Publications by authors named "Yemisi Takwoingi"

95 Publications

QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Ann Intern Med 2021 Oct 26. Epub 2021 Oct 26.

Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands (B.Y., M.M.G.L.).

Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.
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http://dx.doi.org/10.7326/M21-2234DOI Listing
October 2021

Challenges in Comparative Meta-Analysis of the Accuracy of Multiple Diagnostic Tests.

Methods Mol Biol 2022 ;2345:299-316

Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.

The rapid increase in diagnostic and screening techniques has urged the need to choose among multiple diagnostic tests. For the majority of diseases, there is more than a single test available, and studies usually compare a subset of these tests. In such cases, a separate meta-analysis of each test cannot provide a reliable answer on the relative accuracy of the multiple available tests. Extensions of standard (hierarchical) meta-analysis to network meta-analysis (NMA) models for the comparison of at least three diagnostic tests have been the subject of methodological research in recent years. NMA can be used to jointly analyze the totality of evidence in order to provide estimates of relative accuracy (sensitivity and specificity ), to compare tests that have not been compared head-to-head, and to obtain a ranking of all competing tests in order to further facilitate the decision-making process.In this chapter, we illustrate current methodology for meta-analysis of multiple test comparisons, introduce NMA methods of diagnostic tests as an extension to the standard meta-analysis of diagnostic test accuracy (DTA) studies, and present existing approaches to rank tests according to their accuracy, specificity , and sensitivity . We also describe the basic concepts, underlying assumptions, and challenges in NMA of multiple diagnostic tests.
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http://dx.doi.org/10.1007/978-1-0716-1566-9_18DOI Listing
January 2022

Defining the Role of Cellular Immune Signatures in Diagnostic Evaluation of Suspected Tuberculosis.

J Infect Dis 2021 Jul 31. Epub 2021 Jul 31.

TB Research Centre, National Heart and Lung Institute, Imperial College London, London, United Kingdom.

Background: Diagnosis of paucibacillary tuberculosis (TB) including extrapulmonary TB is a significant challenge, particularly in high-income, low-incidence settings. Measurement of Mycobacterium tuberculosis (Mtb)-specific cellular immune signatures by flow cytometry discriminates active TB from latent TB infection (LTBI) in case-control studies; however, their diagnostic accuracy and clinical utility in routine clinical practice is unknown.

Methods: Using a nested case-control study design within a prospective multicenter cohort of patients presenting with suspected TB in England, we assessed diagnostic accuracy of signatures in 134 patients who tested interferon-gamma release assay (IGRA)-positive and had final diagnoses of TB or non-TB diseases with coincident LTBI. Cellular signatures were measured using flow cytometry.

Results: All signatures performed less well than previously reported. Only signatures incorporating measurement of phenotypic markers on functional Mtb-specific CD4 T cells discriminated active TB from non-TB diseases with LTBI. The signatures measuring HLA-DR+IFNγ + CD4 T cells and CD45RA-CCR7-CD127- IFNγ -IL-2-TNFα + CD4 T cells performed best with 95% positive predictive value (95% confidence interval, 90-97) in the clinically challenging subpopulation of IGRA-positive but acid-fast bacillus (AFB) smear-negative TB suspects.

Conclusions: Two cellular immune signatures could improve and accelerate diagnosis in the challenging group of patients who are IGRA-positive, AFB smear-negative, and have paucibacillary TB.
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http://dx.doi.org/10.1093/infdis/jiab311DOI Listing
July 2021

Screening tests for active pulmonary tuberculosis in children.

Cochrane Database Syst Rev 2021 06 28;6:CD013693. Epub 2021 Jun 28.

The Global Tuberculosis Program, Texas Children's Hospital, Section of Global and Immigrant Health, Department of Pediatrics, Baylor College of Medicine , Houston, Texas, USA.

Background: Globally, children under 15 years represent approximately 12% of new tuberculosis cases, but 16% of the estimated 1.4 million deaths. This higher share of mortality highlights the urgent need to develop strategies to improve case detection in this age group and identify children without tuberculosis disease who should be considered for tuberculosis preventive treatment. One such strategy is systematic screening for tuberculosis in high-risk groups.

Objectives: To estimate the sensitivity and specificity of the presence of one or more tuberculosis symptoms, or symptom combinations; chest radiography (CXR); Xpert MTB/RIF; Xpert Ultra; and combinations of these as screening tests for detecting active pulmonary childhood tuberculosis in the following groups. - Tuberculosis contacts, including household contacts, school contacts, and other close contacts of a person with infectious tuberculosis. - Children living with HIV. - Children with pneumonia. - Other risk groups (e.g. children with a history of previous tuberculosis, malnourished children). - Children in the general population in high tuberculosis burden settings.

Search Methods: We searched six databases, including the Cochrane Central Register of Controlled Trials, MEDLINE, and Embase, on 14 February 2020 without language restrictions and contacted researchers in the field.

Selection Criteria: Cross-sectional and cohort studies where at least 75% of children were aged under 15 years. Studies were eligible if conducted for screening rather than diagnosing tuberculosis. Reference standards were microbiological (MRS) and composite reference standard (CRS), which may incorporate symptoms and CXR.

Data Collection And Analysis: Two review authors independently extracted data and assessed study quality using QUADAS-2. We consolidated symptom screens across included studies into groups that used similar combinations of symptoms as follows: one or more of cough, fever, or poor weight gain and one or more of cough, fever, or decreased playfulness. For combination of symptoms, a positive screen was the presence of one or more than one symptom. We used a bivariate model to estimate pooled sensitivity and specificity with 95% confidence intervals (CIs) and performed analyses separately by reference standard. We assessed certainty of evidence using GRADE.

Main Results: Nineteen studies assessed the following screens: one symptom (15 studies, 10,097 participants); combinations of symptoms (12 studies, 29,889 participants); CXR (10 studies, 7146 participants); and Xpert MTB/RIF (2 studies, 787 participants). Several studies assessed more than one screening test. No studies assessed Xpert Ultra. For 16 studies (84%), risk of bias for the reference standard domain was unclear owing to concern about incorporation bias. Across other quality domains, risk of bias was generally low. Symptom screen (verified by CRS) One or more of cough, fever, or poor weight gain in tuberculosis contacts (4 studies, tuberculosis prevalence 2% to 13%): pooled sensitivity was 89% (95% CI 52% to 98%; 113 participants; low-certainty evidence) and pooled specificity was 69% (95% CI 51% to 83%; 2582 participants; low-certainty evidence). Of 1000 children where 50 have pulmonary tuberculosis, 339 would be screen-positive, of whom 294 (87%) would not have pulmonary tuberculosis (false positives); 661 would be screen-negative, of whom five (1%) would have pulmonary tuberculosis (false negatives). One or more of cough, fever, or decreased playfulness in children aged under five years, inpatient or outpatient (3 studies, tuberculosis prevalence 3% to 13%): sensitivity ranged from 64% to 76% (106 participants; moderate-certainty evidence) and specificity from 37% to 77% (2339 participants; low-certainty evidence). Of 1000 children where 50 have pulmonary tuberculosis, 251 to 636 would be screen-positive, of whom 219 to 598 (87% to 94%) would not have pulmonary tuberculosis; 364 to 749 would be screen-negative, of whom 12 to 18 (2% to 3%) would have pulmonary tuberculosis. One or more of cough, fever, poor weight gain, or tuberculosis close contact (World Health Organization four-symptom screen) in children living with HIV, outpatient (2 studies, tuberculosis prevalence 3% and 8%): pooled sensitivity was 61% (95% CI 58% to 64%; 1219 screens; moderate-certainty evidence) and pooled specificity was 94% (95% CI 86% to 98%; 201,916 screens; low-certainty evidence). Of 1000 symptom screens where 50 of the screens are on children with pulmonary tuberculosis, 88 would be screen-positive, of which 57 (65%) would be on children who do not have pulmonary tuberculosis; 912 would be screen-negative, of which 19 (2%) would be on children who have pulmonary tuberculosis. CXR (verified by CRS) CXR with any abnormality in tuberculosis contacts (8 studies, tuberculosis prevalence 2% to 25%): pooled sensitivity was 87% (95% CI 75% to 93%; 232 participants; low-certainty evidence) and pooled specificity was 99% (95% CI 68% to 100%; 3281 participants; low-certainty evidence). Of 1000 children, where 50 have pulmonary tuberculosis, 63 would be screen-positive, of whom 19 (30%) would not have pulmonary tuberculosis; 937 would be screen-negative, of whom 6 (1%) would have pulmonary tuberculosis. Xpert MTB/RIF (verified by MRS) Xpert MTB/RIF, inpatient or outpatient (2 studies, tuberculosis prevalence 1% and 4%): sensitivity was 43% and 100% (16 participants; very low-certainty evidence) and specificity was 99% and 100% (771 participants; moderate-certainty evidence). Of 1000 children, where 50 have pulmonary tuberculosis, 31 to 69 would be Xpert MTB/RIF-positive, of whom 9 to 19 (28% to 29%) would not have pulmonary tuberculosis; 969 to 931 would be Xpert MTB/RIF-negative, of whom 0 to 28 (0% to 3%) would have tuberculosis. Studies often assessed more symptoms than those included in the index test and symptom definitions varied. These differences complicated data aggregation and may have influenced accuracy estimates. Both symptoms and CXR formed part of the CRS (incorporation bias), which may have led to overestimation of sensitivity and specificity.

Authors' Conclusions: We found that in children who are tuberculosis contacts or living with HIV, screening tests using symptoms or CXR may be useful, but our review is limited by design issues with the index test and incorporation bias in the reference standard. For Xpert MTB/RIF, we found insufficient evidence regarding screening accuracy. Prospective evaluations of screening tests for tuberculosis in children will help clarify their use. In the meantime, screening strategies need to be pragmatic to address the persistent gaps in prevention and case detection that exist in resource-limited settings.
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http://dx.doi.org/10.1002/14651858.CD013693.pub2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8237391PMC
June 2021

Conducting invasive urodynamics in primary care: qualitative interview study examining experiences of patients and healthcare professionals.

Diagn Progn Res 2021 May 18;5(1):10. Epub 2021 May 18.

Department of Urology, Newcastle upon Tyne NHS Hospital Trust, Newcastle Freeman Hospital, Freeman Road, High Heaton, Newcastle upon Tyne, UK.

Background: Invasive urodynamics is used to investigate the causes of lower urinary tract symptoms; a procedure usually conducted in secondary care by specialist practitioners. No study has yet investigated the feasibility of carrying out this procedure in a non-specialist setting. Therefore, the aim of this study was to explore, using qualitative methodology, the feasibility and acceptability of conducting invasive urodynamic testing in primary care.

Methods: Semi-structured interviews were conducted during the pilot phase of the PriMUS study, in which men experiencing bothersome lower urinary tract symptoms underwent invasive urodynamic testing along with a series of simple index tests in a primary care setting. Interviewees were 25 patients invited to take part in the PriMUS study and 18 healthcare professionals involved in study delivery. Interviews were audio-recorded, transcribed verbatim and analysed using a framework approach.

Results: Patients generally found the urodynamic procedure acceptable and valued the primary care setting due to its increased accessibility and familiarity. Despite some logistical issues, facilitating invasive urodynamic testing in primary care was also a positive experience for urodynamic nurses. Initial issues with general practitioners receiving and utilising the results of urodynamic testing may have limited the potential benefit to some patients. Effective approaches to study recruitment included emphasising the benefits of the urodynamic test and maintaining contact with potential participants by telephone. Patients' relationship with their general practitioner was an important influence on study participation.

Conclusions: Conducting invasive urodynamics in primary care is feasible and acceptable and has the potential to benefit patients. Facilitating study procedures in a familiar primary care setting can impact positively on research recruitment. However, it is vital that there is a support network for urodynamic nurses and expertise available to help interpret urodynamic results.
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http://dx.doi.org/10.1186/s41512-021-00100-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130146PMC
May 2021

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study.

BJU Int 2021 Oct 8;128(4):440-450. Epub 2021 Sep 8.

Great Western Hospitals NHS Foundation Trust, Swindon, UK.

Objective: To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation.

Patients And Methods: This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries.

Results: Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3-34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1-30.2), UTUC (n = 128) 1.14% (95% CI 0.77-1.52), renal cancer (n = 107) 1.05% (95% CI 0.80-1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32-2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03-1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90-4.15; P < 0.001), male sex 1.30 (95% CI 1.14-1.50; P < 0.001), and smoking 2.70 (95% CI 2.30-3.18; P < 0.001).

Conclusions: A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer.
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http://dx.doi.org/10.1111/bju.15483DOI Listing
October 2021

Study designs for comparative diagnostic test accuracy: A methodological review and classification scheme.

J Clin Epidemiol 2021 Apr 26;138:128-138. Epub 2021 Apr 26.

Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.

Objectives: (1) To identify and classify comparative diagnostic test accuracy (DTA) study designs; (2) to describe study design labels used by authors of comparative DTA studies.

Methods: We performed a methodological review of 100 comparative DTA studies published between 2015 and 2017, randomly sampled from studies included in 238 comparative DTA systematic reviews indexed in MEDLINE in 2017. From each study report, we extracted six design elements characterizing participant flow and the labels used by authors.

Results: We identified a total of 46 unique combinations of study design features in our sample, based on six design elements characterizing participant flow. We classified the studies into five study design categories based on how participants were allocated to receive each index test: 'fully paired' (n=79), 'partially paired, random subset' (n=0), 'partially paired, nonrandom subset' (n=2), 'unpaired randomized' (n=1) and 'unpaired nonrandomized' (n=3). The allocation method used in 15 studies was unclear. Sixty-one studies reported, in total, 29 unique study design labels but only four labels referred to specific design features of comparative studies.

Conclusion: Our classification scheme can help systematic review authors define study eligibility criteria, assess risk of bias, and communicate the strength of the evidence. A standardized labelling scheme could be developed to facilitate communication of specific design features.
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http://dx.doi.org/10.1016/j.jclinepi.2021.04.013DOI Listing
April 2021

Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection.

Cochrane Database Syst Rev 2021 03 24;3:CD013705. Epub 2021 Mar 24.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: Accurate rapid diagnostic tests for SARS-CoV-2 infection could contribute to clinical and public health strategies to manage the COVID-19 pandemic. Point-of-care antigen and molecular tests to detect current infection could increase access to testing and early confirmation of cases, and expediate clinical and public health management decisions that may reduce transmission.

Objectives: To assess the diagnostic accuracy of point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection. We consider accuracy separately in symptomatic and asymptomatic population groups.

Search Methods: Electronic searches of the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) were undertaken on 30 Sept 2020. We checked repositories of COVID-19 publications and included independent evaluations from national reference laboratories, the Foundation for Innovative New Diagnostics and the Diagnostics Global Health website to 16 Nov 2020. We did not apply language restrictions.

Selection Criteria: We included studies of people with either suspected SARS-CoV-2 infection, known SARS-CoV-2 infection or known absence of infection, or those who were being screened for infection. We included test accuracy studies of any design that evaluated commercially produced, rapid antigen or molecular tests suitable for a point-of-care setting (minimal equipment, sample preparation, and biosafety requirements, with results within two hours of sample collection). We included all reference standards that define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction (RT-PCR) tests and established diagnostic criteria).

Data Collection And Analysis: Studies were screened independently in duplicate with disagreements resolved by discussion with a third author. Study characteristics were extracted by one author and checked by a second; extraction of study results and assessments of risk of bias and applicability (made using the QUADAS-2 tool) were undertaken independently in duplicate. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and pooled data using the bivariate model separately for antigen and molecular-based tests. We tabulated results by test manufacturer and compliance with manufacturer instructions for use and according to symptom status.

Main Results: Seventy-eight study cohorts were included (described in 64 study reports, including 20 pre-prints), reporting results for 24,087 samples (7,415 with confirmed SARS-CoV-2). Studies were mainly from Europe (n = 39) or North America (n = 20), and evaluated 16 antigen and five molecular assays. We considered risk of bias to be high in 29 (50%) studies because of participant selection; in 66 (85%) because of weaknesses in the reference standard for absence of infection; and in 29 (45%) for participant flow and timing. Studies of antigen tests were of a higher methodological quality compared to studies of molecular tests, particularly regarding the risk of bias for participant selection and the index test. Characteristics of participants in 35 (45%) studies differed from those in whom the test was intended to be used and the delivery of the index test in 39 (50%) studies differed from the way in which the test was intended to be used. Nearly all studies (97%) defined the presence or absence of SARS-CoV-2 based on a single RT-PCR result, and none included participants meeting case definitions for probable COVID-19. Antigen tests Forty-eight studies reported 58 evaluations of antigen tests. Estimates of sensitivity varied considerably between studies. There were differences between symptomatic (72.0%, 95% CI 63.7% to 79.0%; 37 evaluations; 15530 samples, 4410 cases) and asymptomatic participants (58.1%, 95% CI 40.2% to 74.1%; 12 evaluations; 1581 samples, 295 cases). Average sensitivity was higher in the first week after symptom onset (78.3%, 95% CI 71.1% to 84.1%; 26 evaluations; 5769 samples, 2320 cases) than in the second week of symptoms (51.0%, 95% CI 40.8% to 61.0%; 22 evaluations; 935 samples, 692 cases). Sensitivity was high in those with cycle threshold (Ct) values on PCR ≤25 (94.5%, 95% CI 91.0% to 96.7%; 36 evaluations; 2613 cases) compared to those with Ct values >25 (40.7%, 95% CI 31.8% to 50.3%; 36 evaluations; 2632 cases). Sensitivity varied between brands. Using data from instructions for use (IFU) compliant evaluations in symptomatic participants, summary sensitivities ranged from 34.1% (95% CI 29.7% to 38.8%; Coris Bioconcept) to 88.1% (95% CI 84.2% to 91.1%; SD Biosensor STANDARD Q). Average specificities were high in symptomatic and asymptomatic participants, and for most brands (overall summary specificity 99.6%, 95% CI 99.0% to 99.8%). At 5% prevalence using data for the most sensitive assays in symptomatic people (SD Biosensor STANDARD Q and Abbott Panbio), positive predictive values (PPVs) of 84% to 90% mean that between 1 in 10 and 1 in 6 positive results will be a false positive, and between 1 in 4 and 1 in 8 cases will be missed. At 0.5% prevalence applying the same tests in asymptomatic people would result in PPVs of 11% to 28% meaning that between 7 in 10 and 9 in 10 positive results will be false positives, and between 1 in 2 and 1 in 3 cases will be missed. No studies assessed the accuracy of repeated lateral flow testing or self-testing. Rapid molecular assays Thirty studies reported 33 evaluations of five different rapid molecular tests. Sensitivities varied according to test brand. Most of the data relate to the ID NOW and Xpert Xpress assays. Using data from evaluations following the manufacturer's instructions for use, the average sensitivity of ID NOW was 73.0% (95% CI 66.8% to 78.4%) and average specificity 99.7% (95% CI 98.7% to 99.9%; 4 evaluations; 812 samples, 222 cases). For Xpert Xpress, the average sensitivity was 100% (95% CI 88.1% to 100%) and average specificity 97.2% (95% CI 89.4% to 99.3%; 2 evaluations; 100 samples, 29 cases). Insufficient data were available to investigate the effect of symptom status or time after symptom onset.

Authors' Conclusions: Antigen tests vary in sensitivity. In people with signs and symptoms of COVID-19, sensitivities are highest in the first week of illness when viral loads are higher. The assays shown to meet appropriate criteria, such as WHO's priority target product profiles for COVID-19 diagnostics ('acceptable' sensitivity ≥ 80% and specificity ≥ 97%), can be considered as a replacement for laboratory-based RT-PCR when immediate decisions about patient care must be made, or where RT-PCR cannot be delivered in a timely manner. Positive predictive values suggest that confirmatory testing of those with positive results may be considered in low prevalence settings. Due to the variable sensitivity of antigen tests, people who test negative may still be infected. Evidence for testing in asymptomatic cohorts was limited. Test accuracy studies cannot adequately assess the ability of antigen tests to differentiate those who are infectious and require isolation from those who pose no risk, as there is no reference standard for infectiousness. A small number of molecular tests showed high accuracy and may be suitable alternatives to RT-PCR. However, further evaluations of the tests in settings as they are intended to be used are required to fully establish performance in practice. Several important studies in asymptomatic individuals have been reported since the close of our search and will be incorporated at the next update of this review. Comparative studies of antigen tests in their intended use settings and according to test operator (including self-testing) are required.
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http://dx.doi.org/10.1002/14651858.CD013705.pub2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078597PMC
March 2021

Thoracic imaging tests for the diagnosis of COVID-19.

Cochrane Database Syst Rev 2021 03 16;3:CD013639. Epub 2021 Mar 16.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

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.
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http://dx.doi.org/10.1002/14651858.CD013639.pub4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078565PMC
March 2021

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.

Cochrane Database Syst Rev 2021 02 23;2:CD013665. Epub 2021 Feb 23.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: The clinical implications of SARS-CoV-2 infection are highly variable. Some people with SARS-CoV-2 infection remain asymptomatic, whilst the infection can cause mild to moderate COVID-19 and COVID-19 pneumonia in others. This can lead to some people requiring intensive care support and, in some cases, to death, especially in older adults. Symptoms such as fever, cough, or loss of smell or taste, and signs such as oxygen saturation are the first and most readily available diagnostic information. Such information could be used to either rule out COVID-19, or select patients for further testing. This is an update of this review, the first version of which published in July 2020.

Objectives: To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19.

Search Methods: For this review iteration we undertook electronic searches up to 15 July 2020 in the Cochrane COVID-19 Study Register and the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

Selection Criteria: Studies were eligible if they included patients with clinically suspected COVID-19, or if they recruited known cases with COVID-19 and controls without COVID-19. Studies were eligible when they recruited patients presenting to primary care or hospital outpatient settings. Studies in hospitalised patients were only included if symptoms and signs were recorded on admission or at presentation. Studies including patients who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards.

Data Collection And Analysis: Pairs of review authors independently selected all studies, at both title and abstract stage and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and resolved disagreements by discussion with a third review author. Two review authors independently assessed risk of bias using the Quality Assessment tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary studies were available, and whenever heterogeneity across studies was deemed acceptable.

Main Results: We identified 44 studies including 26,884 participants in total. Prevalence of COVID-19 varied from 3% to 71% with a median of 21%. There were three studies from primary care settings (1824 participants), nine studies from outpatient testing centres (10,717 participants), 12 studies performed in hospital outpatient wards (5061 participants), seven studies in hospitalised patients (1048 participants), 10 studies in the emergency department (3173 participants), and three studies in which the setting was not specified (5061 participants). The studies did not clearly distinguish mild from severe COVID-19, so we present the results for all disease severities together. Fifteen studies had a high risk of bias for selection of participants because inclusion in the studies depended on the applicable testing and referral protocols, which included many of the signs and symptoms under study in this review. This may have especially influenced the sensitivity of those features used in referral protocols, such as fever and cough. Five studies only included participants with pneumonia on imaging, suggesting that this is a highly selected population. In an additional 12 studies, we were unable to assess the risk for selection bias. This makes it very difficult to judge the validity of the diagnostic accuracy of the signs and symptoms from these included studies. The applicability of the results of this review update improved in comparison with the original review. A greater proportion of studies included participants who presented to outpatient settings, which is where the majority of clinical assessments for COVID-19 take place. However, still none of the studies presented any data on children separately, and only one focused specifically on older adults. We found data on 84 signs and symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. Only cough (25 studies) and fever (7 studies) had a pooled sensitivity of at least 50% but specificities were moderate to low. Cough had a sensitivity of 67.4% (95% confidence interval (CI) 59.8% to 74.1%) and specificity of 35.0% (95% CI 28.7% to 41.9%). Fever had a sensitivity of 53.8% (95% CI 35.0% to 71.7%) and a specificity of 67.4% (95% CI 53.3% to 78.9%). The pooled positive likelihood ratio of cough was only 1.04 (95% CI 0.97 to 1.11) and that of fever 1.65 (95% CI 1.41 to 1.93). Anosmia alone (11 studies), ageusia alone (6 studies), and anosmia or ageusia (6 studies) had sensitivities below 50% but specificities over 90%. Anosmia had a pooled sensitivity of 28.0% (95% CI 17.7% to 41.3%) and a specificity of 93.4% (95% CI 88.3% to 96.4%). Ageusia had a pooled sensitivity of 24.8% (95% CI 12.4% to 43.5%) and a specificity of 91.4% (95% CI 81.3% to 96.3%). Anosmia or ageusia had a pooled sensitivity of 41.0% (95% CI 27.0% to 56.6%) and a specificity of 90.5% (95% CI 81.2% to 95.4%). The pooled positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.25 (95% CI 3.17 to 5.71) and 4.31 (95% CI 3.00 to 6.18) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The pooled positive likelihood ratio of ageusia alone was only 2.88 (95% CI 2.02 to 4.09). Only two studies assessed combinations of different signs and symptoms, mostly combining fever and cough with other symptoms. These combinations had a specificity above 80%, but at the cost of very low sensitivity (< 30%).

Authors' Conclusions: The majority of individual signs and symptoms included in this review appear to have very poor diagnostic accuracy, although this should be interpreted in the context of selection bias and heterogeneity between studies. Based on currently available data, neither absence nor presence of signs or symptoms are accurate enough to rule in or rule out COVID-19. The presence of anosmia or ageusia may be useful as a red flag for COVID-19. The presence of fever or cough, given their high sensitivities, may also be useful to identify people for further testing. Prospective studies in an unselected population presenting to primary care or hospital outpatient settings, examining combinations of signs and symptoms to evaluate the syndromic presentation of COVID-19, are still urgently needed. Results from such studies could inform subsequent management decisions.
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http://dx.doi.org/10.1002/14651858.CD013665.pub2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407425PMC
February 2021

Transcriptomic signatures for diagnosing tuberculosis in clinical practice: a prospective, multicentre cohort study.

Lancet Infect Dis 2021 03 25;21(3):366-375. Epub 2021 Jan 25.

Tuberculosis Research Centre, National Heart and Lung Institute, Imperial College London, London, UK. Electronic address:

Background: Blood transcriptomic signatures for diagnosis of tuberculosis have shown promise in case-control studies, but none have been prospectively designed or validated in adults presenting with the full clinical spectrum of suspected tuberculosis, including extrapulmonary tuberculosis and common differential diagnoses that clinically resemble tuberculosis. We aimed to evaluate the diagnostic accuracy of transcriptomic signatures in patients presenting with clinically suspected tuberculosis in routine practice.

Methods: The Validation of New Technologies for Diagnostic Evaluation of Tuberculosis (VANTDET) study was nested within a prospective, multicentre cohort study in secondary care in England (IDEA 11/H0722/8). Patients (aged ≥16 years) suspected of having tuberculosis in the routine clinical inpatient and outpatient setting were recruited at ten National Health Service hospitals in England for IDEA and were included in VANTDET if they provided consent for genomic analysis. Patients had whole blood taken for microarray analysis to measure abundance of transcripts and were followed up for 6-12 months to determine final diagnoses on the basis of predefined diagnostic criteria. The diagnostic accuracy of six signatures derived from the cohort and three previously published transcriptomic signatures with potentially high diagnostic performance were assessed by calculating area under the receiver-operating characteristic curves (AUC-ROCs), sensitivities, and specificities.

Findings: Between Nov 25, 2011, and Dec 31, 2013, 1162 participants were enrolled. 628 participants (aged ≥16 years) were included in the analysis, of whom 212 (34%) had culture-confirmed tuberculosis, 89 (14%) had highly probable tuberculosis, and 327 (52%) had tuberculosis excluded. The novel signature with highest performance for identifying all active tuberculosis gave an AUC-ROC of 0·87 (95% CI 0·81-0·92), sensitivity of 77% (66-87), and specificity of 84% (74-91). The best-performing published signature gave an AUC-ROC of 0·83 (0·80-0·86), sensitivity of 78% (73-83), and specificity of 76% (70-80). For detecting highly probable tuberculosis, the best novel signature yielded results of 0·86 (0·71-0·95), 77% (56-94%), and 77% (57-95%). None of the relevant cohort-derived or previously published signatures achieved the WHO-defined targets of paired sensitivity and specificity for a non-sputum-based diagnostic test.

Interpretation: In a clinically representative cohort in routine practice in a low-incidence setting, transcriptomic signatures did not have adequate accuracy for diagnosis of tuberculosis, including in patients with highly probable tuberculosis where the unmet need is greatest. These findings suggest that transcriptomic signatures have little clinical utility for diagnostic assessment of suspected tuberculosis.

Funding: National Institute for Health Research.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907671PMC
March 2021

Introduction to diagnostic test accuracy studies.

Eur J Endocrinol 2021 Feb;184(2):E5-E9

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Diagnostic accuracy studies are fundamental for the assessment of diagnostic tests. Researchers need to understand the implications of their chosen design, opting for comparative designs where possible. Researchers should analyse test accuracy studies using the appropriate methods, acknowledging the uncertainty of results and avoiding overstating conclusions and ignoring the clinical situation which should inform the trade-off between sensitivity and specificity. Test accuracy studies should be reported with transparency using the STAndards for the Reporting of Diagnostic accuracy studies (STARD) checklist.
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February 2021

Thoracic imaging tests for the diagnosis of COVID-19.

Cochrane Database Syst Rev 2020 11 26;11:CD013639. Epub 2020 Nov 26.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants.

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 22 June 2020. We did not apply any language restrictions.

Selection Criteria: We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study.

Data Collection And Analysis: We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS-2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).

Main Results: We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases). Twenty-six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow. For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1). Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity. For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies.

Authors' Conclusions: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may have limited capability in differentiating SARS-CoV-2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of suspected COVID-19 cases should be carefully interpreted. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. 'threshold' effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways.
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November 2020

Routine laboratory testing to determine if a patient has COVID-19.

Cochrane Database Syst Rev 2020 11 19;11:CD013787. Epub 2020 Nov 19.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: Specific diagnostic tests to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and resulting COVID-19 disease are not always available and take time to obtain results. Routine laboratory markers such as white blood cell count, measures of anticoagulation, C-reactive protein (CRP) and procalcitonin, are used to assess the clinical status of a patient. These laboratory tests may be useful for the triage of people with potential COVID-19 to prioritize them for different levels of treatment, especially in situations where time and resources are limited.

Objectives: To assess the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19.

Search Methods: On 4 May 2020 we undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

Selection Criteria: We included both case-control designs and consecutive series of patients that assessed the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19. The reference standard could be reverse transcriptase polymerase chain reaction (RT-PCR) alone; RT-PCR plus clinical expertise or and imaging; repeated RT-PCR several days apart or from different samples; WHO and other case definitions; and any other reference standard used by the study authors.

Data Collection And Analysis: Two review authors independently extracted data from each included study. They also assessed the methodological quality of the studies, using QUADAS-2. We used the 'NLMIXED' procedure in SAS 9.4 for the hierarchical summary receiver operating characteristic (HSROC) meta-analyses of tests for which we included four or more studies. To facilitate interpretation of results, for each meta-analysis we estimated summary sensitivity at the points on the SROC curve that corresponded to the median and interquartile range boundaries of specificities in the included studies.

Main Results: We included 21 studies in this review, including 14,126 COVID-19 patients and 56,585 non-COVID-19 patients in total. Studies evaluated a total of 67 different laboratory tests. Although we were interested in the diagnotic accuracy of routine tests for COVID-19, the included studies used detection of SARS-CoV-2 infection through RT-PCR as reference standard. There was considerable heterogeneity between tests, threshold values and the settings in which they were applied. For some tests a positive result was defined as a decrease compared to normal vaues, for other tests a positive result was defined as an increase, and for some tests both increase and decrease may have indicated test positivity. None of the studies had either low risk of bias on all domains or low concerns for applicability for all domains. Only three of the tests evaluated had a summary sensitivity and specificity over 50%. These were: increase in interleukin-6, increase in C-reactive protein and lymphocyte count decrease. Blood count Eleven studies evaluated a decrease in white blood cell count, with a median specificity of 93% and a summary sensitivity of 25% (95% CI 8.0% to 27%; very low-certainty evidence). The 15 studies that evaluated an increase in white blood cell count had a lower median specificity and a lower corresponding sensitivity. Four studies evaluated a decrease in neutrophil count. Their median specificity was 93%, corresponding to a summary sensitivity of 10% (95% CI 1.0% to 56%; low-certainty evidence). The 11 studies that evaluated an increase in neutrophil count had a lower median specificity and a lower corresponding sensitivity. The summary sensitivity of an increase in neutrophil percentage (4 studies) was 59% (95% CI 1.0% to 100%) at median specificity (38%; very low-certainty evidence). The summary sensitivity of an increase in monocyte count (4 studies) was 13% (95% CI 6.0% to 26%) at median specificity (73%; very low-certainty evidence). The summary sensitivity of a decrease in lymphocyte count (13 studies) was 64% (95% CI 28% to 89%) at median specificity (53%; low-certainty evidence). Four studies that evaluated a decrease in lymphocyte percentage showed a lower median specificity and lower corresponding sensitivity. The summary sensitivity of a decrease in platelets (4 studies) was 19% (95% CI 10% to 32%) at median specificity (88%; low-certainty evidence). Liver function tests The summary sensitivity of an increase in alanine aminotransferase (9 studies) was 12% (95% CI 3% to 34%) at median specificity (92%; low-certainty evidence). The summary sensitivity of an increase in aspartate aminotransferase (7 studies) was 29% (95% CI 17% to 45%) at median specificity (81%) (low-certainty evidence). The summary sensitivity of a decrease in albumin (4 studies) was 21% (95% CI 3% to 67%) at median specificity (66%; low-certainty evidence). The summary sensitivity of an increase in total bilirubin (4 studies) was 12% (95% CI 3.0% to 34%) at median specificity (92%; very low-certainty evidence). Markers of inflammation The summary sensitivity of an increase in CRP (14 studies) was 66% (95% CI 55% to 75%) at median specificity (44%; very low-certainty evidence). The summary sensitivity of an increase in procalcitonin (6 studies) was 3% (95% CI 1% to 19%) at median specificity (86%; very low-certainty evidence). The summary sensitivity of an increase in IL-6 (four studies) was 73% (95% CI 36% to 93%) at median specificity (58%) (very low-certainty evidence). Other biomarkers The summary sensitivity of an increase in creatine kinase (5 studies) was 11% (95% CI 6% to 19%) at median specificity (94%) (low-certainty evidence). The summary sensitivity of an increase in serum creatinine (four studies) was 7% (95% CI 1% to 37%) at median specificity (91%; low-certainty evidence). The summary sensitivity of an increase in lactate dehydrogenase (4 studies) was 25% (95% CI 15% to 38%) at median specificity (72%; very low-certainty evidence).

Authors' Conclusions: Although these tests give an indication about the general health status of patients and some tests may be specific indicators for inflammatory processes, none of the tests we investigated are useful for accurately ruling in or ruling out COVID-19 on their own. Studies were done in specific hospitalized populations, and future studies should consider non-hospital settings to evaluate how these tests would perform in people with milder symptoms.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078159PMC
November 2020

Rapid diagnostic tests for Plasmodium vivax malaria in endemic countries.

Cochrane Database Syst Rev 2020 11 4;11:CD013218. Epub 2020 Nov 4.

Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.

Background: Plasmodium vivax (P vivax) is a focus of malaria elimination. It is important because P vivax and Plasmodium falciparum infection are co-endemic in some areas. There are asymptomatic carriers of P vivax, and the treatment for P vivax and Plasmodium ovale malaria differs from that used in other types of malaria. Rapid diagnostic tests (RDTs) will help distinguish P vivax from other malaria species to help treatment and elimination. There are RDTs available that detect P vivax parasitaemia through the detection of P vivax-specific lactate dehydrogenase (LDH) antigens.

Objectives: To assess the diagnostic accuracy of RDTs for detecting P vivax malaria infection in people living in malaria-endemic areas who present to ambulatory healthcare facilities with symptoms suggestive of malaria; and to identify which types and brands of commercial tests best detect P vivax malaria.

Search Methods: We undertook a comprehensive search of the following databases up to 30 July 2019: Cochrane Infectious Diseases Group Specialized Register; Central Register of Controlled Trials (CENTRAL), published in the Cochrane Library; MEDLINE (PubMed); Embase (OVID); Science Citation Index Expanded (SCI-EXPANDED) and Conference Proceedings Citation Index-Science (CPCI-S), both in the Web of Science.

Selection Criteria: Studies comparing RDTs with a reference standard (microscopy or polymerase chain reaction (PCR)) in blood samples from patients attending ambulatory health facilities with symptoms suggestive of malaria in P vivax-endemic areas.

Data Collection And Analysis: For each included study, two review authors independently extracted data using a pre-piloted data extraction form. The methodological quality of the studies were assessed using a tailored Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. We grouped studies according to commercial brand of the RDT and performed meta-analysis when appropriate. The results given by the index tests were based on the antibody affinity (referred to as the strength of the bond between an antibody and an antigen) and avidity (referred to as the strength of the overall bond between a multivalent antibody and multiple antigens). All analyses were stratified by the type of reference standard. The bivariate model was used to estimate the pooled sensitivity and specificity with 95% confidence intervals (CIs), this model was simplified when studies were few. We assessed the certainty of the evidence using the GRADE approach.

Main Results: We included 10 studies that assessed the accuracy of six different RDT brands (CareStart Malaria Pf/Pv Combo test, Falcivax Device Rapid test, Immuno-Rapid Malaria Pf/Pv test, SD Bioline Malaria Ag Pf/Pv test, OnSite Pf/Pv test and Test Malaria Pf/Pv rapid test) for detecting P vivax malaria. One study directly compared the accuracy of two RDT brands. Of the 10 studies, six used microscopy, one used PCR, two used both microscopy and PCR separately and one used microscopy corrected by PCR as the reference standard. Four of the studies were conducted in Ethiopia, two in India, and one each in Bangladesh, Brazil, Colombia and Sudan. The studies often did not report how patients were selected. In the patient selection domain, we judged the risk of bias as unclear for nine studies. We judged all studies to be of unclear applicability concern. In the index test domain, we judged most studies to be at low risk of bias, but we judged nine studies to be of unclear applicability concern. There was poor reporting on lot testing, how the RDTs were stored, and background parasitaemia density (a key variable determining diagnostic accuracy of RDTs). Only half of the included studies were judged to be at low risk of bias in the reference standard domain, Studies often did not report whether the results of the reference standard could classify the target condition or whether investigators knew the results of the RDT when interpreting the results of the reference standard. All 10 studies were judged to be at low risk of bias in the flow and timing domain. Only two brands were evaluated by more than one study. Four studies evaluated the CareStart Malaria Pf/Pv Combo test against microscopy and two studies evaluated the Falcivax Device Rapid test against microscopy. The pooled sensitivity and specificity were 99% (95% CI 94% to 100%; 251 patients, moderate-certainty evidence) and 99% (95% CI 99% to 100%; 2147 patients, moderate-certainty evidence) for CareStart Malaria Pf/Pv Combo test. For a prevalence of 20%, about 206 people will have a positive CareStart Malaria Pf/Pv Combo test result and the remaining 794 people will have a negative result. Of the 206 people with positive results, eight will be incorrect (false positives), and of the 794 people with a negative result, two would be incorrect (false negative). For the Falcivax Device Rapid test, the pooled sensitivity was 77% (95% CI: 53% to 91%, 89 patients, low-certainty evidence) and the pooled specificity was 99% (95% CI: 98% to 100%, 621 patients, moderate-certainty evidence), respectively. For a prevalence of 20%, about 162 people will have a positive Falcivax Device Rapid test result and the remaining 838 people will have a negative result. Of the 162 people with positive results, eight will be incorrect (false positives), and of the 838 people with a negative result, 46 would be incorrect (false negative).

Authors' Conclusions: The CareStart Malaria Pf/Pv Combo test was found to be highly sensitive and specific in comparison to microscopy for detecting P vivax in ambulatory healthcare in endemic settings, with moderate-certainty evidence. The number of studies included in this review was limited to 10 studies and we were able to estimate the accuracy of 2 out of 6 RDT brands included, the CareStart Malaria Pf/Pv Combo test and the Falcivax Device Rapid test. Thus, the differences in sensitivity and specificity between all the RDT brands could not be assessed. More high-quality studies in endemic field settings are needed to assess and compare the accuracy of RDTs designed to detect P vivax.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078698PMC
November 2020

Tackling Interobserver Variability in Multiparametric Magnetic Resonance Imaging (MRI): Is MRI Even Better than We Think for Prostate Cancer Diagnosis?

Eur Urol 2021 01 2;79(1):8-10. Epub 2020 Nov 2.

Division of Surgery and Interventional Science, University College London, London, UK; Department of Urology, University College London Hospitals NHS Foundation Trust, London, UK; British Urology Researchers in Surgical Training Research Collaborative, London, UK. Electronic address:

In clinical practice, the agreement between radiologists in detecting suspicious lesions on magnetic resonance images could be higher than previously thought because of biases associated with study design, patient selection, and the statistical approach in current studies of interobserver agreement.
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January 2021

Thoracic imaging tests for the diagnosis of COVID-19.

Cochrane Database Syst Rev 2020 09 30;9:CD013639. Epub 2020 Sep 30.

Department of Radiology, University of Ottawa, Ottawa, Canada.

Background: The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable.

Objectives: To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19.

Search Methods: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020.

Selection Criteria: We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them.

Data Collection And Analysis: We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).

Main Results: We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without.

Authors' Conclusions: The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways.
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September 2020

Xpert MTB/RIF and Xpert MTB/RIF Ultra assays for active tuberculosis and rifampicin resistance in children.

Cochrane Database Syst Rev 2020 08 27;8:CD013359. Epub 2020 Aug 27.

The Global Tuberculosis Program, Texas Children's Hospital, Section of Global and Immigrant Health, Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA.

Background: Every year, at least one million children become ill with tuberculosis and around 200,000 children die. Xpert MTB/RIF and Xpert Ultra are World Health Organization (WHO)-recommended rapid molecular tests that simultaneously detect tuberculosis and rifampicin resistance in adults and children with signs and symptoms of tuberculosis, at lower health system levels. To inform updated WHO guidelines on molecular assays, we performed a systematic review on the diagnostic accuracy of these tests in children presumed to have active tuberculosis.

Objectives: Primary objectives • To determine the diagnostic accuracy of Xpert MTB/RIF and Xpert Ultra for (a) pulmonary tuberculosis in children presumed to have tuberculosis; (b) tuberculous meningitis in children presumed to have tuberculosis; (c) lymph node tuberculosis in children presumed to have tuberculosis; and (d) rifampicin resistance in children presumed to have tuberculosis - For tuberculosis detection, index tests were used as the initial test, replacing standard practice (i.e. smear microscopy or culture) - For detection of rifampicin resistance, index tests replaced culture-based drug susceptibility testing as the initial test Secondary objectives • To compare the accuracy of Xpert MTB/RIF and Xpert Ultra for each of the four target conditions • To investigate potential sources of heterogeneity in accuracy estimates - For tuberculosis detection, we considered age, disease severity, smear-test status, HIV status, clinical setting, specimen type, high tuberculosis burden, and high tuberculosis/HIV burden - For detection of rifampicin resistance, we considered multi-drug-resistant tuberculosis burden • To compare multiple Xpert MTB/RIF or Xpert Ultra results (repeated testing) with the initial Xpert MTB/RIF or Xpert Ultra result SEARCH METHODS: We searched the Cochrane Infectious Diseases Group Specialized Register, MEDLINE, Embase, Science Citation Index, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Scopus, the WHO International Clinical Trials Registry Platform, ClinicalTrials.gov, and the International Standard Randomized Controlled Trials Number (ISRCTN) Registry up to 29 April 2019, without language restrictions.

Selection Criteria: Randomized trials, cross-sectional trials, and cohort studies evaluating Xpert MTB/RIF or Xpert Ultra in HIV-positive and HIV-negative children younger than 15 years. Reference standards comprised culture or a composite reference standard for tuberculosis and drug susceptibility testing or MTBDRplus (molecular assay for detection of Mycobacterium tuberculosis and drug resistance) for rifampicin resistance. We included studies evaluating sputum, gastric aspirate, stool, nasopharyngeal or bronchial lavage specimens (pulmonary tuberculosis), cerebrospinal fluid (tuberculous meningitis), fine needle aspirates, or surgical biopsy tissue (lymph node tuberculosis).

Data Collection And Analysis: Two review authors independently extracted data and assessed study quality using the Quality Assessment of Studies of Diagnostic Accuracy - Revised (QUADAS-2). For each target condition, we used the bivariate model to estimate pooled sensitivity and specificity with 95% confidence intervals (CIs). We stratified all analyses by type of reference standard. We assessed certainty of evidence using the GRADE approach.

Main Results: For pulmonary tuberculosis, 299 data sets (68,544 participants) were available for analysis; for tuberculous meningitis, 10 data sets (423 participants) were available; for lymph node tuberculosis, 10 data sets (318 participants) were available; and for rifampicin resistance, 14 data sets (326 participants) were available. Thirty-nine studies (80%) took place in countries with high tuberculosis burden. Risk of bias was low except for the reference standard domain, for which risk of bias was unclear because many studies collected only one specimen for culture. Detection of pulmonary tuberculosis For sputum specimens, Xpert MTB/RIF pooled sensitivity (95% CI) and specificity (95% CI) verified by culture were 64.6% (55.3% to 72.9%) (23 studies, 493 participants; moderate-certainty evidence) and 99.0% (98.1% to 99.5%) (23 studies, 6119 participants; moderate-certainty evidence). For other specimen types (nasopharyngeal aspirate, 4 studies; gastric aspirate, 14 studies; stool, 11 studies), Xpert MTB/RIF pooled sensitivity ranged between 45.7% and 73.0%, and pooled specificity ranged between 98.1% and 99.6%. For sputum specimens, Xpert Ultra pooled sensitivity (95% CI) and specificity (95% CI) verified by culture were 72.8% (64.7% to 79.6%) (3 studies, 136 participants; low-certainty evidence) and 97.5% (95.8% to 98.5%) (3 studies, 551 participants; high-certainty evidence). For nasopharyngeal specimens, Xpert Ultra sensitivity (95% CI) and specificity (95% CI) were 45.7% (28.9% to 63.3%) and 97.5% (93.7% to 99.3%) (1 study, 195 participants). For all specimen types, Xpert MTB/RIF and Xpert Ultra sensitivity were lower against a composite reference standard than against culture. Detection of tuberculous meningitis For cerebrospinal fluid, Xpert MTB/RIF pooled sensitivity and specificity, verified by culture, were 54.0% (95% CI 27.8% to 78.2%) (6 studies, 28 participants; very low-certainty evidence) and 93.8% (95% CI 84.5% to 97.6%) (6 studies, 213 participants; low-certainty evidence). Detection of lymph node tuberculosis For lymph node aspirates or biopsies, Xpert MTB/RIF pooled sensitivity and specificity, verified by culture, were 90.4% (95% CI 55.7% to 98.6%) (6 studies, 68 participants; very low-certainty evidence) and 89.8% (95% CI 71.5% to 96.8%) (6 studies, 142 participants; low-certainty evidence). Detection of rifampicin resistance Xpert MTB/RIF pooled sensitivity and specificity were 90.0% (67.6% to 97.5%) (6 studies, 20 participants; low-certainty evidence) and 98.3% (87.7% to 99.8%) (6 studies, 203 participants; moderate-certainty evidence).

Authors' Conclusions: We found Xpert MTB/RIF sensitivity to vary by specimen type, with gastric aspirate specimens having the highest sensitivity followed by sputum and stool, and nasopharyngeal specimens the lowest; specificity in all specimens was > 98%. Compared with Xpert MTB/RIF, Xpert Ultra sensitivity in sputum was higher and specificity slightly lower. Xpert MTB/RIF was accurate for detection of rifampicin resistance. Xpert MTB/RIF was sensitive for diagnosing lymph node tuberculosis. For children with presumed tuberculous meningitis, treatment decisions should be based on the entirety of clinical information and treatment should not be withheld based solely on an Xpert MTB/RIF result. The small numbers of studies and participants, particularly for Xpert Ultra, limits our confidence in the precision of these estimates.
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http://dx.doi.org/10.1002/14651858.CD013359.pub2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078611PMC
August 2020

Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection.

Cochrane Database Syst Rev 2020 08 26;8:CD013705. Epub 2020 Aug 26.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify or rule out current infection, identify people in need of care escalation, or to test for past infection and immune response. Point-of-care antigen and molecular tests to detect current SARS-CoV-2 infection have the potential to allow earlier detection and isolation of confirmed cases compared to laboratory-based diagnostic methods, with the aim of reducing household and community transmission.

Objectives: To assess the diagnostic accuracy of point-of-care antigen and molecular-based tests to determine if a person presenting in the community or in primary or secondary care has current SARS-CoV-2 infection.

Search Methods: On 25 May 2020 we undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

Selection Criteria: We included studies of people with suspected current SARS-CoV-2 infection, known to have, or not to have SARS-CoV-2 infection, or where tests were used to screen for infection. We included test accuracy studies of any design that evaluated antigen or molecular tests suitable for a point-of-care setting (minimal equipment, sample preparation, and biosafety requirements, with results available within two hours of sample collection). We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction (RT-PCR) tests and established clinical diagnostic criteria).

Data Collection And Analysis: Two review authors independently screened studies and resolved any disagreements by discussion with a third review author. One review author independently extracted study characteristics, which were checked by a second review author. Two review authors independently extracted 2x2 contingency table data and assessed risk of bias and applicability of the studies using the QUADAS-2 tool. We present sensitivity and specificity, with 95% confidence intervals (CIs), for each test using paired forest plots. We pooled data using the bivariate hierarchical model separately for antigen and molecular-based tests, with simplifications when few studies were available. We tabulated available data by test manufacturer.

Main Results: We included 22 publications reporting on a total of 18 study cohorts with 3198 unique samples, of which 1775 had confirmed SARS-CoV-2 infection. Ten studies took place in North America, two in South America, four in Europe, one in China and one was conducted internationally. We identified data for eight commercial tests (four antigen and four molecular) and one in-house antigen test. Five of the studies included were only available as preprints. We did not find any studies at low risk of bias for all quality domains and had concerns about applicability of results across all studies. We judged patient selection to be at high risk of bias in 50% of the studies because of deliberate over-sampling of samples with confirmed COVID-19 infection and unclear in seven out of 18 studies because of poor reporting. Sixteen (89%) studies used only a single, negative RT-PCR to confirm the absence of COVID-19 infection, risking missing infection. There was a lack of information on blinding of index test (n = 11), and around participant exclusions from analyses (n = 10). We did not observe differences in methodological quality between antigen and molecular test evaluations. Antigen tests Sensitivity varied considerably across studies (from 0% to 94%): the average sensitivity was 56.2% (95% CI 29.5 to 79.8%) and average specificity was 99.5% (95% CI 98.1% to 99.9%; based on 8 evaluations in 5 studies on 943 samples). Data for individual antigen tests were limited with no more than two studies for any test. Rapid molecular assays Sensitivity showed less variation compared to antigen tests (from 68% to 100%), average sensitivity was 95.2% (95% CI 86.7% to 98.3%) and specificity 98.9% (95% CI 97.3% to 99.5%) based on 13 evaluations in 11 studies of on 2255 samples. Predicted values based on a hypothetical cohort of 1000 people with suspected COVID-19 infection (with a prevalence of 10%) result in 105 positive test results including 10 false positives (positive predictive value 90%), and 895 negative results including 5 false negatives (negative predictive value 99%). Individual tests We calculated pooled results of individual tests for ID NOW (Abbott Laboratories) (5 evaluations) and Xpert Xpress (Cepheid Inc) (6 evaluations). Summary sensitivity for the Xpert Xpress assay (99.4%, 95% CI 98.0% to 99.8%) was 22.6 (95% CI 18.8 to 26.3) percentage points higher than that of ID NOW (76.8%, (95% CI 72.9% to 80.3%), whilst the specificity of Xpert Xpress (96.8%, 95% CI 90.6% to 99.0%) was marginally lower than ID NOW (99.6%, 95% CI 98.4% to 99.9%; a difference of -2.8% (95% CI -6.4 to 0.8)) AUTHORS' CONCLUSIONS: This review identifies early-stage evaluations of point-of-care tests for detecting SARS-CoV-2 infection, largely based on remnant laboratory samples. The findings currently have limited applicability, as we are uncertain whether tests will perform in the same way in clinical practice, and according to symptoms of COVID-19, duration of symptoms, or in asymptomatic people. Rapid tests have the potential to be used to inform triage of RT-PCR use, allowing earlier detection of those testing positive, but the evidence currently is not strong enough to determine how useful they are in clinical practice. Prospective and comparative evaluations of rapid tests for COVID-19 infection in clinically relevant settings are urgently needed. Studies should recruit consecutive series of eligible participants, including both those presenting for testing due to symptoms and asymptomatic people who may have come into contact with confirmed cases. Studies should clearly describe symptomatic status and document time from symptom onset or time since exposure. Point-of-care tests must be conducted on samples according to manufacturer instructions for use and be conducted at the point of care. Any future research study report should conform to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline.
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http://dx.doi.org/10.1002/14651858.CD013705DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078202PMC
August 2020

Risk of bias assessment of test comparisons was uncommon in comparative accuracy systematic reviews: an overview of reviews.

J Clin Epidemiol 2020 11 13;127:167-174. Epub 2020 Aug 13.

Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.

Objectives: Comparative diagnostic test accuracy systematic reviews (DTA reviews) assess the accuracy of two or more tests and compare their diagnostic performance. We investigated how comparative DTA reviews assessed the risk of bias (RoB) in primary studies that compared multiple index tests.

Study Design And Setting: This is an overview of comparative DTA reviews indexed in MEDLINE from January 1st to December 31st, 2017. Two assessors independently identified DTA reviews including at least two index tests and containing at least one statement in which the accuracy of the index tests was compared. Two assessors independently extracted data on the methods used to assess RoB in studies that directly compared the accuracy of multiple index tests.

Results: We included 238 comparative DTA reviews. Only two reviews (0.8%, 95% confidence interval 0.1 to 3.0%) conducted RoB assessment of test comparisons undertaken in primary studies; neither used an RoB tool specifically designed to assess bias in test comparisons.

Conclusion: Assessment of RoB in test comparisons undertaken in primary studies was uncommon in comparative DTA reviews, possibly due to lack of existing guidance on and awareness of potential sources of bias. Based on our findings, guidance on how to assess and incorporate RoB in comparative DTA reviews is needed.
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November 2020

Chest ultrasonography versus supine chest radiography for diagnosis of pneumothorax in trauma patients in the emergency department.

Cochrane Database Syst Rev 2020 07 23;7:CD013031. Epub 2020 Jul 23.

Department of Emergency Medicine, Beaumont Hospital, Dublin, Ireland.

Background: Chest X-ray (CXR) is a longstanding method for the diagnosis of pneumothorax but chest ultrasonography (CUS) may be a safer, more rapid, and more accurate modality in trauma patients at the bedside that does not expose the patient to ionizing radiation. This may lead to improved and expedited management of traumatic pneumothorax and improved patient safety and clinical outcomes.

Objectives: To compare the diagnostic accuracy of chest ultrasonography (CUS) by frontline non-radiologist physicians versus chest X-ray (CXR) for diagnosis of pneumothorax in trauma patients in the emergency department (ED). To investigate the effects of potential sources of heterogeneity such as type of CUS operator (frontline non-radiologist physicians), type of trauma (blunt vs penetrating), and type of US probe on test accuracy.

Search Methods: We conducted a comprehensive search of the following electronic databases from database inception to 10 April 2020: Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Plus, Database of Abstracts of Reviews of Effects, Web of Science Core Collection and Clinicaltrials.gov. We handsearched reference lists of included articles and reviews retrieved via electronic searching; and we carried out forward citation searching of relevant articles in Google Scholar and looked at the "Related articles" on PubMed.

Selection Criteria: We included prospective, paired comparative accuracy studies comparing CUS performed by frontline non-radiologist physicians to supine CXR in trauma patients in the emergency department (ED) suspected of having pneumothorax, and with computed tomography (CT) of the chest or tube thoracostomy as the reference standard.

Data Collection And Analysis: Two review authors independently extracted data from each included study using a data extraction form. We included studies using patients as the unit of analysis in the main analysis and we included those using lung fields in the secondary analysis. We performed meta-analyses by using a bivariate model to estimate and compare summary sensitivities and specificities.

Main Results: We included 13 studies of which nine (410 traumatic pneumothorax patients out of 1271 patients) used patients as the unit of analysis; we thus included them in the primary analysis. The remaining four studies used lung field as the unit of analysis and we included them in the secondary analysis. We judged all studies to be at high or unclear risk of bias in one or more domains, with most studies (11/13, 85%) being judged at high or unclear risk of bias in the patient selection domain. There was substantial heterogeneity in the sensitivity of supine CXR amongst the included studies. In the primary analysis, the summary sensitivity and specificity of CUS were 0.91 (95% confidence interval (CI) 0.85 to 0.94) and 0.99 (95% CI 0.97 to 1.00); and the summary sensitivity and specificity of supine CXR were 0.47 (95% CI 0.31 to 0.63) and 1.00 (95% CI 0.97 to 1.00). There was a significant difference in the sensitivity of CUS compared to CXR with an absolute difference in sensitivity of 0.44 (95% CI 0.27 to 0.61; P < 0.001). In contrast, CUS and CXR had similar specificities: comparing CUS to CXR, the absolute difference in specificity was -0.007 (95% CI -0.018 to 0.005, P = 0.35). The findings imply that in a hypothetical cohort of 100 patients if 30 patients have traumatic pneumothorax (i.e. prevalence of 30%), CUS would miss 3 (95% CI 2 to 4) cases (false negatives) and overdiagnose 1 (95% CI 0 to 2) of those without pneumothorax (false positives); while CXR would miss 16 (95% CI 11 to 21) cases with 0 (95% CI 0 to 2) overdiagnosis of those who do not have pneumothorax.

Authors' Conclusions: The diagnostic accuracy of CUS performed by frontline non-radiologist physicians for the diagnosis of pneumothorax in ED trauma patients is superior to supine CXR, independent of the type of trauma, type of CUS operator, or type of CUS probe used. These findings suggest that CUS for the diagnosis of traumatic pneumothorax should be incorporated into trauma protocols and algorithms in future medical training programmes; and that CUS may beneficially change routine management of trauma.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390330PMC
July 2020

Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease.

Cochrane Database Syst Rev 2020 07 7;7:CD013665. Epub 2020 Jul 7.

NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.

Background: Some people with SARS-CoV-2 infection remain asymptomatic, whilst in others the infection can cause mild to moderate COVID-19 disease and COVID-19 pneumonia, leading some patients to require intensive care support and, in some cases, to death, especially in older adults. Symptoms such as fever or cough, and signs such as oxygen saturation or lung auscultation findings, are the first and most readily available diagnostic information. Such information could be used to either rule out COVID-19 disease, or select patients for further diagnostic testing.

Objectives: To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in primary care or to hospital outpatient settings, such as the emergency department or dedicated COVID-19 clinics, has COVID-19 disease or COVID-19 pneumonia.

Search Methods: On 27 April 2020, we undertook electronic searches in the Cochrane COVID-19 Study Register and the University of Bern living search database, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

Selection Criteria: Studies were eligible if they included patients with suspected COVID-19 disease, or if they recruited known cases with COVID-19 disease and controls without COVID-19. Studies were eligible when they recruited patients presenting to primary care or hospital outpatient settings. Studies including patients who contracted SARS-CoV-2 infection while admitted to hospital were not eligible. The minimum eligible sample size of studies was 10 participants. All signs and symptoms were eligible for this review, including individual signs and symptoms or combinations. We accepted a range of reference standards including reverse transcription polymerase chain reaction (RT-PCR), clinical expertise, imaging, serology tests and World Health Organization (WHO) or other definitions of COVID-19.

Data Collection And Analysis: Pairs of review authors independently selected all studies, at both title and abstract stage and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and resolved disagreements by discussion with a third review author. Two review authors independently assessed risk of bias using the QUADAS-2 checklist. Analyses were descriptive, presenting sensitivity and specificity in paired forest plots, in ROC (receiver operating characteristic) space and in dumbbell plots. We did not attempt meta-analysis due to the small number of studies, heterogeneity across studies and the high risk of bias.

Main Results: We identified 16 studies including 7706 participants in total. Prevalence of COVID-19 disease varied from 5% to 38% with a median of 17%. There were no studies from primary care settings, although we did find seven studies in outpatient clinics (2172 participants), and four studies in the emergency department (1401 participants). We found data on 27 signs and symptoms, which fall into four different categories: systemic, respiratory, gastrointestinal and cardiovascular. No studies assessed combinations of different signs and symptoms and results were highly variable across studies. Most had very low sensitivity and high specificity; only six symptoms had a sensitivity of at least 50% in at least one study: cough, sore throat, fever, myalgia or arthralgia, fatigue, and headache. Of these, fever, myalgia or arthralgia, fatigue, and headache could be considered red flags (defined as having a positive likelihood ratio of at least 5) for COVID-19 as their specificity was above 90%, meaning that they substantially increase the likelihood of COVID-19 disease when present. Seven studies carried a high risk of bias for selection of participants because inclusion in the studies depended on the applicable testing and referral protocols, which included many of the signs and symptoms under study in this review. Five studies only included participants with pneumonia on imaging, suggesting that this is a highly selected population. In an additional four studies, we were unable to assess the risk for selection bias. These factors make it very difficult to determine the diagnostic properties of these signs and symptoms from the included studies. We also had concerns about the applicability of these results, since most studies included participants who were already admitted to hospital or presenting to hospital settings. This makes these findings less applicable to people presenting to primary care, who may have less severe illness and a lower prevalence of COVID-19 disease. None of the studies included any data on children, and only one focused specifically on older adults. We hope that future updates of this review will be able to provide more information about the diagnostic properties of signs and symptoms in different settings and age groups.

Authors' Conclusions: The individual signs and symptoms included in this review appear to have very poor diagnostic properties, although this should be interpreted in the context of selection bias and heterogeneity between studies. Based on currently available data, neither absence nor presence of signs or symptoms are accurate enough to rule in or rule out disease. Prospective studies in an unselected population presenting to primary care or hospital outpatient settings, examining combinations of signs and symptoms to evaluate the syndromic presentation of COVID-19 disease, are urgently needed. Results from such studies could inform subsequent management decisions such as self-isolation or selecting patients for further diagnostic testing. We also need data on potentially more specific symptoms such as loss of sense of smell. Studies in older adults are especially important.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386785PMC
July 2020

mary care anagement of lower rinary tract ymptoms in men: protocol for development and validation of a diagnostic and clinical decision support tool (the PriMUS study).

BMJ Open 2020 06 30;10(6):e037634. Epub 2020 Jun 30.

Division of Population Medicine, Cardiff University, Cardiff, South Glamorgan, UK.

Introduction: Lower urinary tract symptoms (LUTS) is a bothersome condition affecting older men which can lead to poor quality of life. General practitioners (GPs) currently have no easily available assessment tools to help effectively diagnose causes of LUTS and aid discussion of treatment with patients. Men are frequently referred to urology specialists who often recommend treatments that could have been initiated in primary care. GP access to simple, accurate tests and clinician decision tools are needed to facilitate accurate and effective patient management of LUTS in primary care.

Methods And Analysis: PRImary care Management of lower Urinary tract Symptoms (PriMUS) is a prospective diagnostic accuracy study based in primary care. The study will determine which of a number of index tests used in combination best predict three urodynamic observations in men who present to their GP with LUTS. These are detrusor overactivity, bladder outlet obstruction and/or detrusor underactivity. Two cohorts of participants, one for development of the prototype diagnostic tool and other for validation, will undergo a series of simple index tests and the invasive reference standard (invasive urodynamics). We will develop and validate three diagnostic prediction models based on each condition and then combine them with management recommendations to form a clinical decision support tool.

Ethics And Dissemination: Ethics approval is from the Wales Research Ethics Committee 6. Findings will be disseminated through peer-reviewed journals and conferences, and results will be of interest to professional and patient stakeholders.

Trial Registration Number: ISRCTN10327305.
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http://dx.doi.org/10.1136/bmjopen-2020-037634DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7328815PMC
June 2020

Antibody tests for identification of current and past infection with SARS-CoV-2.

Cochrane Database Syst Rev 2020 06 25;6:CD013652. Epub 2020 Jun 25.

Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS-CoV-2 aim to identify previous SARS-CoV-2 infection, and may help to confirm the presence of current infection.

Objectives: To assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection, and the accuracy of antibody tests for use in seroprevalence surveys.

Search Methods: We undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020.

Selection Criteria: We included test accuracy studies of any design that evaluated antibody tests (including enzyme-linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS-CoV-2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS-CoV-2 infection. We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR) and clinical diagnostic criteria).

Data Collection And Analysis: We assessed possible bias and applicability of the studies using the QUADAS-2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random-effects logistic regression where appropriate, stratifying by time since post-symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs).

Main Results: We included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS-CoV-2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in-house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi-group designs (n = 29), inclusion of only COVID-19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID-19 infection. There were no studies exclusively in asymptomatic participants. Two-thirds of the studies (n = 33) defined COVID-19 cases based on RT-PCR results alone, ignoring the potential for false-negative RT-PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post-symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post-symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False-positive results were more common where COVID-19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post-symptom onset. At a prevalence of 20%, a likely value in surveys in high-risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands.

Authors' Conclusions: The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease. The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.
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http://dx.doi.org/10.1002/14651858.CD013652DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7387103PMC
June 2020

Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models.

Europace 2020 05;22(5):748-760

Institute of Applied Health Research, University of Birmingham, Birmingham B15 2TT, UK.

Aims: We assessed the performance of modelsf (risk scores) for predicting recurrence of atrial fibrillation (AF) in patients who have undergone catheter ablation.

Methods And Results: Systematic searches of bibliographic databases were conducted (November 2018). Studies were eligible for inclusion if they reported the development, validation, or impact assessment of a model for predicting AF recurrence after ablation. Model performance (discrimination and calibration) measures were extracted. The Prediction Study Risk of Bias Assessment Tool (PROBAST) was used to assess risk of bias. Meta-analysis was not feasible due to clinical and methodological differences between studies, but c-statistics were presented in forest plots. Thirty-three studies developing or validating 13 models were included; eight studies compared two or more models. Common model variables were left atrial parameters, type of AF, and age. Model discriminatory ability was highly variable and no model had consistently poor or good performance. Most studies did not assess model calibration. The main risk of bias concern was the lack of internal validation which may have resulted in overly optimistic and/or biased model performance estimates. No model impact studies were identified.

Conclusion: Our systematic review suggests that clinical risk prediction of AF after ablation has potential, but there remains a need for robust evaluation of risk factors and development of risk scores.
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http://dx.doi.org/10.1093/europace/euaa041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203634PMC
May 2020

Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies.

BMJ 2020 02 10;368:m127. Epub 2020 Feb 10.

Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

Objective: To examine the validity and findings of studies that examine the accuracy of algorithm based smartphone applications ("apps") to assess risk of skin cancer in suspicious skin lesions.

Design: Systematic review of diagnostic accuracy studies.

Data Sources: Cochrane Central Register of Controlled Trials, MEDLINE, Embase, CINAHL, CPCI, Zetoc, Science Citation Index, and online trial registers (from database inception to 10 April 2019).

Eligibility Criteria For Selecting Studies: Studies of any design that evaluated algorithm based smartphone apps to assess images of skin lesions suspicious for skin cancer. Reference standards included histological diagnosis or follow-up, and expert recommendation for further investigation or intervention. Two authors independently extracted data and assessed validity using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2 tool). Estimates of sensitivity and specificity were reported for each app.

Results: Nine studies that evaluated six different identifiable smartphone apps were included. Six verified results by using histology or follow-up (n=725 lesions), and three verified results by using expert recommendations (n=407 lesions). Studies were small and of poor methodological quality, with selective recruitment, high rates of unevaluable images, and differential verification. Lesion selection and image acquisition were performed by clinicians rather than smartphone users. Two CE (Conformit Europenne) marked apps are available for download. SkinScan was evaluated in a single study (n=15, five melanomas) with 0% sensitivity and 100% specificity for the detection of melanoma. SkinVision was evaluated in two studies (n=252, 61 malignant or premalignant lesions) and achieved a sensitivity of 80% (95% confidence interval 63% to 92%) and a specificity of 78% (67% to 87%) for the detection of malignant or premalignant lesions. Accuracy of the SkinVision app verified against expert recommendations was poor (three studies).

Conclusions: Current algorithm based smartphone apps cannot be relied on to detect all cases of melanoma or other skin cancers. Test performance is likely to be poorer than reported here when used in clinically relevant populations and by the intended users of the apps. The current regulatory process for awarding the CE marking for algorithm based apps does not provide adequate protection to the public.

Systematic Review Registration: PROSPERO CRD42016033595.
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http://dx.doi.org/10.1136/bmj.m127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190019PMC
February 2020
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