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 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.
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 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.
IntroductionPrognosis following surgical rotator cuff repair (RCR) is often established through the assessment of non-modifiable biomedical factors such as tear size. This understates the complex ...nature of recovery following RCR. There is a need to identify modifiable psychosocial and sleep-related variables, and to find out whether changes in central pain processing influence prognosis after RCR. This will improve our knowledge on how to optimise recovery, using a holistic rehabilitation approach.Methods and analysisThis longitudinal study will analyse 141 participants undergoing usual care for first time RCR. Data will be collected 1–21 days preoperatively (T1), then 11–14 weeks (T2) and 12–14 months (T3) postoperatively. We will use mixed-effects linear regression to assess relationships between potential prognostic factors and our primary and secondary outcome measures—the Western Ontario Rotator Cuff Index; the Constant-Murley Score; the Subjective Shoulder Value; Maximal Pain (Numeric Rating Scale); and Quality of Life (European Quality of Life, 5 dimensions, 5 levels). Potential prognostic factors include: four psychosocial variables; pain catastrophising, perceived stress, injury perceptions and patients’ expectations for RCR; sleep; and four factors related to central pain processing (central sensitisation inventory, temporal summation, cold hyperalgesia and pressure pain threshold). Intercorrelations will be assessed to determine the strength of relationships between all potential prognostic indicators.Our aim is to explore whether modifiable psychosocial factors, sleep-related variables and altered central pain processing are associated with outcomes pre-RCR and post-RCR and to identify them as potential prognostic factors.Ethics and disseminationThe results of the study will be disseminated at conferences such as the European Pain Congress. One or more manuscripts will be published in a peer-reviewed SCI-ranked journal. Findings will be reported in accordance with the STROBE statement and PROGRESS framework. Ethical approval is granted by the Ethical commission of Canton of Zurich, Switzerland, No: ID_2018-02089Trial registration numberNCT04946149.
The latissimus dorsi plays a major role in generating the propulsive force during swimming. In addition, stiffness of this muscle may result in altered stroke biomechanics and predispose swimmers to ...shoulder pain. Measuring the flexibility of the latissimus dorsi can be of interest to reduce injury. However, the reliability of such measurement has not yet been investigated in competitive swimmers.
To assess the within-session intrarater and interrater reliability of a passive shoulder flexion range of motion measurement for latissimus dorsi flexibility in competitive swimmers.
Within-session intrarater and interrater reliability.
Competitive swimming clubs in Flanders, Belgium.
Twenty-six competitive swimmers (15.46 2.98 y; 16 men and 10 women).
Each rater performed 2 alternating (eg, left-right-left-right) measurements of passive shoulder flexion range of motion twice, with a 30-second rest period in between.
The intraclass correlation coefficients were calculated to assess intrarater and interrater reliability.
Interrater intraclass correlation coefficient ranged from .54 (95% confidence interval CI, -.16 to .81) to .57 (95% CI, -.24 to .85). Results for the intrarater reliability ranged from .91 (95% CI, .81 to .96) to .94 (95% CI, .87 to .97).
Results of this study suggest that shoulder flexion range of motion in young competitive swimmers can be measured reliably by a single rater within the same session.
Objectives
To describe antimicrobial prescribing by Belgian dentists in ambulatory care, from 2010 until 2016.
Materials and methods
Reimbursement data from the Belgian National Institute for Health ...and Disability Insurance were analysed to evaluate antimicrobial prescribing (WHO ATC‐codes J01/P01AB). Utilisation was expressed in defined daily doses (DDDs), and in DDDs and packages per 1000 inhabitants per day (DID and PID, respectively). Additionally, the number of DDD and packages per prescriber was calculated.
Results
In 2016, the dentistry‐related prescribing rate of ‘Antibacterials for systemic use’ (J01) and ‘Antiprotozoals’ (P01AB) was 1.607 and 0.014 DID, respectively. From 2010 to 2016, the DID rate of J01 increased by 6.3%, while the PID rate declined by 6.7%. Amoxicillin and amoxicillin with an enzyme inhibitor were the most often prescribed products, followed by clindamycin, clarithromycin, doxycycline, azithromycin and metronidazole. The proportion of amoxicillin relative to amoxicillin with an enzyme inhibitor was low. The narrow‐spectrum antibiotic penicillin V was almost never prescribed.
Conclusions
Antibiotics typically classified as broad‐ or extended‐spectrum were prescribed most often by Belgian dentists during the period 2000–2016. Although the DID rate of all ‘Antibacterials for systemic use’ (J01) increased over the years, the number of prescriptions per dentist decreased since 2013. The high prescription level of amoxicillin with an enzyme inhibitor is particularly worrying. It indicates that there is a need for comprehensive clinical practice guidelines for Belgian dentists.
Full text
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BFBNIB, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
Background
To improve the management of childhood urinary tract infections, it is essential to understand the incidence rates, testing and treatment strategy.
Methods
A retrospective study ...using data from 45 to 104 general practices (2000 to 2020) in Flanders (Belgium). We calculated the incidence rates (per 1000 person-years) of cystitis, pyelonephritis, and lab-based urine tests per age (< 2, 2-4, 5-9 and 10-18 years)) and gender in children and performed an autoregressive time-series analysis and seasonality analysis. In children with UTI, we calculated the number of lab-based urine tests and antibiotic prescriptions per person-year and performed an autoregressive time-series analysis.
Results
There was a statistically significant increase in the number of UTI episodes from 2000 to 2020 in each age group (
p
< 0.05), except in boys 2-4 years. Overall, the change in incidence rate was low. In 2020, the incidence rates of cystitis were highest in girls 2-4 years old (40.3 /1000 person-years 95%CI 34.5-46.7) and lowest in boys 10-18 (2.6 /1000 person-years 95%CI 1.8-3.6) The incidence rates of pyelonephritis were highest in girls 2-4 years (5.5, 95%CI 3.5-8.1 /1000 person-years) and children < 2 years of age (boys: 5.4, 95%CI 3.1-8.8 and girls: 4.9, 95%CI 2.7-8.8 /1000 person-years). In children 2-10 years, there was an increase in number of lab-based urine tests per cystitis episode per year and a decrease in total number of electronic antibiotic prescriptions per cystitis episode per year, from 2000 to 2020. In children with cystitis < 10 years in 2020, 51% (95%CI 47-56%) received an electronic antibiotic prescription, of which the majority were broad-spectrum agents.
Conclusions
Over the last 21 years, there was a slight increase in the number of UTI episodes diagnosed in children in Flemish general practices, although the overall change was low. More targeted antibiotic therapy for cystitis in accordance with clinical guidelines is necessary to reduce the use of broad-spectrum agents in children below 10 years.
Full text
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background:
Knowledge of predictors for shoulder pain in swimmers can assist professionals working with the athlete in developing optimal prevention strategies. However, study methodology and limited ...available data have constrained a comprehensive understanding of which factors cause shoulder pain.
Purpose:
To investigate risk factors and develop and internally validate a multivariable prognostic model for the prediction of shoulder pain in swimmers.
Study Design:
Cohort study; Level of evidence, 2.
Methods:
A total of 201 pain-free club- to international-level competitive swimmers were followed for 2 consecutive seasons. The cohort consisted of 96 male (mean ± SD age, 13.9 ± 2.2 years) and 105 female (13.9 ± 2.2 years) swimmers. Demographic, sport-specific, and musculoskeletal characteristics were assessed every 6 months. Swim-training exposure was observed prospectively. Shoulder pain interfering with training was the primary outcome. Multiple imputation was used to cope with missing data. The final model was estimated using multivariable logistic regression. We applied bootstrapping to internally validate the model and correct for overoptimism.
Results:
A total of 42 new cases of shoulder pain were recorded during the study. Average duration of follow-up was 1.1 years. Predictors included in the final model were acute:chronic workload ratio (odds ratio OR, 4.31; 95% CI, 1.00-18.54), competitive level (OR, 0.19; 95% CI, 0.06-0.63), shoulder flexion range of motion, posterior shoulder muscle endurance (OR, 0.96; 95% CI, 0.92-0.99), and hand entry position error (OR, 0.37; 95% CI, 0.16-0.91). After internal validation, this model maintained good calibration and discriminative power (area under the receiver operating characteristic curve, 0.71; 95% CI, 0.60-0.94).
Conclusion:
Our model consists of parameters that are readily measurable in a swimming setting, allowing the identification of swimmers at risk for shoulder pain. Multivariable logistic regression showed the strongest predictors for shoulder pain were regional competitive swimming level, acute:chronic workload ratio, posterior shoulder muscle endurance, and hand entry error.
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FSPLJ, NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
OBJECTIVE: To explore modifiable psychosocial factors, sleep-related variables, indices of central pain processing and patients’ characteristics as potential prognostic factors for pain, shoulder ...function, and Quality of Life (QoL) one-year after rotator cuff repair. DESIGN: This observational longitudinal study included 142 patients who were undergoing rotator cuff repair. All measures took place pre-rotator cuff repair (T0), and 12 weeks (T1) and 12 months (T2) after rotator cuff repair. METHODS: Mixed-effects linear regression modelled relationships between the Western Ontario Rotator Cuff Index (WORC, model A), the Subjective Shoulder Value (SSV, model B) and Euroqol’s EQ-5D-5L for QoL (model C) and potential prognostic factors over time. Factors included; psychosocial variables, sleep–related indices and proxies of central pain processing. Patients’ age, sex, and body mass index complemented the analyses. RESULTS: At follow-up (T2) data from 124 participants were available for analysis. Five prognostic factors were identified for the one-year outcome. Better expectations for symptom reduction (p < 0.0001, -1.4mm) and an increase in Douleur Neuropathique 4 score (p = 0.0481, -0.9mm) affected the evolution of WORC over time (model A). An increase in injury perception subscale consequence (p = 0.0035, 0.04%) influenced the SSV trajectory (model B). And when sleep quality (p = 0.0011, -0.13%) and sleep efficiency (p = 0.0002, 0.005%) improved, the EQ-5D-5L slope was affected (model C). CONCLUSION: Addressing cognitions, pain mechanisms and sleep behaviour prior to rotator cuff repair can identify people who are at risk of a poor outcome after surgery.
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 < 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.
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