Health services for children in western Europe Wolfe, Ingrid, Dr; Thompson, Matthew; Gill, Peter, BMSc ...
The Lancet (British edition),
04/2013, Volume:
381, Issue:
9873
Journal Article
Peer reviewed
Summary Western European health systems are not keeping pace with changes in child health needs. Non-communicable diseases are increasingly common causes of childhood illness and death. Countries are ...responding to changing needs by adapting child health services in different ways and useful insights can be gained through comparison, especially because some have better outcomes, or have made more progress, than others. Although overall child health has improved throughout Europe, wide inequities remain. Health services and social and cultural determinants contribute to differences in health outcomes. Improvement of child health and reduction of suffering are achievable goals. Development of systems more responsive to evolving child health needs is likely to necessitate reconfiguring of health services as part of a whole-systems approach to improvement of health. Chronic care services and first-contact care systems are important aspects. The Swedish and Dutch experiences of development of integrated systems emphasise the importance of supportive policies backed by adequate funding. France, the UK, Italy, and Germany offer further insights into chronic care services in different health systems. First-contact care models and the outcomes they deliver are highly variable. Comparisons between systems are challenging. Important issues emerging include the organisation of first-contact models, professional training, arrangements for provision of out-of-hours services, and task-sharing between doctors and nurses. Flexible first-contact models in which child health professionals work closely together could offer a way to balance the need to provide expertise with ready access. Strategies to improve child health and health services in Europe necessitate a whole-systems approach in three interdependent systems—practice (chronic care models, first-contact care, competency standards for child health professionals), plans (child health indicator sets, reliable systems for capture and analysis of data, scale-up of child health research, anticipation of future child health needs), and policy (translation of high-level goals into actionable policies, open and transparent accountability structures, political commitment to delivery of improvements in child health and equity throughout Europe).
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Summary Background Our aim was to identify which clinical features have value in confirming or excluding the possibility of serious infection in children presenting to ambulatory care settings in ...developed countries. Methods In this systematic review, we searched electronic databases (Medline, Embase, DARE, CINAHL), reference lists of relevant studies, and contacted experts to identify articles assessing clinical features of serious infection in children. 1939 potentially relevant studies were identified. Studies were selected on the basis of six criteria: design (studies of diagnostic accuracy or prediction rules), participants (otherwise healthy children aged 1 month to 18 years), setting (ambulatory care), outcome (serious infection), features assessed (assessable in ambulatory care setting), and sufficient data reported. Quality assessment was based on the Quality Assessment of Diagnostic Accuracy Studies criteria. We calculated likelihood ratios for the presence (positive likelihood ratio) or absence (negative likelihood ratio) of each clinical feature and pre-test and post-test probabilities of the outcome. Clinical features with a positive likelihood ratio of more than 5·0 were deemed red flags (ie, warning signs for serious infection); features with a negative likelihood ratio of less than 0·2 were deemed rule-out signs. Findings 30 studies were included in the analysis. Cyanosis (positive likelihood ratio range 2·66–52·20), rapid breathing (1·26–9·78), poor peripheral perfusion (2·39–38·80), and petechial rash (6·18–83·70) were identified as red flags in several studies. Parental concern (positive likelihood ratio 14·40, 95% CI 9·30–22·10) and clinician instinct (positive likelihood ratio 23·50, 95 % CI 16·80–32·70) were identified as strong red flags in one primary care study. Temperature of 40°C or more has value as a red flag in settings with a low prevalence of serious infection. No single clinical feature has rule-out value but some combinations can be used to exclude the possibility of serious infection—for example, pneumonia is very unlikely (negative likelihood ratio 0·07, 95% CI 0·01–0·46) if the child is not short of breath and there is no parental concern. The Yale Observation Scale had little value in confirming (positive likelihood ratio range 1·10–6·70) or excluding (negative likelihood ratio range 0·16–0·97) the possibility of serious infection. Interpretation The red flags for serious infection that we identified should be used routinely, but serious illness will still be missed without effective use of precautionary measures. We now need to identify the level of risk at which clinical action should be taken. Funding Health Technology Assessment and National Institute for Health Research National School for Primary Care Research.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
ObjectiveLeukaemia is the most common cancer of childhood, accounting for a third of cases. In order to assist clinicians in its early detection, we systematically reviewed all existing data on its ...clinical presentation and estimated the frequency of signs and symptoms presenting at or prior to diagnosis.DesignWe searched MEDLINE and EMBASE for all studies describing presenting features of leukaemia in children (0–18 years) without date or language restriction, and, when appropriate, meta-analysed data from the included studies.ResultsWe screened 12 303 abstracts for eligibility and included 33 studies (n=3084) in the analysis. All were cohort studies without control groups. 95 presenting signs and symptoms were identified and ranked according to frequency. Five features were present in >50% of children: hepatomegaly (64%), splenomegaly (61%), pallor (54%), fever (53%) and bruising (52%). An additional eight features were present in a third to a half of children: recurrent infections (49%), fatigue (46%), limb pain (43%), hepatosplenomegaly (42%), bruising/petechiae (42%), lymphadenopathy (41%), bleeding tendency (38%) and rash (35%). 6% of children were asymptomatic on diagnosis.ConclusionsOver 50% of children with leukaemia have palpable livers, palpable spleens, pallor, fever or bruising on diagnosis. Abdominal symptoms such as anorexia, weight loss, abdominal pain and abdominal distension are common. Musculoskeletal symptoms such as limp and joint pain also feature prominently. Children with unexplained illness require a thorough history and focused clinical examination, which should include abdominal palpation, palpation for lymphadenopathy and careful scrutiny of the skin. Occurrence of multiple symptoms and signs should alert clinicians to possible leukaemia.
Background Most guidelines recommend the use of capillary refill time (CRT) as part of the routine assessment of unwell children, but there is little consensus on the optimum method of measurement ...and cut-off time. Methods We searched Medline (from 1948), Embase (from 1980) and CINAHL (from 1991) to June 2014 to identify studies with information on the normal range of CRT in healthy children, the validity of CRT compared with reference standard measures of haemodynamic status, reliability and factors influencing measurement of CRT, such as body site, pressing time and temperature. Findings We included 21 studies on 1915 children. Four studies provided information on the relationship between CRT and measures of cardiovascular status, 13 provided data on the normal range of CRT, 7 provided data on reliability and 10 assessed the effect of various confounding factors. In children over 7 days of age, the upper limit of normal CRT is approximately 2 s when measured on a finger, and 4 s when measured on the chest or foot, irrespective of whether the child is feverish or not. Longer pressing times and ambient temperature outside 20°C–25°C are associated with longer CRT. Evidence suggests that the use of stopwatches reduces variability between observers. Interpretation We recommend use of the following standardised CRT method of measurement: press on the finger for 5 s using moderate pressure at an ambient temperature of 20°C–25°C. A capillary refill time of 3 s or more should be considered abnormal.
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
Diagnosing childhood urinary tract infections (UTIs) is challenging. Clinical prediction rules may help to identify children that require urine sampling. However, there is a lack of ...research to determine the accuracy of the scores in general practice.
Aim
To validate clinical prediction rules (UTI Calculator UTICalc, A Diagnosis of Urinary Tract Infection in Young Children DUTY, and Gorelick score) for paediatric UTIs in primary care.
Design & setting
Post-hoc analysis of a cross-sectional study in 39 general practices and two emergency departments (EDs). The study took place in Belgium from March 2019–March 2020.
Method
Physicians recruited acutely ill children aged ≤18 years and sampled urine systematically for culture. Per rule, an apparent validation was performed, and sensitivities and specificities were calculated with 95% confidence intervals (CIs) per threshold in the target group. For the DUTY coefficient-based algorithm, a logistic calibration was performed and the area under the receiver operating characteristic curve (AUC) was calculated with 95% CI.
Results
Of 834 children aged ≤18 years recruited, there were 297 children aged <5 years. The UTICalc and Gorelick score had high-to-moderate sensitivity and low specificity: UTICalc (≥2%) 75% and 16%, respectively; Gorelick (≥2 variables) 91% and 8%, respectively. In contrast, the DUTY score ≥5 points had low sensitivity (8%) but high specificity (99%). Urine samples would be obtained in 72% versus 38% (UTICalc), 92% versus 38% (Gorelick) or 1% versus 32% (DUTY) of children, compared with routine care. The number of missed infections per score was 1/4 (UTICalc), 2/23 (Gorelick), and 24/26 (DUTY). The UTICalc + dipstick model had high sensitivity and specificity (100% and 91%), resulting in no missed cases and 59% (95% CI = 49% to 68%) of antibiotics prescribed inappropriately.
Conclusion
In this study, the UTICalc and Gorelick score were useful for ruling out UTI, but resulted in high urine sampling rates. The DUTY score had low sensitivity, meaning that 92% of UTIs would be missed.
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.
Abstract Purpose Presentation with acute lower respiratory tract infection (LRTI) in primary care is common. The aim of this study was to help clinicians treat patients presenting with LRTI in ...primary care by identifying those at risk of serious adverse outcomes (death, admission, late-onset pneumonia). Methods In a prospective cohort study of patients presenting with LRTI symptoms, patient characteristics and clinical findings were recorded and adverse events identified over 30 days by chart review. Multivariable logistic regression analyses identified predictors of adverse outcomes. Results Participants were recruited from 522 UK practices in 2009–2013. The analysis was restricted to the 28,846 adult patients not referred immediately to the hospital. Serious adverse outcomes occurred in 325/28,846 (1.1%). Eight factors were independently predictive; these characterized symptom severity (absence of coryza, fever, chest pain, and clinician-assessed severity), patient vulnerability (age >65 years, comorbidity), and physiological impact (oxygen saturation <95%, low blood pressure). In aggregate, the 8 features had moderate predictive value (area under the receiver operating characteristic curve 0.71, 95% CI, 0.68–0.74); the 4% of patients with ≥5 features had an approximately 1 in 17 (5.7%) risk of serious adverse outcomes, the 35% with 3 or 4 features had an intermediate risk (1 in 50, 2.0%), and the 61% with ≤2 features had a low (1 in 200, 0.5%) risk. Conclusions In routine practice most patients presenting with LRTI in primary care can be identified as at intermediate or low risk of serious outcome.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis.
To generate a clinical prediction rule to identify primary care patients who are at ...highest risk of myeloma.
Retrospective open cohort study using electronic health records data from the UK's Clinical Practice Research Datalink (CPRD) between 1 January 2000 and 1 January 2014.
Patients from the CPRD were included in the study if they were aged ≥40 years, had two full blood counts within a year, and had no previous diagnosis of myeloma. Cases of myeloma were identified in the following 2 years. Derivation and external validation datasets were created based on geographical region. Prediction equations were estimated using Cox proportional hazards models including patient characteristics, symptoms, and blood test results. Calibration, discrimination, and clinical utility were evaluated in the validation set.
Of 1 281 926 eligible patients, 737 (0.06%) were diagnosed with myeloma within 2 years. Independent predictors of myeloma included: older age; male sex; back, chest and rib pain; nosebleeds; low haemoglobin, platelets, and white cell count; and raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate. A model including symptoms and full blood count had an area under the curve of 0.84 (95% CI = 0.81 to 0.87) and sensitivity of 62% (95% CI = 55% to 68%) at the highest risk decile. The corresponding statistics for a second model, which also included calcium and inflammatory markers, were an area under the curve of 0.87 (95% CI = 0.84 to 0.90) and sensitivity of 72% (95% CI = 66% to 78%).
The implementation of these prediction rules would highlight the possibility of myeloma in patients where GPs do not suspect myeloma. Future research should focus on the prospective evaluation of further external validity and the impact on clinical practice.
Background
Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the resulting COVID‐19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to ...identify or rule out current infection, identify people in need of care escalation, or to test for past infection and immune response. Point‐of‐care antigen and molecular tests to detect current SARS‐CoV‐2 infection have the potential to allow earlier detection and isolation of confirmed cases compared to laboratory‐based diagnostic methods, with the aim of reducing household and community transmission.
Objectives
To assess the diagnostic accuracy of point‐of‐care antigen and molecular‐based tests to determine if a person presenting in the community or in primary or secondary care has current SARS‐CoV‐2 infection.
Search methods
On 25 May 2020 we undertook electronic searches in the Cochrane COVID‐19 Study Register and the COVID‐19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID‐19 publications. We did not apply any language restrictions.
Selection criteria
We included studies of people with suspected current SARS‐CoV‐2 infection, known to have, or not to have SARS‐CoV‐2 infection, or where tests were used to screen for infection. We included test accuracy studies of any design that evaluated antigen or molecular tests suitable for a point‐of‐care setting (minimal equipment, sample preparation, and biosafety requirements, with results available within two hours of sample collection). We included all reference standards to define the presence or absence of SARS‐CoV‐2 (including reverse transcription polymerase chain reaction (RT‐PCR) tests and established clinical diagnostic criteria).
Data collection and analysis
Two review authors independently screened studies and resolved any disagreements by discussion with a third review author. One review author independently extracted study characteristics, which were checked by a second review author. Two review authors independently extracted 2x2 contingency table data and assessed risk of bias and applicability of the studies using the QUADAS‐2 tool. We present sensitivity and specificity, with 95% confidence intervals (CIs), for each test using paired forest plots. We pooled data using the bivariate hierarchical model separately for antigen and molecular‐based tests, with simplifications when few studies were available. We tabulated available data by test manufacturer.
Main results
We included 22 publications reporting on a total of 18 study cohorts with 3198 unique samples, of which 1775 had confirmed SARS‐CoV‐2 infection. Ten studies took place in North America, two in South America, four in Europe, one in China and one was conducted internationally. We identified data for eight commercial tests (four antigen and four molecular) and one in‐house antigen test. Five of the studies included were only available as preprints.
We did not find any studies at low risk of bias for all quality domains and had concerns about applicability of results across all studies. We judged patient selection to be at high risk of bias in 50% of the studies because of deliberate over‐sampling of samples with confirmed COVID‐19 infection and unclear in seven out of 18 studies because of poor reporting. Sixteen (89%) studies used only a single, negative RT‐PCR to confirm the absence of COVID‐19 infection, risking missing infection. There was a lack of information on blinding of index test (n = 11), and around participant exclusions from analyses (n = 10). We did not observe differences in methodological quality between antigen and molecular test evaluations.
Antigen tests
Sensitivity varied considerably across studies (from 0% to 94%): the average sensitivity was 56.2% (95% CI 29.5 to 79.8%) and average specificity was 99.5% (95% CI 98.1% to 99.9%; based on 8 evaluations in 5 studies on 943 samples). Data for individual antigen tests were limited with no more than two studies for any test.
Rapid molecular assays
Sensitivity showed less variation compared to antigen tests (from 68% to 100%), average sensitivity was 95.2% (95% CI 86.7% to 98.3%) and specificity 98.9% (95% CI 97.3% to 99.5%) based on 13 evaluations in 11 studies of on 2255 samples. Predicted values based on a hypothetical cohort of 1000 people with suspected COVID‐19 infection (with a prevalence of 10%) result in 105 positive test results including 10 false positives (positive predictive value 90%), and 895 negative results including 5 false negatives (negative predictive value 99%).
Individual tests
We calculated pooled results of individual tests for ID NOW (Abbott Laboratories) (5 evaluations) and Xpert Xpress (Cepheid Inc) (6 evaluations). Summary sensitivity for the Xpert Xpress assay (99.4%, 95% CI 98.0% to 99.8%) was 22.6 (95% CI 18.8 to 26.3) percentage points higher than that of ID NOW (76.8%, (95% CI 72.9% to 80.3%), whilst the specificity of Xpert Xpress (96.8%, 95% CI 90.6% to 99.0%) was marginally lower than ID NOW (99.6%, 95% CI 98.4% to 99.9%; a difference of −2.8% (95% CI −6.4 to 0.8))
Authors' conclusions
This review identifies early‐stage evaluations of point‐of‐care tests for detecting SARS‐CoV‐2 infection, largely based on remnant laboratory samples. The findings currently have limited applicability, as we are uncertain whether tests will perform in the same way in clinical practice, and according to symptoms of COVID‐19, duration of symptoms, or in asymptomatic people. Rapid tests have the potential to be used to inform triage of RT‐PCR use, allowing earlier detection of those testing positive, but the evidence currently is not strong enough to determine how useful they are in clinical practice.
Prospective and comparative evaluations of rapid tests for COVID‐19 infection in clinically relevant settings are urgently needed. Studies should recruit consecutive series of eligible participants, including both those presenting for testing due to symptoms and asymptomatic people who may have come into contact with confirmed cases. Studies should clearly describe symptomatic status and document time from symptom onset or time since exposure. Point‐of‐care tests must be conducted on samples according to manufacturer instructions for use and be conducted at the point of care. Any future research study report should conform to the Standards for Reporting of Diagnostic Accuracy (STARD) guideline.
PDF
