Search

Although 38% of respondents (26 of 69) reported patients using smartphone apps (including MySkinSelfie and Miiskin) to monitor their lesions, only 19% (13 of 69) recommended apps to monitor skin lesions to their patients. Total body photography (TBP) is increasingly used to monitor skin lesions in individuals at high risk of melanoma. [Extracted from the article]

Background: Pressure injuries (PIs) place a substantial burden on healthcare systems worldwide. Risk stratification of those who are at risk of developing PIs allows preventive interventions to be focused on patients who are at the highest risk. The considerable number of risk assessment scales and prediction models available underscores the need for a thorough evaluation of their development, validation, and clinical utility. Our objectives were to identify and describe available risk prediction tools for PI occurrence, their content and the development and validation methods used., Methods: The umbrella review was conducted according to Cochrane guidance. MEDLINE, Embase, CINAHL, EPISTEMONIKOS, Google Scholar, and reference lists were searched to identify relevant systematic reviews. The risk of bias was assessed using adapted AMSTAR-2 criteria. Results were described narratively. All included reviews contributed to building a comprehensive list of risk prediction tools., Results: We identified 32 eligible systematic reviews only seven of which described the development and validation of risk prediction tools for PI. Nineteen reviews assessed the prognostic accuracy of the tools and 11 assessed clinical effectiveness. Of the seven reviews reporting model development and validation, six included only machine learning models. Two reviews included external validations of models, although only one review reported any details on external validation methods or results. This was also the only review to report measures of both discrimination and calibration. Five reviews presented measures of discrimination, such as the area under the curve (AUC), sensitivities, specificities, F1 scores, and G-means. For the four reviews that assessed the risk of bias assessment using the PROBAST tool, all models but one were found to be at high or unclear risk of bias., Conclusions: Available tools do not meet current standards for the development or reporting of risk prediction models. The majority of tools have not been externally validated. Standardised and rigorous approaches to risk prediction model development and validation are needed., Trial Registration: The protocol was registered on the Open Science Framework ( https://osf.io/tepyk )., Competing Interests: Declarations. Ethics approval and consent to participate.: Not applicable. Consent for publication: Not applicable. Competing interests: The authors of this manuscript have the following competing interests: VV is an employee of Paul Hartmann AG; ES and THB received consultancy fees from Paul Hartmann AG. VV, ES and THB were not involved in data curation, screening, data extraction, analysis of results or writing of the original draft. These roles were conducted independently by authors at the University of Birmingham. All other authors received no personal funding or personal compensation from Paul Hartmann AG and have declared that no competing interests exist., (© 2024. The Author(s).)

Background: The aim of the study was to investigate the development of evidence-based monitoring strategies in a population with progressive or recurrent disease. A simulation study of monitoring strategies using a new biomarker (ELF) for the detection of liver cirrhosis in people with known liver fibrosis was undertaken alongside a randomised controlled trial (ELUCIDATE)., Methods: Existing data and expert opinion were used to estimate the progression of disease and the performance of repeat testing with ELF. Knowledge of the true disease status in addition to the observed test results for a cohort of simulated patients allowed various monitoring strategies to be implemented, evaluated and validated against trial data., Results: Several monitoring strategies ranging in complexity were successfully modelled and compared regarding the timing of detection of disease, the duration of monitoring, and the predictive value of a positive test result. The results of sensitivity analysis showed the importance of accurate data to inform the simulation. Results of the simulation were similar to those from the trial., Conclusion: Monitoring data can be simulated and strategies compared given adequate knowledge of disease progression and test performance. Such exercises should be carried out to ensure optimal strategies are evaluated in trials thus reducing research waste. Monitoring data can be generated and monitoring strategies can be assessed if data is available on the monitoring test performance and the test variability. This work highlights the data necessary and the general method for evaluating the performance of monitoring strategies, allowing appropriate strategies to be selected for evaluation. Modelling work should be conducted prior to full scale investigation of monitoring strategies, allowing optimal monitoring strategies to be assessed., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Julie Parkes has been paid for providing lectures by Siemens on the topic of the ELF marker. Julie Parkes reported receiving support from the Speakers’ Bureau for Siemens Healthcare Diagnostics, outside the submitted work, and is married to William Rosenberg. William Rosenberg was an inventor of the enhanced liver fibrosis (ELF) test when he was an employee of the University of Southampton. His rights were transferred to Siemens Healthcare Diagnostics Ltd by the University of Southampton. He does not receive any payment in relation to sales of the test by the manufacturer Siemens Healthcare Diagnostics. He has received grant support and speaker fees from Siemens Healthcare Diagnostics and is a director of iQur Ltd (Southampton, UK), a company that provides ELF testing. In the context of this NIHR-funded study, all ELF testing provided by iQur Ltd was performed on a not-for-profit, cost-recovery basis. During the period of the study Jonathan J Deeks was a panel member of the HTA Commissioning Board. Jenny Hewison was a panel member of the National Institute for Health Research (NIHR) Clinical Trials Unit Standing Advisory Committee and Subpanel Chair, NIHR Programme Grants for Applied Research. Walter Gregory was the ELUCIDATE Trial Director and Principal Statistician. Alice Sitch and Jacqueline Dinnes report no competing interests., (© 2024. The Author(s).)

Background: Sample collection is a key driver of accuracy in the diagnosis of SARS-CoV-2 infection. Viral load may vary at different anatomical sampling sites and accuracy may be compromised by difficulties obtaining specimens and the expertise of the person taking the sample. It is important to optimise sampling accuracy within cost, safety and accessibility constraints., Objectives: To compare the sensitivity of different sampling collection sites and methods for the detection of current SARS-CoV-2 infection with any molecular or antigen-based test., 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 22 February 2022. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. We did not apply language restrictions., Selection Criteria: We included studies of symptomatic or asymptomatic people with suspected SARS-CoV-2 infection undergoing testing. We included studies of any design that compared results from different sample types (anatomical location, operator, collection device) collected from the same participant within a 24-hour period., Data Collection and Analysis: Within a sample pair, we defined a reference sample and an index sample collected from the same participant within the same clinical encounter (within 24 hours). Where the sample comparison was different anatomical sites, the reference standard was defined as a nasopharyngeal or combined naso/oropharyngeal sample collected into the same sample container and the index sample as the alternative anatomical site. Where the sample comparison was concerned with differences in the sample collection method from the same site, we defined the reference sample as that closest to standard practice for that sample type. Where the sample pair comparison was concerned with differences in personnel collecting the sample, the more skilled or experienced operator was considered the reference sample. Two review authors independently assessed the risk of bias and applicability concerns using the QUADAS-2 and QUADAS-C checklists, tailored to this review. We present estimates of the difference in the sensitivity (reference sample (%) minus index sample sensitivity (%)) in a pair and as an average across studies for each index sampling method using forest plots and tables. We examined heterogeneity between studies according to population (age, symptom status) and index sample (time post-symptom onset, operator expertise, use of transport medium) characteristics., Main Results: This review includes 106 studies reporting 154 evaluations and 60,523 sample pair comparisons, of which 11,045 had SARS-CoV-2 infection. Ninety evaluations were of saliva samples, 37 nasal, seven oropharyngeal, six gargle, six oral and four combined nasal/oropharyngeal samples. Four evaluations were of the effect of operator expertise on the accuracy of three different sample types. The majority of included evaluations (146) used molecular tests, of which 140 used RT-PCR (reverse transcription polymerase chain reaction). Eight evaluations were of nasal samples used with Ag-RDTs (rapid antigen tests). The majority of studies were conducted in Europe (35/106, 33%) or the USA (27%) and conducted in dedicated COVID-19 testing clinics or in ambulatory hospital settings (53%). Targeted screening or contact tracing accounted for only 4% of evaluations. Where reported, the majority of evaluations were of adults (91/154, 59%), 28 (18%) were in mixed populations with only seven (4%) in children. The median prevalence of confirmed SARS-CoV-2 was 23% (interquartile (IQR) 13%-40%). Risk of bias and applicability assessment were hampered by poor reporting in 77% and 65% of included studies, respectively. Risk of bias was low across all domains in only 3% of evaluations due to inappropriate inclusion or exclusion criteria, unclear recruitment, lack of blinding, nonrandomised sampling order or differences in testing kit within a sample pair. Sixty-eight percent of evaluation cohorts were judged as being at high or unclear applicability concern either due to inflation of the prevalence of SARS-CoV-2 infection in study populations by selectively including individuals with confirmed PCR-positive samples or because there was insufficient detail to allow replication of sample collection. When used with RT-PCR • There was no evidence of a difference in sensitivity between gargle and nasopharyngeal samples (on average -1 percentage points, 95% CI -5 to +2, based on 6 evaluations, 2138 sample pairs, of which 389 had SARS-CoV-2). • There was no evidence of a difference in sensitivity between saliva collection from the deep throat and nasopharyngeal samples (on average +10 percentage points, 95% CI -1 to +21, based on 2192 sample pairs, of which 730 had SARS-CoV-2). • There was evidence that saliva collection using spitting, drooling or salivating was on average -12 percentage points less sensitive (95% CI -16 to -8, based on 27,253 sample pairs, of which 4636 had SARS-CoV-2) compared to nasopharyngeal samples. We did not find any evidence of a difference in the sensitivity of saliva collected using spitting, drooling or salivating (sensitivity difference: range from -13 percentage points (spit) to -21 percentage points (salivate)). • Nasal samples (anterior and mid-turbinate collection combined) were, on average, 12 percentage points less sensitive compared to nasopharyngeal samples (95% CI -17 to -7), based on 9291 sample pairs, of which 1485 had SARS-CoV-2. We did not find any evidence of a difference in sensitivity between nasal samples collected from the mid-turbinates (3942 sample pairs) or from the anterior nares (8272 sample pairs). • There was evidence that oropharyngeal samples were, on average, 17 percentage points less sensitive than nasopharyngeal samples (95% CI -29 to -5), based on seven evaluations, 2522 sample pairs, of which 511 had SARS-CoV-2. A much smaller volume of evidence was available for combined nasal/oropharyngeal samples and oral samples. Age, symptom status and use of transport media do not appear to affect the sensitivity of saliva samples and nasal samples. When used with Ag-RDTs • There was no evidence of a difference in sensitivity between nasal samples compared to nasopharyngeal samples (sensitivity, on average, 0 percentage points -0.2 to +0.2, based on 3688 sample pairs, of which 535 had SARS-CoV-2)., Authors' Conclusions: When used with RT-PCR, there is no evidence for a difference in sensitivity of self-collected gargle or deep-throat saliva samples compared to nasopharyngeal samples collected by healthcare workers when used with RT-PCR. Use of these alternative, self-collected sample types has the potential to reduce cost and discomfort and improve the safety of sampling by reducing risk of transmission from aerosol spread which occurs as a result of coughing and gagging during the nasopharyngeal or oropharyngeal sample collection procedure. This may, in turn, improve access to and uptake of testing. Other types of saliva, nasal, oral and oropharyngeal samples are, on average, less sensitive compared to healthcare worker-collected nasopharyngeal samples, and it is unlikely that sensitivities of this magnitude would be acceptable for confirmation of SARS-CoV-2 infection with RT-PCR. When used with Ag-RDTs, there is no evidence of a difference in sensitivity between nasal samples and healthcare worker-collected nasopharyngeal samples for detecting SARS-CoV-2. The implications of this for self-testing are unclear as evaluations did not report whether nasal samples were self-collected or collected by healthcare workers. Further research is needed in asymptomatic individuals, children and in Ag-RDTs, and to investigate the effect of operator expertise on accuracy. Quality assessment of the evidence base underpinning these conclusions was restricted by poor reporting. There is a need for further high-quality studies, adhering to reporting standards for test accuracy studies., (Copyright © 2024 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Background: Diagnosing people with a SARS-CoV-2 infection played a critical role in managing the COVID-19 pandemic and remains a priority for the transition to long-term management of COVID-19. Initial shortages of extraction and reverse transcription polymerase chain reaction (RT-PCR) reagents impaired the desired upscaling of testing in many countries, which led to the search for alternatives to RNA extraction/purification and RT-PCR testing. Reference standard methods for diagnosing the presence of SARS-CoV-2 infection rely primarily on real-time reverse transcription-polymerase chain reaction (RT-PCR). Alternatives to RT-PCR could, if sufficiently accurate, have a positive impact by expanding the range of diagnostic tools available for the timely identification of people infected by SARS-CoV-2, access to testing and the use of resources., Objectives: To assess the diagnostic accuracy of alternative (to RT-PCR assays) laboratory-based molecular tests for diagnosing SARS-CoV-2 infection., Search Methods: We searched the COVID-19 Open Access Project living evidence database from the University of Bern until 30 September 2020 and the WHO COVID-19 Research Database until 31 October 2022. We did not apply language restrictions., Selection Criteria: We included studies of people with suspected or known SARS-CoV-2 infection, or where tests were used to screen for infection, and studies evaluating commercially developed laboratory-based molecular tests for the diagnosis of SARS-CoV-2 infection considered as alternatives to RT-PCR testing. We also included all reference standards to define the presence or absence of SARS-CoV-2, including RT-PCR tests and established clinical diagnostic criteria., Data Collection and Analysis: Two authors independently screened studies and resolved disagreements by discussing them with a third author. Two authors independently extracted data and assessed the risk of bias and applicability of the studies using the QUADAS-2 tool. We presented sensitivity and specificity, with 95% confidence intervals (CIs), for each test using paired forest plots and summarised results using average sensitivity and specificity using a bivariate random-effects meta-analysis. We illustrated the findings per index test category and assay brand compared to the WHO's acceptable sensitivity and specificity threshold for diagnosing SARS-CoV-2 infection using nucleic acid tests., Main Results: We included data from 64 studies reporting 94 cohorts of participants and 105 index test evaluations, with 74,753 samples and 7517 confirmed SARS-CoV-2 cases. We did not identify any published or preprint reports of accuracy for a considerable number of commercially produced NAAT assays. Most cohorts were judged at unclear or high risk of bias in more than three QUADAS-2 domains. Around half of the cohorts were considered at high risk of selection bias because of recruitment based on COVID status. Three quarters of 94 cohorts were at high risk of bias in the reference standard domain because of reliance on a single RT-PCR result to determine the absence of SARS-CoV-2 infection or were at unclear risk of bias due to a lack of clarity about the time interval between the index test assessment and the reference standard, the number of missing results, or the absence of a participant flow diagram. For index tests categories with four or more evaluations and when summary estimations were possible, we found that: a) For RT-PCR assays designed to omit/adapt RNA extraction/purification, the average sensitivity was 95.1% (95% CI 91.1% to 97.3%), and the average specificity was 99.7% (95% CI 98.5% to 99.9%; based on 27 evaluations, 2834 samples and 1178 SARS-CoV-2 cases); b) For RT-LAMP assays, the average sensitivity was 88.4% (95% CI 83.1% to 92.2%), and the average specificity was 99.7% (95% CI 98.7% to 99.9%; 24 evaluations, 29,496 samples and 2255 SARS-CoV-2 cases); c) for TMA assays, the average sensitivity was 97.6% (95% CI 95.2% to 98.8%), and the average specificity was 99.4% (95% CI 94.9% to 99.9%; 14 evaluations, 2196 samples and 942 SARS-CoV-2 cases); d) for digital PCR assays, the average sensitivity was 98.5% (95% CI 95.2% to 99.5%), and the average specificity was 91.4% (95% CI 60.4% to 98.7%; five evaluations, 703 samples and 354 SARS-CoV-2 cases); e) for RT-LAMP assays omitting/adapting RNA extraction, the average sensitivity was 73.1% (95% CI 58.4% to 84%), and the average specificity was 100% (95% CI 98% to 100%; 24 evaluations, 14,342 samples and 1502 SARS-CoV-2 cases). Only two index test categories fulfil the WHO-acceptable sensitivity and specificity requirements for SARS-CoV-2 nucleic acid tests: RT-PCR assays designed to omit/adapt RNA extraction/purification and TMA assays. In addition, WHO-acceptable performance criteria were met for two assays out of 35 when tests were used according to manufacturer instructions. At 5% prevalence using a cohort of 1000 people suspected of SARS-CoV-2 infection, the positive predictive value of RT-PCR assays omitting/adapting RNA extraction/purification will be 94%, with three in 51 positive results being false positives, and around two missed cases. For TMA assays, the positive predictive value of RT-PCR assays will be 89%, with 6 in 55 positive results being false positives, and around one missed case., Authors' Conclusions: Alternative laboratory-based molecular tests aim to enhance testing capacity in different ways, such as reducing the time, steps and resources needed to obtain valid results. Several index test technologies with these potential advantages have not been evaluated or have been assessed by only a few studies of limited methodological quality, so the performance of these kits was undetermined. Only two index test categories with enough evaluations for meta-analysis fulfil the WHO set of acceptable accuracy standards for SARS-CoV-2 nucleic acid tests: RT-PCR assays designed to omit/adapt RNA extraction/purification and TMA assays. These assays might prove to be suitable alternatives to RT-PCR for identifying people infected by SARS-CoV-2, especially when the alternative would be not having access to testing. However, these findings need to be interpreted and used with caution because of several limitations in the evidence, including reliance on retrospective samples without information about the symptom status of participants and the timing of assessment. No extrapolation of found accuracy data for these two alternatives to any test brands using the same techniques can be made as, for both groups, one test brand with high accuracy was overrepresented with 21/26 and 12/14 included studies, respectively. Although we used a comprehensive search and had broad eligibility criteria to include a wide range of tests that could be alternatives to RT-PCR methods, further research is needed to assess the performance of alternative COVID-19 tests and their role in pandemic management., (Copyright © 2024 The Cochrane Collaboration. Published by John Wiley & Sons, Ltd.)

Background: Identifying patients with COVID-19 disease who will deteriorate can be useful to assess whether they should receive intensive care, or whether they can be treated in a less intensive way or through outpatient care. In clinical care, routine laboratory markers, such as C-reactive protein, are used to assess a person's health status., Objectives: To assess the accuracy of routine blood-based laboratory tests to predict mortality and deterioration to severe or critical (from mild or moderate) COVID-19 in people with SARS-CoV-2., Search Methods: On 25 August 2022, we searched the Cochrane COVID-19 Study Register, encompassing searches of various databases such as MEDLINE via PubMed, CENTRAL, Embase, medRxiv, and ClinicalTrials.gov. We did not apply any language restrictions., Selection Criteria: We included studies of all designs that produced estimates of prognostic accuracy in participants who presented to outpatient services, or were admitted to general hospital wards with confirmed SARS-CoV-2 infection, and studies that were based on serum banks of samples from people. All routine blood-based laboratory tests performed during the first encounter were included. We included any reference standard used to define deterioration to severe or critical disease that was provided by the authors., Data Collection and Analysis: Two review authors independently extracted data from each included study, and independently assessed the methodological quality using the Quality Assessment of Prognostic Accuracy Studies tool. As studies reported different thresholds for the same test, we used the Hierarchical Summary Receiver Operator Curve model for meta-analyses to estimate summary curves in SAS 9.4. We estimated the sensitivity at points on the SROC curves that corresponded to the median and interquartile range boundaries of specificities in the included studies. Direct and indirect comparisons were exclusively conducted for biomarkers with an estimated sensitivity and 95% CI of ≥ 50% at a specificity of ≥ 50%. The relative diagnostic odds ratio was calculated as a summary of the relative accuracy of these biomarkers., Main Results: We identified a total of 64 studies, including 71,170 participants, of which 8169 participants died, and 4031 participants deteriorated to severe/critical condition. The studies assessed 53 different laboratory tests. For some tests, both increases and decreases relative to the normal range were included. There was important heterogeneity between tests and their cut-off values. None of the included studies had a low risk of bias or low concern for applicability for all domains. None of the tests included in this review demonstrated high sensitivity or specificity, or both. The five tests with summary sensitivity and specificity above 50% were: C-reactive protein increase, neutrophil-to-lymphocyte ratio increase, lymphocyte count decrease, d-dimer increase, and lactate dehydrogenase increase. Inflammation For mortality, summary sensitivity of a C-reactive protein increase was 76% (95% CI 73% to 79%) at median specificity, 59% (low-certainty evidence). For deterioration, summary sensitivity was 78% (95% CI 67% to 86%) at median specificity, 72% (very low-certainty evidence). For the combined outcome of mortality or deterioration, or both, summary sensitivity was 70% (95% CI 49% to 85%) at median specificity, 60% (very low-certainty evidence). For mortality, summary sensitivity of an increase in neutrophil-to-lymphocyte ratio was 69% (95% CI 66% to 72%) at median specificity, 63% (very low-certainty evidence). For deterioration, summary sensitivity was 75% (95% CI 59% to 87%) at median specificity, 71% (very low-certainty evidence). For mortality, summary sensitivity of a decrease in lymphocyte count was 67% (95% CI 56% to 77%) at median specificity, 61% (very low-certainty evidence). For deterioration, summary sensitivity of a decrease in lymphocyte count was 69% (95% CI 60% to 76%) at median specificity, 67% (very low-certainty evidence). For the combined outcome, summary sensitivity was 83% (95% CI 67% to 92%) at median specificity, 29% (very low-certainty evidence). For mortality, summary sensitivity of a lactate dehydrogenase increase was 82% (95% CI 66% to 91%) at median specificity, 60% (very low-certainty evidence). For deterioration, summary sensitivity of a lactate dehydrogenase increase was 79% (95% CI 76% to 82%) at median specificity, 66% (low-certainty evidence). For the combined outcome, summary sensitivity was 69% (95% CI 51% to 82%) at median specificity, 62% (very low-certainty evidence). Hypercoagulability For mortality, summary sensitivity of a d-dimer increase was 70% (95% CI 64% to 76%) at median specificity of 56% (very low-certainty evidence). For deterioration, summary sensitivity was 65% (95% CI 56% to 74%) at median specificity of 63% (very low-certainty evidence). For the combined outcome, summary sensitivity was 65% (95% CI 52% to 76%) at median specificity of 54% (very low-certainty evidence). To predict mortality, neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR (diagnostic Odds Ratio) 2.05, 95% CI 1.30 to 3.24), C-reactive protein increase (RDOR 2.64, 95% CI 2.09 to 3.33), and lymphocyte count decrease (RDOR 2.63, 95% CI 1.55 to 4.46). D-dimer increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.49, 95% CI 1.23 to 1.80), C-reactive protein increase (RDOR 1.31, 95% CI 1.03 to 1.65), and lactate dehydrogenase increase (RDOR 1.42, 95% CI 1.05 to 1.90). Additionally, lactate dehydrogenase increase had higher accuracy compared to lymphocyte count decrease (RDOR 1.30, 95% CI 1.13 to 1.49). To predict deterioration to severe disease, C-reactive protein increase had higher accuracy compared to d-dimer increase (RDOR 1.76, 95% CI 1.25 to 2.50). The neutrophil-to-lymphocyte ratio increase had higher accuracy compared to d-dimer increase (RDOR 2.77, 95% CI 1.58 to 4.84). Lastly, lymphocyte count decrease had higher accuracy compared to d-dimer increase (RDOR 2.10, 95% CI 1.44 to 3.07) and lactate dehydrogenase increase (RDOR 2.22, 95% CI 1.52 to 3.26)., Authors' Conclusions: Laboratory tests, associated with hypercoagulability and hyperinflammatory response, were better at predicting severe disease and mortality in patients with SARS-CoV-2 compared to other laboratory tests. However, to safely rule out severe disease, tests should have high sensitivity (> 90%), and none of the identified laboratory tests met this criterion. In clinical practice, a more comprehensive assessment of a patient's health status is usually required by, for example, incorporating these laboratory tests into clinical prediction rules together with clinical symptoms, radiological findings, and patient's characteristics., (Copyright © 2024 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Background: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation., Objective: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England., Methods: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote., Results: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024., Conclusions: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas., International Registered Report Identifier (irrid): DERR1-10.2196/50568., (©Trystan Macdonald, Jacqueline Dinnes, Gregory Maniatopoulos, Sian Taylor-Phillips, Bethany Shinkins, Jeffry Hogg, John Kevin Dunbar, Ameenat Lola Solebo, Hannah Sutton, John Attwood, Michael Pogose, Rosalind Given-Wilson, Felix Greaves, Carl Macrae, Russell Pearson, Daniel Bamford, Adnan Tufail, Xiaoxuan Liu, Alastair K Denniston. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 27.03.2024.)

Background: Rapid antigen tests (RATs) were crucial during the COVID-19 pandemic. Information provided by the test manufacturer in product package inserts, also known as instructions for use (IFUs), is often the only data available to clinicians, public health professionals, and individuals on the diagnostic accuracy of these tests. We aimed to assess whether manufacturer IFU accuracy data aligned with evidence from independent research., Methods: We searched company websites for package inserts for RATs that were included in the July 2022 update of the Cochrane meta-analysis of SARS-CoV-2 RATs, which served as a benchmark for research evidence. We fitted bivariate hierarchical models to obtain absolute differences in sensitivity and specificity between IFU and Cochrane Review estimates for each test, as well as overall combined differences., Findings: We found 22 (100%) of 22 IFUs of the RATs included in the Cochrane Review. IFUs for 12 (55%) of 22 RATs reported statistically significantly higher sensitivity estimates than the Cochrane Review, and none reported lower estimates. The mean difference between IFU and Cochrane Review sensitivity estimates across tests was 12·0% (95% CI 7·5-16·6). IFUs in three (14%) of 22 diagnostic tests had significantly higher specificity estimates than the Cochrane Review and two (9%) of 22 had lower estimates. The mean difference between IFU and Cochrane Review specificity estimates across tests was 0·3% (95% CI 0·1-0·5). If 100 people with SARS-CoV-2 infection were tested with each of the tests in this study, on average 12 fewer people would be correctly diagnosed than is suggested by the package inserts., Interpretation: Health professionals and the public should be aware that package inserts for SARS-CoV-2 RATs might provide an overly optimistic picture of the sensitivity of a test. Regulatory bodies should strengthen their requirements for the reporting of diagnostic accuracy data in package inserts and policy makers should demand independent validation data for decision making., Funding: None., Competing Interests: Declaration of interests JB has received consulting fees from the New Diagnostics Working Group of the Stop TB Partnership. SB, JD, and JJD are supported by the National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre. This Article presents independent research supported by the NIHR Birmingham Biomedical Research Centre at the University Hospitals Birmingham National Health Services (NHS) Foundation Trust and the University of Birmingham. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. CMD has received research grants from the US National Institutes of Health, German Ministry of Education and Research, German Alliance for Global Health Research, United States Agency for International Development, FIND, German Center for Infection Research, UNAIDS, WHO, and Roche. CMD also declares a payment from Roche Diagnostics that she accepted as German law requires a manufacturer to pay for the use of data for regulatory purposes. Data was generated as part of an independent evaluation by CMD and team. CMD is an academic editor of PLoS Medicine and sits on the WHO technical advisory group on tuberculosis diagnostics. MP holds a Canada Research Chair Award from the Canadian Institutes of Health Research and serves as an adviser to the following non-profit agencies in global health: Bill & Melinda Gates Foundation; Foundation for Innovative New Diagnostics, WHO, and Stop TB Partnership. All other authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Competing Interests: Conflicts of interest R.N.M. is Chair of the British Association of Dermatologists Artificial Intelligence Working Party Group.

Background: The global spread of COVID-19 created an explosion in rapid tests with results in < 1 hour, but their relative performance characteristics are not fully understood yet. Our aim was to determine the most sensitive and specific rapid test for the diagnosis of SARS-CoV-2., Methods: Design: Rapid review and diagnostic test accuracy network meta-analysis (DTA-NMA)., Eligibility Criteria: Randomized controlled trials (RCTs) and observational studies assessing rapid antigen and/or rapid molecular test(s) to detect SARS-CoV-2 in participants of any age, suspected or not with SARS-CoV-2 infection., Information Sources: Embase, MEDLINE, and Cochrane Central Register of Controlled Trials, up to September 12, 2021., Outcome Measures: Sensitivity and specificity of rapid antigen and molecular tests suitable for detecting SARS-CoV-2. Data extraction and risk of bias assessment: Screening of literature search results was conducted by one reviewer; data abstraction was completed by one reviewer and independently verified by a second reviewer. Risk of bias was not assessed in the included studies., Data Synthesis: Random-effects meta-analysis and DTA-NMA., Results: We included 93 studies (reported in 88 articles) relating to 36 rapid antigen tests in 104,961 participants and 23 rapid molecular tests in 10,449 participants. Overall, rapid antigen tests had a sensitivity of 0.75 (95% confidence interval 0.70-0.79) and specificity of 0.99 (0.98-0.99). Rapid antigen test sensitivity was higher when nasal or combined samples (e.g., combinations of nose, throat, mouth, or saliva samples) were used, but lower when nasopharyngeal samples were used, and in those classified as asymptomatic at the time of testing. Rapid molecular tests may result in fewer false negatives than rapid antigen tests (sensitivity: 0.93, 0.88-0.96; specificity: 0.98, 0.97-0.99). The tests with the highest sensitivity and specificity estimates were the Xpert Xpress rapid molecular test by Cepheid (sensitivity: 0.99, 0.83-1.00; specificity: 0.97, 0.69-1.00) among the 23 commercial rapid molecular tests and the COVID-VIRO test by AAZ-LMB (sensitivity: 0.93, 0.48-0.99; specificity: 0.98, 0.44-1.00) among the 36 rapid antigen tests we examined., Conclusions: Rapid molecular tests were associated with both high sensitivity and specificity, while rapid antigen tests were mainly associated with high specificity, according to the minimum performance requirements by WHO and Health Canada. Our rapid review was limited to English, peer-reviewed published results of commercial tests, and study risk of bias was not assessed. A full systematic review is required., Review Registration: PROSPERO CRD42021289712., (© 2023. The Author(s).)

Background: The diagnostic challenges associated with the COVID-19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS-CoV-2 infection. Serology tests to detect the presence of antibodies to SARS-CoV-2 enable detection of past infection and may detect cases of SARS-CoV-2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS-CoV-2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS-CoV-2 epidemiology., Objectives: To assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS-CoV-2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS-CoV-2. Sources of heterogeneity investigated included: timing of test, test method, SARS-CoV-2 antigen used, test brand, and reference standard for non-SARS-CoV-2 cases., Search Methods: The COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre) 'COVID-19: Living map of the evidence' and the Norwegian Institute of Public Health 'NIPH systematic and living map on COVID-19 evidence'. We did not apply language restrictions., Selection Criteria: We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT-PCR test. Small studies with fewer than 25 SARS-CoV-2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR), clinical diagnostic criteria, and pre-pandemic samples)., Data Collection and Analysis: We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS-2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta-analysis, we fitted univariate random-effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria., Main Results: We included 178 separate studies (described in 177 study reports, with 45 as pre-prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS-CoV-2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS-CoV-2 infection were most commonly hospital inpatients (78/178, 44%), and pre-pandemic samples were used by 45% (81/178) to estimate specificity. Over two-thirds of studies recruited participants based on known SARS-CoV-2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS-CoV-2 vaccines and present data for naturally acquired antibody responses. Seventy-nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme-linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS-CoV-2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre-pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent-phase infection) and specific (pre-pandemic samples) compared to both ELISAs and LFAs (P &lt; 0.001 for differences across test methods). The antigen(s) used (whether from the Spike-protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent-phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low-prevalence (2%) setting, where antibody testing is used to diagnose COVID-19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS-CoV-2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post-symptom onset or post-positive PCR) of SARS-CoV-2 infection., Authors' Conclusions: Some antibody tests could be a useful diagnostic tool for those in whom molecular- or antigen-based tests have failed to detect the SARS-CoV-2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post-acute sequelae of COVID-19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero-epidemiological purposes. The applicability of results for detection of vaccination-induced antibodies is uncertain., (Copyright © 2022 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy (DTA) provides guidance on important aspects of conducting a test accuracy systematic review. In this paper we present TOMAS-R (Template of Multiplicity and Analysis in Systematic Reviews), a structured template to use in conjunction with current Cochrane DTA guidance, to help identify complexities in the review question and to assist planning of data extraction and analysis when clinically important variation and multiplicity is present. Examples of clinically important variation and multiplicity could include differences in participants, index tests and test methods, target conditions and reference standards used to define them, study design and methodological quality. Our TOMAS-R template goes beyond the broad topic headings in current guidance that are sources of potential variation and multiplicity, by providing prompts for common sources of heterogeneity encountered from our experience of authoring over 100 reviews. We provide examples from two reviews to assist users. The TOMAS-R template adds value by supplementing available guidance for DTA reviews by providing a tool to facilitate discussions between methodologists, clinicians, statisticians and patient/public team members to identify the full breadth of review question complexities early in the process. The use of a structured set of prompting questions at the important stage of writing the protocol ensures clinical relevance as a main focus of the review, while allowing identification of key clinical components for data extraction and later analysis thereby facilitating a more efficient review process., (© 2022. The Author(s).)

Background: Accurate rapid diagnostic tests for SARS-CoV-2 infection would be a useful tool to help manage the COVID-19 pandemic. Testing strategies that use rapid antigen tests to detect current infection have the potential to increase access to testing, speed detection of infection, and inform clinical and public health management decisions to reduce transmission. This is the second update of this review, which was first published in 2020., Objectives: To assess the diagnostic accuracy of rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection. We consider accuracy separately in symptomatic and asymptomatic population groups. Sources of heterogeneity investigated included setting and indication for testing, assay format, sample site, viral load, age, timing of test, and study design., Search Methods: We searched the COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) on 08 March 2021. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. 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 tests. We included evaluations of single applications of a test (one test result reported per person) and evaluations of serial testing (repeated antigen testing over time). Reference standards for presence or absence of infection were any laboratory-based molecular test (primarily reverse transcription polymerase chain reaction (RT-PCR)) or pre-pandemic respiratory sample., Data Collection and Analysis: We used standard screening procedures with three people. Two people independently carried out quality assessment (using the QUADAS-2 tool) and extracted study results. Other study characteristics were extracted by one review author and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test, and pooled data using the bivariate model. We investigated heterogeneity by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and compliance with manufacturer instructions for use and according to symptom status., Main Results: We included 155 study cohorts (described in 166 study reports, with 24 as preprints). The main results relate to 152 evaluations of single test applications including 100,462 unique samples (16,822 with confirmed SARS-CoV-2). Studies were mainly conducted in Europe (101/152, 66%), and evaluated 49 different commercial antigen assays. Only 23 studies compared two or more brands of test. Risk of bias was high because of participant selection (40, 26%); interpretation of the index test (6, 4%); weaknesses in the reference standard for absence of infection (119, 78%); and participant flow and timing 41 (27%). Characteristics of participants (45, 30%) and index test delivery (47, 31%) differed from the way in which and in whom the test was intended to be used. Nearly all studies (91%) used a single RT-PCR result to define presence or absence of infection. The 152 studies of single test applications reported 228 evaluations of antigen tests. Estimates of sensitivity varied considerably between studies, with consistently high specificities. Average sensitivity was higher in symptomatic (73.0%, 95% CI 69.3% to 76.4%; 109 evaluations; 50,574 samples, 11,662 cases) compared to asymptomatic participants (54.7%, 95% CI 47.7% to 61.6%; 50 evaluations; 40,956 samples, 2641 cases). Average sensitivity was higher in the first week after symptom onset (80.9%, 95% CI 76.9% to 84.4%; 30 evaluations, 2408 cases) than in the second week of symptoms (53.8%, 95% CI 48.0% to 59.6%; 40 evaluations, 1119 cases). For those who were asymptomatic at the time of testing, sensitivity was higher when an epidemiological exposure to SARS-CoV-2 was suspected (64.3%, 95% CI 54.6% to 73.0%; 16 evaluations; 7677 samples, 703 cases) compared to where COVID-19 testing was reported to be widely available to anyone on presentation for testing (49.6%, 95% CI 42.1% to 57.1%; 26 evaluations; 31,904 samples, 1758 cases). Average specificity was similarly high for symptomatic (99.1%) or asymptomatic (99.7%) participants. We observed a steady decline in summary sensitivities as measures of sample viral load decreased. Sensitivity varied between brands. When tests were used according to manufacturer instructions, average sensitivities by brand ranged from 34.3% to 91.3% in symptomatic participants (20 assays with eligible data) and from 28.6% to 77.8% for asymptomatic participants (12 assays). For symptomatic participants, summary sensitivities for seven assays were 80% or more (meeting acceptable criteria set by the World Health Organization (WHO)). The WHO acceptable performance criterion of 97% specificity was met by 17 of 20 assays when tests were used according to manufacturer instructions, 12 of which demonstrated specificities above 99%. For asymptomatic participants the sensitivities of only two assays approached but did not meet WHO acceptable performance standards in one study each; specificities for asymptomatic participants were in a similar range to those observed for symptomatic people. At 5% prevalence using summary data in symptomatic people during the first week after symptom onset, the positive predictive value (PPV) of 89% means that 1 in 10 positive results will be a false positive, and around 1 in 5 cases will be missed. At 0.5% prevalence using summary data for asymptomatic people, where testing was widely available and where epidemiological exposure to COVID-19 was suspected, resulting PPVs would be 38% to 52%, meaning that between 2 in 5 and 1 in 2 positive results will be false positives, and between 1 in 2 and 1 in 3 cases will be missed., 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. Assays that meet appropriate performance standards, such as those set by WHO, could replace laboratory-based RT-PCR when immediate decisions about patient care must be made, or where RT-PCR cannot be delivered in a timely manner. However, they are more suitable for use as triage to RT-PCR testing. The variable sensitivity of antigen tests means that people who test negative may still be infected. Many commercially available rapid antigen tests have not been evaluated in independent validation studies. Evidence for testing in asymptomatic cohorts has increased, however sensitivity is lower and there is a paucity of evidence for testing in different settings. Questions remain about the use of antigen test-based repeat testing strategies. Further research is needed to evaluate the effectiveness of screening programmes at reducing transmission of infection, whether mass screening or targeted approaches including schools, healthcare setting and traveller screening., (Copyright © 2022 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)

Competing Interests: The authors declare no conflict of interest.

Background: COVID-19 illness is highly variable, ranging from infection with no symptoms through to pneumonia and life-threatening consequences. Symptoms such as fever, cough, or loss of sense of smell (anosmia) or taste (ageusia), can help flag early on if the disease is present. Such information could be used either to rule out COVID-19 disease, or to identify people who need to go for COVID-19 diagnostic tests. This is the second update of this review, which was first published in 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: We undertook electronic searches up to 10 June 2021 in the University of Bern living search database. In addition, we checked repositories of COVID-19 publications. We used artificial intelligence text analysis to conduct an initial classification of documents. We did not apply any language restrictions., Selection Criteria: Studies were eligible if they included people with clinically suspected COVID-19, or recruited known cases with COVID-19 and also controls without COVID-19 from a single-gate cohort. Studies were eligible when they recruited people presenting to primary care or hospital outpatient settings. Studies that included people 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, and full-text stage. They resolved any disagreements by discussion with a third review author. Two review authors independently extracted data and assessed risk of bias using the QUADAS-2 checklist, and resolved disagreements by discussion with a third review author. Analyses were restricted to prospective studies only. We presented sensitivity and specificity in paired forest plots, in receiver operating characteristic (ROC) space and in dumbbell plots. We estimated summary parameters using a bivariate random-effects meta-analysis whenever five or more primary prospective studies were available, and whenever heterogeneity across studies was deemed acceptable., Main Results: We identified 90 studies; for this update we focused on the results of 42 prospective studies with 52,608 participants. Prevalence of COVID-19 disease varied from 3.7% to 60.6% with a median of 27.4%. Thirty-five studies were set in emergency departments or outpatient test centres (46,878 participants), three in primary care settings (1230 participants), two in a mixed population of in- and outpatients in a paediatric hospital setting (493 participants), and two overlapping studies in nursing homes (4007 participants). The studies did not clearly distinguish mild COVID-19 disease from COVID-19 pneumonia, so we present the results for both conditions together. Twelve studies had a high risk of bias for selection of participants because they used a high level of preselection to decide whether reverse transcription polymerase chain reaction (RT-PCR) testing was needed, or because they enrolled a non-consecutive sample, or because they excluded individuals while they were part of the study base. We rated 36 of the 42 studies as high risk of bias for the index tests because there was little or no detail on how, by whom and when, the symptoms were measured. For most studies, eligibility for testing was dependent on the local case definition and testing criteria that were in effect at the time of the study, meaning most people who were included in studies had already been referred to health services based on the symptoms that we are evaluating in this review. The applicability of the results of this review iteration improved in comparison with the previous reviews. This version has more studies of people presenting to ambulatory settings, which is where the majority of assessments for COVID-19 take place. Only three studies presented any data on children separately, and only one focused specifically on older adults. We found data on 96 symptoms or combinations of signs and symptoms. Evidence on individual signs as diagnostic tests was rarely reported, so this review reports mainly on the diagnostic value of symptoms. Results were highly variable across studies. Most had very low sensitivity and high specificity. RT-PCR was the most often used reference standard (40/42 studies). Only cough (11 studies) had a summary sensitivity above 50% (62.4%, 95% CI 50.6% to 72.9%)); its specificity was low (45.4%, 95% CI 33.5% to 57.9%)). Presence of fever had a sensitivity of 37.6% (95% CI 23.4% to 54.3%) and a specificity of 75.2% (95% CI 56.3% to 87.8%). The summary positive likelihood ratio of cough was 1.14 (95% CI 1.04 to 1.25) and that of fever 1.52 (95% CI 1.10 to 2.10). Sore throat had a summary positive likelihood ratio of 0.814 (95% CI 0.714 to 0.929), which means that its presence increases the probability of having an infectious disease other than COVID-19. Dyspnoea (12 studies) and fatigue (8 studies) had a sensitivity of 23.3% (95% CI 16.4% to 31.9%) and 40.2% (95% CI 19.4% to 65.1%) respectively. Their specificity was 75.7% (95% CI 65.2% to 83.9%) and 73.6% (95% CI 48.4% to 89.3%). The summary positive likelihood ratio of dyspnoea was 0.96 (95% CI 0.83 to 1.11) and that of fatigue 1.52 (95% CI 1.21 to 1.91), which means that the presence of fatigue slightly increases the probability of having COVID-19. Anosmia alone (7 studies), ageusia alone (5 studies), and anosmia or ageusia (6 studies) had summary sensitivities below 50% but summary specificities over 90%. Anosmia had a summary sensitivity of 26.4% (95% CI 13.8% to 44.6%) and a specificity of 94.2% (95% CI 90.6% to 96.5%). Ageusia had a summary sensitivity of 23.2% (95% CI 10.6% to 43.3%) and a specificity of 92.6% (95% CI 83.1% to 97.0%). Anosmia or ageusia had a summary sensitivity of 39.2% (95% CI 26.5% to 53.6%) and a specificity of 92.1% (95% CI 84.5% to 96.2%). The summary positive likelihood ratios of anosmia alone and anosmia or ageusia were 4.55 (95% CI 3.46 to 5.97) and 4.99 (95% CI 3.22 to 7.75) respectively, which is just below our arbitrary definition of a 'red flag', that is, a positive likelihood ratio of at least 5. The summary positive likelihood ratio of ageusia alone was 3.14 (95% CI 1.79 to 5.51). Twenty-four studies assessed combinations of different signs and symptoms, mostly combining olfactory symptoms. By combining symptoms with other information such as contact or travel history, age, gender, and a local recent case detection rate, some multivariable prediction scores reached a sensitivity as high as 90%., Authors' Conclusions: Most individual symptoms included in this review have poor diagnostic accuracy. Neither absence nor presence of symptoms are accurate enough to rule in or rule out the disease. The presence of anosmia or ageusia may be useful as a red flag for the presence of COVID-19. The presence of cough also supports further testing. There is currently no evidence to support further testing with PCR in any individuals presenting only with upper respiratory symptoms such as sore throat, coryza or rhinorrhoea. Combinations of symptoms with other readily available information such as contact or travel history, or the local recent case detection rate may prove more useful and should be further investigated in an unselected population presenting to primary care or hospital outpatient settings. The diagnostic accuracy of symptoms for COVID-19 is moderate to low and any testing strategy using symptoms as selection mechanism will result in both large numbers of missed cases and large numbers of people requiring testing. Which one of these is minimised, is determined by the goal of COVID-19 testing strategies, that is, controlling the epidemic by isolating every possible case versus identifying those with clinically important disease so that they can be monitored or treated to optimise their prognosis. The former will require a testing strategy that uses very few symptoms as entry criterion for testing, the latter could focus on more specific symptoms such as fever and anosmia., (Copyright © 2022 The Authors. Cochrane Database of Systematic Reviews published by John Wiley & Sons, Ltd. on behalf of The Cochrane Collaboration.)