the reports: title or abstract (1), abstract (1,2), introduction (3), methods (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) , results (19) (20) (21) (22) (23) (24) (25) , discussion (26, 27) and other (28) (29) (30) .

I n p r e s s Upper ten studies concern diagnostic test accuracy studies (in bold), bottom three concern nondiagnostic test accuracy studies. The individual STARD items (presented as 1-30) are listed in Figure   4 . 

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