Search results

Evaluation of a novel antigen-based rapid detection test for the diagnosis of SARS-CoV-2 in respiratory samples.

ObjectivesIn the context of the coronavirus disease 2019 (COVID-19) pandemic, the development and validation of rapid and easy-to-perform diagnostic methods are of high priority. This study was performed to evaluate a novel rapid antigen detection test (RDT) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in respiratory samples.MethodsThe fluorescence immunochromatographic SARS-CoV-2 antigen test (Bioeasy Biotechnology Co., Shenzhen, China) was evaluated using universal transport medium with nasopharyngeal (NP) and oropharyngeal (OP) swabs from suspected COVID-19 cases. Diagnostic accuracy was determined in comparison to SARS-CoV-2 real-time (RT)-PCR.ResultsA total of 127 samples were included; 82 were RT-PCR-positive. The median patient age was 38 years, 53.5% were male, and 93.7% were from the first week after symptom onset. Overall sensitivity and specificity were 93.9% (95% confidence interval 86.5-97.4%) and 100% (95% confidence interval 92.1-100%), respectively, with a diagnostic accuracy of 96.1% and Kappa coefficient of 0.9. Sensitivity was significantly higher in samples with high viral loads.ConclusionsThe RDT evaluated in this study showed a high sensitivity and specificity in samples mainly obtained during the first week of symptoms and with high viral loads, despite the use of a non-validated sample material. The assay has the potential to become an important tool for early diagnosis of SARS-CoV-2, particularly in situations with limited access to molecular methods.

Serodiagnostics for Severe Acute Respiratory Syndrome-Related Coronavirus 2 : A Narrative Review.

Accurate serologic tests to detect host antibodies to severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) will be critical for the public health response to the coronavirus disease 2019 pandemic. Many use cases are envisaged, including complementing molecular methods for diagnosis of active disease and estimating immunity for individuals. At the population level, carefully designed seroepidemiologic studies will aid in the characterization of transmission dynamics and refinement of disease burden estimates and will provide insight into the kinetics of humoral immunity. Yet, despite an explosion in the number and availability of serologic assays to test for antibodies against SARS-CoV-2, most have undergone minimal external validation to date. This hinders assay selection and implementation, as well as interpretation of study results. In addition, critical knowledge gaps remain regarding serologic correlates of protection from infection or disease, and the degree to which these assays cross-react with antibodies against related coronaviruses. This article discusses key use cases for SARS-CoV-2 antibody detection tests and their application to serologic studies, reviews currently available assays, highlights key areas of ongoing research, and proposes potential strategies for test implementation.

When fever is not malaria in Latin America: a systematic review.

BackgroundIn malaria-endemic countries, febrile episodes caused by diseases other than malaria are a growing concern. However, limited knowledge of the prevalent etiologic agents and their geographic distributions restrict the ability of health services to address non-malarial morbidity and mortality through effective case management. Here, we review the etiology of fever in Latin America (LA) between 1980 and 2015 and map significant pathogens commonly implicated in febrile infectious diseases.MethodsA literature search was conducted, without language restrictions, in three distinct databases in order to identify fever etiology studies that report laboratory-confirmed fever-causing pathogens that were isolated from usually sterile body sites. Data analyses and mapping was conducted with Tableau Desktop (version 2018.2.3).ResultsInclusion criteria were met by 625 publications corresponding to data relative to 34 countries. Studies using serology (n = 339) predominated for viral infections, culture (n = 131) for bacteria, and microscopy (n = 62) for fungi and parasites. The pathogen groups most frequently reported were viral infections (n = 277), bacterial infections (n = 265), parasitic infections (n = 59), fungal infections (n = 47), and more than one pathogen group (n = 24). The most frequently reported virus was dengue virus (n = 171), followed by other arboviruses (n = 55), and hantavirus (n = 18). For bacteria, Staphylococcus spp. (n = 82), Rickettsia spp. (n = 70), and Leptospira spp. (n = 55) were frequently reported. Areas with biggest gaps on etiology of fever were apparent.ConclusionsThis review provides a landscape of pathogens causing febrile illness other than malaria in LA for over 30 years. Our findings highlight the need to standardize protocols and report guidelines for fever etiology studies for better comparability of results and improved interpretation. Lastly, we should improve existing national laboratory surveillance systems, especially from low- to middle-income countries, to inform global fever policy priorities and timely identify emerging infections threats.Study registrationPROSPERO systematic review registration number: CRD42016049281.

Application of a simple point-of-care test to reduce UK healthcare costs and adverse events in outpatient acute respiratory infections.

Background: Acute respiratory infection (ARI) accounts for over two-thirds of total antibiotic prescriptions although most are caused by viruses that do not benefit from antibiotics. Most antibiotics are prescribed in the outpatients setting. Antibiotic overuse leads to antibiotic-related adverse events (AEs), inclusive of secondary infections, resistance, and increased costs. Point-of-care tests (POCT) may reduce unnecessary antibiotics. A cost analysis was performed to assess diagnostic POCT options to identify patients with an ARI that may benefit from antibiotics in a United Kingdom (UK) outpatient setting.Methods: Healthcare savings were estimated using a budget impact analysis based on UK National Institute for Health and Care Excellence (NICE) data and direct costs (antibiotics, AEs, POCTs) derived from published literature. Otitis media, sinusitis, pharyngitis and bronchitis were considered the most common ARIs. Antibiotic-related AE costs were calculated using re-consultation costs for anaphylaxis, Stevens-Johnson syndrome, allergies/diarrhea/nausea, C. difficile infection (CDI). Potential cost-savings from POCTs was assessed by evaluating NICE guideline-referenced POCTs (CRP, FebriDx, Sarasota, FL) as well as a target product profile (TPP).Results: Fifty-percent (7,718,283) of ARI consultations resulted in antibiotics while guideline-based prescribing suggest appropriate antibiotic prescriptions are warranted 9% (1,444,877) of ARI consultations. Direct antibiotic costs for actual ARI consultations associated with antibiotics was £24,003,866 vs. £4,493,568 for guideline-based, "appropriate" antibiotic prescriptions. Antibiotic-related AEs and re-consultations for actual vs. appropriate prescribing totaled £302,496,486 vs. £63,854,269. ARI prescribing plus AE costs totaled £326,729,943 annually without the use of delayed prescribing practices or POCT while the addition of delayed prescribing plus POCT totaled £60,114,564-£78,148,933 depending on the POCT.Conclusions: Adding POCT to outpatient triage of ARI can reduce unnecessary antibiotics and antibiotic-related AEs, resulting in substantial cost savings. Further, near patient diagnostic testing can benefit health systems and patients by avoiding exposure to unnecessary drugs, side effects and antibiotic resistant pathogens.Key points for decision makersMany patients are unnecessarily treated with antibiotics for respiratory infections.Antibiotic misuse leads to unnecessary adverse events, secondary infections, re-consultations, antimicrobial resistance and increased costs.Point-of-care diagnostic tests used to guide antibiotic prescriptions will avoid unnecessary adverse health effects and expenses.

Keep the quality high: the benefits of lot testing for the quality control of malaria rapid diagnostic tests.

BackgroundThe production and use of malaria rapid diagnostic tests (RDTs) has risen dramatically over the past 20 years. In view of weak or non-existing in vitro diagnostics (IVD) regulations and post-marketing surveillance (PMS) systems in malaria endemic countries, the World Health Organization, later joined by the Foundation for Innovative New Diagnostics, established an independent, centralized performance evaluation and Lot Testing (LT) programme to safeguard against poor quality of RDTs being distributed through the public health sector of malaria endemic countries. RDT performances and manufacturer quality management systems have evolved over the past decade raising questions about the future need for a centralized LT programme.ResultsBetween 2007 and 2017, 6056 lots have been evaluated, representing approximately 1.6 Billion RDTs. A total of 69 lots (1.1%) failed the quality control. Of these failures, 26 were detected at receipt of the RDT lot in the LT laboratory, representing an estimated 7.9 million poor quality RDTs, and LT requesters were advised that RDTs were not of sufficient quality for use in patient management. Forty-three were detected after long-term storage in the laboratory, of which 24 (56%) were found to be due to a major issue with insufficient buffer volume in single use buffer vials, others predominantly showing loss of sensitivity. The annual cost of running the programme, based on expenses recorded in years 2014-2016, an estimated volume of 700 lots per year and including replenishment of quality control samples, was estimated at US$ 178,500 ($US 255 per lot tested).ConclusionsDespite the clear benefits of the centralized LT programme and its low cost compared with the potential costs of each country establishing its own PMS system for RDTs, funding concerns have made its future beyond 2020 uncertain. In order to manage the risks of misdiagnosis due to low quality RDTs, and to ensure the continued safety and reliability of malaria case management, there is a need to ensure that an effective and implementable approach to RDT quality control continues to be available to programmes in endemic countries.

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

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

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

BackgroundThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS-CoV-2 aim to identify previous SARS-CoV-2 infection, and may help to confirm the presence of current infection.ObjectivesTo assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection, and the accuracy of antibody tests for use in seroprevalence surveys.Search methodsWe undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020.Selection criteriaWe included test accuracy studies of any design that evaluated antibody tests (including enzyme-linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS-CoV-2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS-CoV-2 infection. We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR) and clinical diagnostic criteria).Data collection and analysisWe assessed possible bias and applicability of the studies using the QUADAS-2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random-effects logistic regression where appropriate, stratifying by time since post-symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs).Main resultsWe included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS-CoV-2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in-house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi-group designs (n = 29), inclusion of only COVID-19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID-19 infection. There were no studies exclusively in asymptomatic participants. Two-thirds of the studies (n = 33) defined COVID-19 cases based on RT-PCR results alone, ignoring the potential for false-negative RT-PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post-symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post-symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False-positive results were more common where COVID-19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post-symptom onset. At a prevalence of 20%, a likely value in surveys in high-risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands.Authors' conclusionsThe sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease. The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.

Diagnostic Testing for Severe Acute Respiratory Syndrome-Related Coronavirus 2: A Narrative Review.

Diagnostic testing to identify persons infected with severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infection is central to control the global pandemic of COVID-19 that began in late 2019. In a few countries, the use of diagnostic testing on a massive scale has been a cornerstone of successful containment strategies. In contrast, the United States, hampered by limited testing capacity, has prioritized testing for specific groups of persons. Real-time reverse transcriptase polymerase chain reaction-based assays performed in a laboratory on respiratory specimens are the reference standard for COVID-19 diagnostics. However, point-of-care technologies and serologic immunoassays are rapidly emerging. Although excellent tools exist for the diagnosis of symptomatic patients in well-equipped laboratories, important gaps remain in screening asymptomatic persons in the incubation phase, as well as in the accurate determination of live viral shedding during convalescence to inform decisions to end isolation. Many affluent countries have encountered challenges in test delivery and specimen collection that have inhibited rapid increases in testing capacity. These challenges may be even greater in low-resource settings. Urgent clinical and public health needs currently drive an unprecedented global effort to increase testing capacity for SARS-CoV-2 infection. Here, the authors review the current array of tests for SARS-CoV-2, highlight gaps in current diagnostic capacity, and propose potential solutions.

The good and the bad: using C reactive protein to distinguish bacterial from non-bacterial infection among febrile patients in low-resource settings.

C reactive protein (CRP), a marker for the presence of an inflammatory process, is the most extensively studied marker for distinguishing bacterial from non-bacterial infections in febrile patients. A point-of-care test for bacterial infections would be of particular use in low-resource settings where other laboratory diagnostics are not always available, antimicrobial resistance rates are high and bacterial infections such as pneumonia are a leading cause of death. This document summarises evidence on CRP testing for bacterial infections in low-income and middle-income countries (LMICs). With a push for universal health coverage and prevention of antimicrobial resistance, it is important to understand if CRP might be able to do the job. The use of CRP polarised the global health community and the aim of this document is to summarise the 'good and the bad' of CRP in multiple settings in LMICs. In brief, the literature that was reviewed suggests that CRP testing may be beneficial in low-resource settings to improve rational antibiotic use for febrile patients, but the positive predictive value is insufficient to allow it to be used alone as a single tool. CRP testing may be best used as part of a panel of diagnostic tests and algorithms. Further studies in low-resource settings, particularly with regard to impact on antibiotic prescribing and cost-effectiveness of CRP testing, are warranted.

Diagnosis of SARS-CoV-2 infection and COVID-19: accuracy of signs and symptoms; molecular, antigen, and antibody tests; and routine laboratory markers

Objectives: This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows:. To assess the diagnostic accuracy of laboratory real-time polymerase chain reaction (RT-PCR) and other laboratory molecular tests to determine if a person presenting in the community or in secondary care has SARS-CoV-2 infection. To assess the diagnostic accuracy of each rapid PCR and antigen test to determine if a person presenting in the community or in secondary care has SARS-CoV-2 infection. To assess the diagnostic accuracy of each antibody test to determine if a person presenting in the community or in secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection. To assess the diagnostic accuracy of signs and symptoms to determine if a person presenting in the community, general practice, or at the emergency department has SARS-CoV-2 infection, COVID-19 pneumonia, or severe COVID-19 pneumonia/ARDS requiring hospital admission. To assess the diagnostic accuracy of routine laboratory testing to determine if a person has COVID-19 pneumonia or SARS-CoV-2 infection. Secondary objectives Where data are available, for reviews #1 to #5, we will investigate the accuracy (either by stratified analysis or meta-regression) according to: laboratory method, days of symptoms, severity of symptoms, reference standard, sample type, study design, setting; test brand and version, days of symptoms, severity of symptoms, reference standard, sample type, study design, setting; current infection or past infection, test brand and version, days of symptoms or days since symptoms resolved, reference standard, study design, setting; days of symptoms, reference standard, study design, setting; specific measurement or biomarker, days of symptoms, severity of symptoms, reference standard, sample type, study design, setting.

Prioritising pathogens for the management of severe febrile patients to improve clinical care in low- and middle-income countries.

BackgroundSevere febrile illness without a known source (SFWS) is a challenge for clinicians when deciding how to manage a patient, particularly given the wide spectrum of potential aetiologies that contribute to fever. These infections are difficult to distinguish clinically, and accurate diagnosis requires a plethora of diagnostics including blood cultures, imaging techniques, molecular or serological tests, and more. When laboratory services are available, a limited test menu hinders clinical decision-making and antimicrobial stewardship, leading to empiric treatment and suboptimal patient outcomes. To specifically address SFWS, this work aimed to identify priority pathogens for a globally applicable panel for fever causing pathogens.MethodA pragmatic two-pronged approach combining currently available scientific data in an analytical hierarchy process and systematically gathered expert input, was designed to address the lack of comprehensive global aetiology data. The expert re-ranked list was then further adapted for a specific use case to focus on community acquired infections in whole blood specimens. The resulting list was further analysed to address different geographical regions (Asia, Africa, and Latin America), and Cohen kappa scores of agreement were calculated.ResultsThe expert ranked prioritized pathogen list generated as part of this two-pronged approach included typhoidal Salmonella, Plasmodium species and Mycobacterium tuberculosis as the top 3 pathogens. This pathogen list was then further adapted for the SFWS use case to develop a final pathogen list to inform product development. Subsequent analysis comparing the relevance of the SFWS pathogen list to multiple populations and geographical regions showed that the SFWS prioritized list had considerable utility across Africa and Asia, but less so for Latin America. In addition, the list showed high levels of agreement across different patient sub-populations, but lower relevance for neonates and symptomatic HIV patients.ConclusionThis work highlighted once again the challenges of prioritising in global health, but it also shows that taking a two-pronged approach, combining available prevalence data with expert input, can result in a broadly applicable priority list. This comprehensive utility is particularly important in the context of product development, where a sufficient market size is essential to achieve a sustainable commercialized diagnostic product to address SFWS.

Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile.

Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.

Target Product Profile for a mobile app to read rapid diagnostic tests to strengthen infectious disease surveillance.

The essential role of rapid diagnostic tests (RDTs) in disease control is compromised every time a test is not performed correctly or its result is not reported accurately and promptly. A mobile app that utilizes the camera and connectivity of a common smartphone can fill this role of supporting the test's proper execution and the automatic transmission of results. In a consensus process with 51 expert participants representing the needs of clinical users, healthcare programs, health information systems, surveillance systems, and global public health stakeholders, we developed a Target Product Profile describing the minimal and optimal characteristics of such an app. We collected feedback over two rounds and refined the characteristics to arrive at a preferred agreement level of greater than 75%, with an average of 92% agreement (range: 79-100%). As per this feedback, such an app should be compatible with many RDTs and mobile devices without needing accessories. The app should assist the user with RDT-specific instructions, include checks to facilitate quality control of the testing process and suggest results with ≥ 95% accuracy across common lighting conditions while allowing the user to determine the final result. Data from the app must be under the control of the health program that operates it, and the app should support at least one of the common data exchange formats HL7, FHIR, ASTM or JSON. The Target Product Profile also lays out the minimum data security and privacy requirements for the app.

REDEFINING TYPHOID DIAGNOSIS: WHAT SHOULD A BETTER TEST LOOK LIKE, AND WHAT INNOVATIONS ARE AVAILABLE TO MEET THE NEEDS?

PROSPECTIVE PERFORMANCE EVALUATION OF A COMBINED MALARIA/CRP RAPID DIAGNOSTIC TEST IN INDIA

From Biomarker Discovery to Differential Diagnosis in Malaria Endemic Settings

A REPORT ON THE INTEGRATION OF A MALARIA RAPID DIAGNOSTIC TEST IN A POINT OF CARE CLINICAL DECISION SUPPORT PLATFORM, MEDSCINC, FOR USE IN PRIMARY HEALTHCARE SETTINGS IN KANO STATE, NIGERIA

Diagnostic tools used in the evaluation of acute febrile illness in South India: a scoping review.

BackgroundAcute febrile illness (AFI) is characterized by malaise, myalgia and a raised temperature that is a nonspecific manifestation of infectious diseases in the tropics. The lack of appropriate diagnostics for the evaluation of AFI leads to increased morbidity and mortality in resource-limited settings, specifically low-income countries like India. The review aimed to identify the number, type and quality of diagnostics used for AFI evaluation during passive case detection at health care centres in South India.MethodsA scoping review of peer-reviewed English language original research articles published between 1946-July 2018 from four databases was undertaken to assess the type and number of diagnostics used in AFI evaluation in South India. Results were stratified according to types of pathogen-specific tests used in AFI management.ResultsThe review included a total of 40 studies, all conducted in tertiary care centres (80% in private settings). The studies demonstrated the use of 5-22 tests per patient for the evaluation of AFI. Among 25 studies evaluating possible causes of AFI, 96% tested for malaria followed by 80% for dengue, 72% for scrub typhus, 68% for typhoid and 60% for leptospirosis identifying these as commonly suspected causes of AFI. 54% studies diagnosed malaria with smear microscopy while others diagnosed dengue, scrub typhus, typhoid and leptospirosis using antibody or antigen detection assays. 39% studies used the Weil-Felix test (WFT) for scrub typhus diagnosis and 82% studies used the Widal test for diagnosing typhoid.ConclusionsThe review demonstrated the use of five or more pathogen-specific tests in evaluating AFI as well as described the widespread use of suboptimal tests like the WFT and Widal in fever evaluation. It identified the need for the development of better-quality tests for aetiological diagnosis and improved standardised testing guidelines for AFI.

Redefining typhoid diagnosis: what would an improved test need to look like?

IntroductionTyphoid fever is one of the most common bacterial causes of acute febrile illness in the developing world, with an estimated 10.9 million new cases and 116.8 thousand deaths in 2017. Typhoid point-of-care (POC) diagnostic tests are widely used but have poor sensitivity and specificity, resulting in antibiotic overuse that has led to the emergence and spread of multidrug-resistant strains. With recent advances in typhoid surveillance and detection, this is the ideal time to produce a target product profile (TPP) that guides product development and ensure that a next-generation test meets the needs of users in the resource-limited settings where typhoid is endemic.MethodsA structured literature review was conducted to develop a draft TPP for a next-generation typhoid diagnostic test with minimal and optimal desired characteristics for 36 test parameters. The TPP was refined using feedback collected from a Delphi survey of key stakeholders in clinical medicine, microbiology, diagnostics and public and global health.ResultsA next-generation typhoid diagnostic test should improve patient management through the diagnosis and treatment of infection with acute Salmonella enterica serovars Typhi or Paratyphi with a sensitivity ≥90% and specificity ≥95%. The test would ideally be used at the lowest level of the healthcare system in settings without a reliable power or water supply and provide results in <15 min at a cost of ConclusionThis report outlines the first comprehensive TPP for typhoid fever and is intended to guide the development of a next-generation typhoid diagnostic test. An accurate POC test will reduce the morbidity and mortality of typhoid fever through rapid diagnosis and treatment and will have the greatest impact in reducing antimicrobial resistance if it is combined with diagnostics for other causes of acute febrile illness in a treatment algorithm.

Quantifying the incidence of severe-febrile-illness hospital admissions in sub-Saharan Africa.

Severe-febrile-illness (SFI) is a common cause of morbidity and mortality across sub-Saharan Africa (SSA). The burden of SFI in SSA is currently unknown and its estimation is fraught with challenges. This is due to a lack of diagnostic capacity for SFI in SSA, and thus a dearth of baseline data on the underlying etiology of SFI cases and scant SFI-specific causative-agent prevalence data. To highlight the public health significance of SFI in SSA, we developed a Bayesian model to quantify the incidence of SFI hospital admissions in SSA. Our estimates indicate a mean population-weighted SFI-inpatient-admission incidence rate of 18.4 (6.8-31.1, 68% CrI) per 1000 people for the year 2014, across all ages within areas of SSA with stable Plasmodium falciparum transmission. We further estimated a total of 16,200,337 (5,993,249-27,321,779, 68% CrI) SFI hospital admissions. This analysis reveals the significant burden of SFI in hospitals in SSA, but also highlights the paucity of pathogen-specific prevalence and incidence data for SFI in SSA. Future improvements in pathogen-specific diagnostics for causative agents of SFI will increase the abundance of SFI-specific prevalence and incidence data, aid future estimations of SFI burden, and enable clinicians to identify SFI-specific pathogens, administer appropriate treatment and management, and facilitate appropriate antibiotic use.