Evaluating the performance characteristics of five lateral flow assays for the detection of the SARS-CoV-2 nucleocapsid antigen

In response to the COVID-19 pandemic, lateral flow assays (LFAs) for the detection of SARS-CoV-2 antigen have been proposed as a complementary option to the more costly and time consuming reverse-transcriptase polymerase chain reaction (RT-PCR). We assessed five commercially available SARS-CoV-2 antigen detecting LFAs (ASSUT EUROPE (Rome, Italy), Besthree (Taizhou, China), Encode (Zhuhai, China), Fortress (Antrim UK), and Hughes Medical (Buckinghamshire, UK), using samples collected from hospitalised individuals with COVID-19 and compared these results against established RT-PCR assays with the aim of estimating test performance characteristics. We performed a diagnostic accuracy study of the five LFAs on 110 inpatients with confirmed COVID-19 and 75 COVID-19 negative control participants. Assay evaluation was performed using a modified version of each manufacturer's protocol allowing for parallel testing of a single sample on multiple assays. Additional variables were studied including infection acquisition, oxygenation requirements at time of swabbing, and patient outcomes. The 110 patients were 48% (53) female, with mean age 67 years (range 26-100 years), and 77% (85) cases were community onset SARS-CoV-2. Across the five assays, sensitivity ranged from 64 (95% CI 53-73) to 76% (95% CI 65-85); Fortress performed best with sensitivity of 76% (95% CI 65-85). Specificity was high across all assays with 4/5 LFAs achieving 100%. LFA sensitivity was not dependant on RT-PCR cycle thresholds. SARS-CoV-2 antigen detecting LFAs may complement RT-PCR testing to facilitate early diagnosis and provide community testing strategies for identification of patients with COVID-19, however we find suboptimal test performance characteristics across a range of commercially available manufacturers, below WHO and MHRA pre-set sensitivity performance thresholds. With such variation in sensitivity between LFAs and PCR testing and between assay brands, we advise caution in the deployment of LFAs outside of environments with clinical oversight.

Point-of-care SARS-CoV-2 serological assays for enhanced case finding in a UK inpatient population

Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) has become a global pandemic. Case identification is currently made by real-time polymerase chain reaction (PCR) during the acute phase and largely restricted to healthcare laboratories. Serological assays are emerging but independent validation is urgently required to assess their utility. We evaluated five different point-of-care (POC) SARS-CoV-2 antibody test kits against PCR, finding concordance across the assays (n = 15). We subsequently tested 200 patients using the OrientGene COVID-19 IgG/IgM Rapid Test Cassette and find a sensitivity of 74% in the early infection period (day 5–9 post symptom onset), with 100% sensitivity not seen until day 13, demonstrating inferiority to PCR testing in the infectious period. Negative rate was 96%, but in validating the serological tests uncovered potential false-negatives from PCR testing late-presenting cases. A positive predictive value (PPV) of 37% in the general population precludes any use for general screening. Where a case definition is applied however, the PPV is substantially improved (95.4%), supporting use of serology testing in carefully targeted, high-risk populations. Larger studies in specific patient cohorts, including those with mild infection are urgently required to inform on the applicability of POC serological assays to help control the spread of SARS-CoV-2 and improve case finding of patients that may experience late complications.

Bacterial and fungal coinfection among hospitalized patients with COVID-19: a retrospective cohort study in a UK secondary-care setting

Objectives

To investigate the incidence of bacterial and fungal coinfection of hospitalized patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in this retrospective observational study across two London hospitals during the first UK wave of coronavirus disease 2019 (COVID-19).

Methods

A retrospective case series of hospitalized patients with confirmed SARS-CoV-2 by PCR was analysed across two acute NHS hospitals (20 February–20 April 2020; each isolate reviewed independently in parallel). This was contrasted to a control group of influenza-positive patients admitted during the 2019–2020 flu season. Patient demographics, microbiology and clinical outcomes were analysed.

Results

A total of 836 patients with confirmed SARS-CoV-2 were included; 27 (3.2%) of 836 had early confirmed bacterial isolates identified (0–5 days after admission), rising to 51 (6.1%) of 836 throughout admission. Blood cultures, respiratory samples, pneumococcal or Legionella urinary antigens and respiratory viral PCR panels were obtained from 643 (77%), 110 (13%), 249 (30%), 246 (29%) and 250 (30%) COVID-19 patients, respectively. A positive blood culture was identified in 60 patients (7.1%), of which 39 were classified as contaminants. Bacteraemia resulting from respiratory infection was confirmed in two cases (one each community-acquired Klebsiella pneumoniae and ventilator-associated Enterobacter cloacae). Line-related bacteraemia was identified in six patients (three Candida, two Enterococcus spp. and one Pseudomonas aeruginosa). All other community-acquired bacteraemias (n = 16) were attributed to nonrespiratory infection. Zero concomitant pneumococcal, Legionella or influenza infection was detected. A low yield of positive respiratory cultures was identified; Staphylococcus aureus was the most common respiratory pathogen isolated in community-acquired coinfection (4/24; 16.7%), with pseudomonas and yeast identified in late-onset infection. Invasive fungal infections (n = 3) were attributed to line-related infections. Comparable rates of positive coinfection were identified in the control group of confirmed influenza infection; clinically relevant bacteraemias (2/141; 1.4%), respiratory cultures (10/38; 26.3%) and pneumococcal-positive antigens (1/19; 5.3%) were low.

Conclusions

We found a low frequency of bacterial coinfection in early COVID-19 hospital presentation, and no evidence of concomitant fungal infection, at least in the early phase of COVID-19.

Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study

BACKGROUND

Infection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.

METHODS

An SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.

RESULTS

One hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21-98)  years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20-0.40). ROC AUC was 0.84 (95% CI: 0.76-0.91).

CONCLUSIONS

An SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.

New-onset anosmia and ageusia in adult patients diagnosed with SARS-CoV-2 infection

Objectives

We investigated the prevalence of anosmia and ageusia in adult patients with a laboratory-confirmed diagnosis of infection with severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2).

Methods

This was a retrospective observational analysis of patients infected with SARS-CoV-2 admitted to hospital or managed in the community and their household contacts across a London population during the period March 1st to April 1st, 2020. Symptomatology and duration were extracted from routinely collected clinical data and follow-up telephone consultations. Descriptive statistics were used.

Results

Of 386 patients, 141 (92 community patients, 49 discharged inpatients) were included for analysis; 77/141 (55%) reported anosmia and ageusia, nine reported only ageusia and three only anosmia. The median onset of anosmia in relation to onset of SARS-CoV-2 disease (COVID-19) symptoms (as defined by the Public Health England case definition) was 4 days (interquartile range (IQR) 5). Median duration of anosmia was 8 days (IQR 16). Median duration of COVID-19 symptoms in community patients was 10 days (IQR 8) versus 18 days (IQR 13.5) in admitted patients. As of April 1, 45 patients had ongoing COVID-19 symptoms and/or anosmia; 107/141 (76%) patients had household contacts, and of 185 non-tested household contacts 79 (43%) had COVID-19 symptoms with 46/79 (58%) reporting anosmia. Six household contacts had anosmia only.

Conclusions

Over half of the positive patients reported anosmia and ageusia, suggesting that these should be added to the case definition and used to guide self-isolation protocols. This adaptation may be integral to case findings in the absence of population-level testing. Until we have successful population-level vaccination coverage, these steps remain critical in the current and future waves of this pandemic.

Evaluating the risk of hyperkalaemia and acute kidney injury with cotrimoxazole: a retrospective observational study

OBJECTIVES

Increasing antimicrobial resistance has renewed interest in older, less used antimicrobials. Cotrimoxazole shows promise; however, hyperkalaemia and acute kidney injury (AKI) are potential complications. Identifying risk factors for and quantification of these events is required for safe use. This study aimed to evaluate predictors of cotrimoxazole-associated AKI and hyperkalaemia in a clinical setting.

METHODS

Patients prescribed cotrimoxazole were identified using electronic healthcare records over 3 years (1 April 2016 to 31 March 2019). Individual risk factors were recognized. Serum creatinine and potassium trends were analysed over the subsequent 21 days. AKI and patients with hyperkalaemia were classified using Kidney Disease Improving Global Outcomes (KDIGO) and laboratory criteria. Univariate and multiple logistic regression analyses were performed.

RESULTS

Among 214 patients prescribed cotrimoxazole, 42 (19.6%, 95% confidence interval (CI) 14.6-25.7) met AKI criteria and 33 (15.4%, 95% CI 11.0-21.1) developed hyperkalaemia. Low baseline estimated glomerular filtration rate (<60 mL/min/1.73 m², odds ratio (OR) 7.78, 95% CI 3.57-16.13, p < 0.0001) and cardiac disorders (OR 2.40, 95% CI 1.17-4.82, p 0.011) predicted AKI, while low baseline estimated glomerular filtration rate (<60 mL/min/1.73 m², OR 6.80, 95% CI 3.09-15.06, p < 0.0001) and higher baseline serum potassium (p 0.001) predicted hyperkalaemia. Low-dose cotrimoxazole (<1920 mg/d) was associated with lower AKI and hyperkalaemia risk (p 0.007 and 0.019 respectively). Early (within the first 2-4 days of therapy) serum creatinine changes predicted AKI (OR 3.65, 95% CI 1.73-7.41, p 0.001), and early serum potassium changes predicted hyperkalaemia (>0.6 mmol/L, OR 2.47, 95% CI 1.14-5.27, p 0.0236).

CONCLUSIONS

Cotrimoxazole-associated AKI and hyperkalaemia is frequent and dose dependent. Renal function, serum potassium and preexisting cardiac disorders should be evaluated before prescribing cotrimoxazole. Serum creatinine and potassium monitoring within first 2 to 4 days of treatment to identify susceptible patients is recommended, and the lowest effective dose ought to be prescribed.

Delivering precision antimicrobial therapy through closed-loop control systems

Sub-optimal exposure to antimicrobial therapy is associated with poor patient outcomes and the development of antimicrobial resistance. Mechanisms for optimizing the concentration of a drug within the individual patient are under development. However, several barriers remain in realizing true individualization of therapy. These include problems with plasma drug sampling, availability of appropriate assays, and current mechanisms for dose adjustment. Biosensor technology offers a means of providing real-time monitoring of antimicrobials in a minimally invasive fashion. We report the potential for using microneedle biosensor technology as part of closed-loop control systems for the optimization of antimicrobial therapy in individual patients.

Effect of adding a mobile health intervention to a multimodal antimicrobial stewardship programme across three teaching hospitals: an interrupted time series study

Objectives: To evaluate the impact of adding a mobile health (mHealth) decision support system for antibiotic prescribing to an established antimicrobial stewardship programme (ASP).

Methods: In August 2011, the antimicrobial prescribing policy was converted into a mobile application (app). A segmented regression analysis of interrupted time series was used to assess the impact of the app on prescribing indicators, using data (2008–14) from a biannual point prevalence survey of medical and surgical wards. There were six data points pre-implementation and six data points post-implementation.

Results: There was an increase in compliance with policy (e.g. compliance with empirical therapy or expert advice) in the two specialties of medicine (6.48%, 95% CI = −1.25 to 14.20) and surgery (6.63%, 95% CI = 0.15–13.10) in the implementation period, with a significant sudden change in level in surgery (P < 0.05). There was an increase, though not significant, in medicine (15.20%, 95% CI = −17.81 to 48.22) and surgery (35.97%, 95% CI = −3.72 to 75.66) in the percentage of prescriptions that had a stop/review date documented. The documentation of indication decreased in both medicine (−16.25%, 95% CI = −42.52 to 10.01) and surgery (−14.62%, 95% CI = −42.88 to 13.63).

Conclusions: Introducing the app into an existing ASP had a significant impact on the compliance with policy in surgery, and a positive, but not significant, effect on documentation of stop/review date in both specialties. The negative effect on the third indicator may reflect a high level of compliance pre-intervention, due to existing ASP efforts. The broader value of providing an antimicrobial policy on a digital platform, e.g. the reach and access to the policy, should be measured using indicators more sensitive to mHealth interventions.

A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?

OBJECTIVES

Clinical decision support systems (CDSS) for antimicrobial management can support clinicians to optimize antimicrobial therapy. We reviewed all original literature (qualitative and quantitative) to understand the current scope of CDSS for antimicrobial management and analyse existing methods used to evaluate and report such systems.

METHOD

PRISMA guidelines were followed. Medline, EMBASE, HMIC Health and Management and Global Health databases were searched from 1 January 1980 to 31 October 2015. All primary research studies describing CDSS for antimicrobial management in adults in primary or secondary care were included. For qualitative studies, thematic synthesis was performed. Quality was assessed using Integrated quality Criteria for the Review Of Multiple Study designs (ICROMS) criteria. CDSS reporting was assessed against a reporting framework for behaviour change intervention implementation.

RESULTS

Fifty-eight original articles were included describing 38 independent CDSS. The majority of systems target antimicrobial prescribing (29/38;76%), are platforms integrated with electronic medical records (28/38;74%), and have a rules-based infrastructure providing decision support (29/38;76%). On evaluation against the intervention reporting framework, CDSS studies fail to report consideration of the non-expert, end-user workflow. They have narrow focus, such as antimicrobial selection, and use proxy outcome measures. Engagement with CDSS by clinicians was poor.

CONCLUSION

Greater consideration of the factors that drive non-expert decision making must be considered when designing CDSS interventions. Future work must aim to expand CDSS beyond simply selecting appropriate antimicrobials with clear and systematic reporting frameworks for CDSS interventions developed to address current gaps identified in the reporting of evidence.

Behaviour change interventions to influence antimicrobial prescribing: a cross-sectional analysis of reports from UK state-of-the-art scientific conferences

BACKGROUND

To improve the quality of antimicrobial stewardship (AMS) interventions the application of behavioural sciences supported by multidisciplinary collaboration has been recommended. We analysed major UK scientific research conferences to investigate AMS behaviour change intervention reporting.

METHODS

Leading UK 2015 scientific conference abstracts for 30 clinical specialties were identified and interrogated. All AMS and/or antimicrobial resistance(AMR) abstracts were identified using validated search criteria. Abstracts were independently reviewed by four researchers with reported behavioural interventions classified using a behaviour change taxonomy.

RESULTS

Conferences ran for 110 days with >57,000 delegates. 311/12,313(2.5%) AMS-AMR abstracts (oral and poster) were identified. 118/311(40%) were presented at the UK's infectious diseases/microbiology conference. 56/311(18%) AMS-AMR abstracts described behaviour change interventions. These were identified across 12/30(40%) conferences. The commonest abstract reporting behaviour change interventions were quality improvement projects [44/56 (79%)]. In total 71 unique behaviour change functions were identified. Policy categories; "guidelines" (16/71) and "service provision" (11/71) were the most frequently reported. Intervention functions; "education" (6/71), "persuasion" (7/71), and "enablement" (9/71) were also common. Only infection and primary care conferences reported studies that contained multiple behaviour change interventions. The remaining 10 specialties tended to report a narrow range of interventions focusing on "guidelines" and "enablement".

CONCLUSION

Despite the benefits of behaviour change interventions on antimicrobial prescribing, very few AMS-AMR studies reported implementing them in 2015. AMS interventions must focus on promoting behaviour change towards antimicrobial prescribing. Greater focus must be placed on non-infection specialties to engage with the issue of behaviour change towards antimicrobial use.

Forecasting carbapenem resistance from antimicrobial consumption surveillance: Lessons learnt from an OXA-48-producing Klebsiella pneumoniae outbreak in a West London renal unit

This study aimed to forecast the incidence rate of carbapenem resistance and to assess the impact of an antimicrobial stewardship intervention using routine antimicrobial consumption surveillance data. Following an outbreak of OXA-48-producing Klebsiella pneumoniae (January 2008-April 2010) in a renal cohort in London, a forecasting ARIMA model was derived using meropenem consumption data [defined daily dose per 100 occupied bed-days (DDD/100OBD)] from 2005-2014 as a predictor of the incidence rate of OXA-48-producing organisms (number of new cases/year/100,000OBD). Interrupted times series assessed the impact of meropenem consumption restriction as part of the outbreak control. Meropenem consumption at lag -1 year (the preceding year), highly correlated with the incidence of OXA-48-producing organisms (r=0.71; P=0.005), was included as a predictor within the forecasting model. The number of cases/100,000OBD for 2014-2015 was estimated to be 4.96 (95% CI 2.53-7.39). Analysis of meropenem consumption pre- and post-intervention demonstrated an increase of 7.12 DDD/100OBD/year (95% CI 2.97-11.27; P<0.001) in the 4 years preceding the intervention, but a decrease thereafter. The change in slope was -9.11 DDD/100OBD/year (95% CI -13.82 to -4.39). Analysis of alternative antimicrobials showed a significant increase in amikacin consumption post-intervention from 0.54 to 3.41 DDD/100OBD/year (slope +0.72, 95% CI 0.29-1.15; P=0.01). Total antimicrobials significantly decreased from 176.21 to 126.24 DDD/100OBD/year (P=0.05). Surveillance of routinely collected antimicrobial consumption data may provide a key warning indicator to anticipate increased incidence of carbapenem-resistant organisms. Further validation using real-time data is needed.

Exploring the epidemiology of carbapenem-resistant Gram-negative bacteria in west London and the utility of routinely collected hospital microbiology data

OBJECTIVES

The objective of this study was to identify carbapenem-resistant organisms using routinely collected local microbiology data and describe the epidemiology of carbapenem resistance in two London teaching hospitals.

METHODS

Data on inpatients infected or colonized with Gram-negative organisms between March 2009 and February 2012 were extracted. A computer algorithm was developed incorporating internationally recognized criteria to distinguish carbapenem-resistant organisms. Multivariable analysis was conducted to identify factors associated with infection or colonization with carbapenem-resistant organisms. Binomial regression was performed to detect changes in resistance trends over time.

RESULTS

Yearly incidence of carbapenem resistance was observed to be increasing, with significant increasing trends in Acinetobacter baumannii (47.1% in 2009-10 to 77.2% in 2011-12; P<0.001) and Enterobacter spp. (2.2% in 2009-10 to 11.5% in 2011-12; P<0.001). Single-variable and multivariable analysis demonstrated differences in the proportion of carbapenem-resistant isolates across all variables investigated, including age, sex and clinical specialty; in the latter organism-specific niches were identified. Patients in the youngest age group (16-24 years old) had the highest odds of being infected or colonized with carbapenem-resistant isolates of Escherichia coli, Klebsiella spp. or Pseudomonas aeruginosa. Furthermore, proportions of carbapenem-resistant organisms differed between the hospitals.

CONCLUSIONS

Carbapenem resistance is an emerging problem within the UK inpatient healthcare setting. This is not an issue confined to the Enterobacteriaceae and fine-resolution surveillance is needed to identify at-risk groups. Regular analysis of routinely collected data can provide insight into the evolving carbapenem-resistance threat, with the ability to inform efforts to prevent the spread of resistance.

Syndromic surveillance of surgical site infections--a case study in coronary artery bypass graft patients

OBJECTIVE

There is a wealth of data routinely collected and stored by healthcare facilities, which are not consistently exploited for surveillance of healthcare associated infections (HCAI). Syndromic surveillance has not yet been widely applied to HCAI. This study aimed to create syndromic surveillance for surgical site infections (SSI) following coronary artery bypass graft (CABG) procedures.

METHODS

A cohort of CABG patients from Imperial College Healthcare NHS Trust was investigated. Data from the local Patient Administration System, Laboratory Information Management System, radiology department, cardiac registry and Health Protection Agency SSI surveillance were linked. This data was explored for biological markers and proxies of infection, which were used to develop syndromic surveillance algorithms; sensitivity analysis was used to determine the best algorithms.

RESULTS

303 patients were included, with a SSI incidence of 6.6%. Wound culture requests, raised platelet and fibrinogen levels were all found to be good indicators of SSI. Two algorithms were generated, one to detect all SSI (sensitivity: 90%; specificity: 93.8%) and one to detect organ space infections specifically (sensitivity: 100%; specificity: 98.5%).

CONCLUSION

Data which is routinely collected and stored in healthcare facilities can be used for syndromic surveillance of SSI, allowing for an efficient surveillance system without the need for resource intensive data collection.

An analysis of the development and implementation of a smartphone application for the delivery of antimicrobial prescribing policy: lessons learnt

Objectives

Smartphone usage amongst clinicians is widespread. Yet smartphones are not widely used for the dissemination of policy or as clinical decision support systems. We report here on the development, adoption and implementation process of the Imperial Antimicrobial Prescribing Application across five teaching hospitals in London.

Methods

Doctors and clinical pharmacists were recruited to this study, which employed a mixed methods in-depth case-study design with focus groups, structured pre- and post-intervention survey questionnaires and live data on application uptake. The primary outcome measure was uptake of the application by doctors and its acceptability. The development and implementation processes were also mapped.

Results

The application was downloaded by 40% (376) of junior doctors with smartphones (primary target user group) within the first month and by 100% within 12 months. There was an average of 1900 individual access sessions per month, compared with 221 hits on the Intranet version of the policy. Clinicians (71%) reported that using the application improved their antibiotic knowledge.

Conclusions

Clinicians rapidly adopted the mobile application for antimicrobial prescribing at the point of care, enabling the policy to reach a much wider audience in comparison with paper- and desktop-based versions of the policy. Organizations seeking to optimize antimicrobial prescribing should consider utilizing mobile technology to deliver point-of-care decision support. The process revealed a series of barriers, which will need to be addressed at individual and organizational levels to ensure safe and high-quality delivery of local policy at the point of care.