Publications
199 results found
Denny S, Rawson T, Hart P, et al., 2021, Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis, BMC Infectious Diseases, Vol: 21, Pages: 1-9, ISSN: 1471-2334
Background – We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across five London hospitals.Methods – A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across five acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation.Results –119,584 blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst all CoNS BSI were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p=0.013), CoNS central line associated BSIs (CLABSI) (p<0.01) and CoNS non-CLABSI (p<0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p=0.631) and S. aureus (p=0.617) BSI did not vary significant throughout the study period. Conclusions – Significantly fewer than expected Enterobacterales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, with evidence of increased CLABSI, but also likely contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.
Denny S, Abdolrasouli A, Elamin T, et al., 2021, A retrospective multicenter analysis of candidaemia among COVID-19 patients during the first UK pandemic wave, Journal of Infection, Vol: 82, Pages: 276-316, ISSN: 0163-4453
Low R, Young K, Verani L, et al., 2021, Point of Care Testing for Tetanus Immunity: A Systematic Review, ASIT VIRTUAL SURGICAL SUMMIT, Publisher: OXFORD UNIV PRESS, ISSN: 0007-1323
Pallett SJC, Jones R, Randell P, et al., 2021, Structured serological testing is an essential component to investigating SARS-CoV-2 reinfection, Lancet Infectious Diseases, Vol: 21, Pages: 598-599, ISSN: 1473-3099
Pallett SJ, Brown CS, Mughal N, et al., 2021, Optimising the initial investigation of suspected cases of SARS-CoV-2 reinfection, Travel Medicine and Infectious Disease, Vol: 42, Pages: 102078-102078, ISSN: 1477-8939
Pallett SC, Jones R, Randell P, et al., 2021, Structured serological testing is an essential component to investigating SARS-CoV-2 reinfection (Jan, 10.1016/S1473-3099(20)30990-7, 2021), LANCET INFECTIOUS DISEASES, Vol: 21, Pages: E81-E81, ISSN: 1473-3099
Abdolrasouli A, Gibani MM, de Groot T, et al., 2021, A pseudo-outbreak of Rhinocladiella similis in a bronchoscopy unit of a tertiary care teaching hospital in London, United Kingdom., Mycoses: diagnosis, therapy and prophylaxis of fungal diseases, Vol: 64, Pages: 394-404, ISSN: 0933-7407
Outbreaks of fungal infections due to emerging and rare species are increasingly reported in healthcare settings. We investigated a pseudo-outbreak of Rhinocladiella similis in a bronchoscopy unit of a tertiary care teaching hospital in London, UK. We aimed to determine route of healthcare-associated transmission and prevent additional infections. From July 2018 through February 2019, we detected a pseudo-outbreak of R. similis isolated from bronchoalveolar lavage (BAL) fluid samples collected from nine patients who had undergone bronchoscopy in a multispecialty teaching hospital, during a period of 8 months. Isolates were identified by MALDI-TOF mass spectrometry. Antifungal susceptibility testing was performed by EUCAST broth microdilution. To determine genetic relatedness among R. similis isolates, we undertook amplified fragment length polymorphism analysis. To determine the potential source of contamination, an epidemiological investigation was carried out. We reviewed patient records retrospectively and audited steps taken during bronchoscopy as well as the subsequent cleaning and decontamination procedures. Fungal cultures were performed on samples collected from bronchoscopes and automated endoscope washer-disinfector systems. No patient was found to have an infection due to R. similis either before or after bronchoscopy. One bronchoscope was identified to be used among all affected patients with positive fungal cultures. Physical damage was found in the index bronchoscope; however, no fungus was recovered after sampling of the affected scope or the rinse water of automated endoscope washer-disinfectors. Use of the scope was halted, and, during the following 12-month period, Rhinocladiella species were not isolated from any BAL specimen. All pseudo-outbreak isolates were identified as R. similis with high genetic relatedness (>90% similarity) on ALFP analysis. The study emphasises the emergence of a rare and uncommon black yeast R
Pallett SJC, Denny S, Patel A, et al., 2021, Point-of-care SARS-CoV-2 serological assays for enhanced case finding in a UK inpatient population., Scientific Reports, Vol: 11, Pages: 1-8, ISSN: 2045-2322
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.
Moshe M, Daunt A, Flower B, et al., 2021, SARS-CoV-2 lateral flow assays for possible use in national covid-19 seroprevalence surveys (REACT2): diagnostic accuracy study, BMJ: British Medical Journal, Vol: 372, Pages: 1-8, ISSN: 0959-535X
Objective: To evaluate the performance of new lateral flow immunoassays (LFIAs) suitable for use in a national COVID-19 seroprevalence programme (REACT2).Design: Laboratory sensitivity and specificity analyses were performed for seven LFIAs on a minimum of 200 sera from individuals with confirmed SARS-CoV-2 infection, and 500 pre-pandemic sera respectively. Three LFIAs were found to have a laboratory sensitivity superior to the finger-prick sensitivity of the LFIA currently used in REACT2 seroprevalence studies (84%). These LFIAs were then further evaluated through finger-prick testing on participants with confirmed previous SARS-CoV-2 infection. Two LFIAs (Surescreen, Panbio) were evaluated in clinics in June-July, 2020, and a third LFIA (AbC-19) in September, 2020. A Spike protein enzyme-linked immunoassay (S-ELISA) and hybrid double antigen binding assay (DABA) were used as laboratory reference standards.Setting: Laboratory analyses were performed at Imperial College, London and University facilities in London, UK. Research clinics for finger-prick sampling were run in two affiliated NHS trusts.Participants: Sensitivity analysis on sera were performed on 320 stored samples from previous participants in the REACT2 programme with confirmed previous SARS-CoV-2 infection. Specificity analysis was performed using 1000 pre-pandemic sera. 100 new participants with confirmed previous SARS-CoV-2 infection attended study clinics for finger-prick testing.Main outcome measures: The accuracy of LFIAs in detecting IgG antibodies to SARS-CoV-2 in comparison to two in-house ELISAs.Results: The sensitivity of seven new LFIAs using sera varied between 69% and 100% (vs S-ELISA/hybrid DABA). Specificity using sera varied between 99.6% and 100%. Sensitivity on finger-prick testing for Panbio, Surescreen and AbC-19 was 77% (CI 61.4 to 88.2), 86% (CI 72.7 to 94.8) and 69% (CI 53.8 to 81.3) respectively vs S-ELISA/hybrid DABA. Sensitivity for sera from matched clinical samples performe
Heard K, Killington K, Mughal N, et al., 2021, Clinical outcomes of temocillin use for invasive Enterobacterales infections; a single centre retrospective analysis, Journal of Antimicrobial Chemotherapy, Vol: 3, Pages: 1-7, ISSN: 0305-7453
Background: With increasing frequency of resistant Gram-negative bacteria, temocillin has potential utility in reducing carbapenem use. The 2020 EUCAST guideline changes to temocillin breakpoints and reclassifies isolates with a minimum inhibitory concentration from 0.001-16mg/L as ‘susceptible, increased exposure’ necessitating 6g/day rather than the previous 4g/day, associated with significant cost implications.Objectives: We explore the clinical utility and treatment failure rate of temocillin at 4g/day dosing.Method: All adult inpatient electronic prescriptions of temocillin (3 days or greater) from March 2016 to October 2019 were retrieved using a clinical decision support system (ICNET®). Treatment success was defined as survival, no switch to broad-spectrum agent for the same indication, no subsequent recrudescence of infection, occurring within 30days.Results: Temocillin was used in 205 eligible patient-episodes, median age 79years (IQR:71-87years), 42.4% female. Median temocillin course length 5.9days (IQR:4.6-7.8days). Indications for use: urinary tract infection (UTI) (n=141), pneumonia (n=53), other (n=11). 144 (70.2%) patients had targeted treatment; 74 (36.1%) against Escherichia coli, 70 (34.4%) other Enterobacterales. 130 (63%) patients received 4g/day; the remaining patients had reduced renal function with dosing in accordance with guidance. Overall temocillin treatment success was 79.5%; highest when used to treat UTI 85.8% (versus 67.9% in respiratory infections, p=0.008). Empirical treatment demonstrated 82.0% (50/61) success (versus 78.5% (113/144) among targeted treatment, p=0. 71). Discussion: 4g/day temocillin is an effective and safe alternative in treating patients with Gram-negative infections, but should be considered in the context of patient age and co-morbidities. Increased dosing or alternate strategies may be indicated when the infection is not of a urinary source.
Asumang J, Heard K, Troise O, et al., 2021, Evaluation of a thrice weekly administration of teicoplanin in the outpatient setting; retrospective observational multi-centre study, JAC-Antimicrobial Resistance, Vol: 3, ISSN: 2632-1823
Introduction:The glycopeptide teicoplanin is commonly utilised to facilitate Outpatient Parenteral Antimicrobial Therapy (OPAT). Licensed for once daily maintenance dosing, teicoplanin’s long half-life allows for less frequent dosing (e.g. thrice weekly) following successful loading. This service evaluation reviews the safety and effectiveness of a novel thrice weekly teicoplanin dosing regimen.MethodsA retrospective, observational study was conducted at Chelsea & Westminster hospital (March 2018 – July 2020), evaluating trough serum teicoplanin concentrations for patients receiving >5 days of teicoplanin in the OPAT setting. Teicoplanin dosing and administration (once daily versus thrice weekly), clinical outcomes, and therapeutic levels were analysed for all patients. The project was registered with clinical governance locally.ResultsA total of 82 patients treated with teicoplanin in the OPAT service where included; 53/82 receiving thrice weekly and 29/82 receiving once daily dosing. Mean teicoplanin trough levels were similar in both groups (26.2mg/L and 25.8mg/L in once daily and thrice weekly groups, p=0.8895). High clinical success rates were recorded in both groups (25/29 [86.2%] versus 50/53 [94.3%]). No correlation with clinical outcomes and initial teicoplanin serum levels was identified. Normal renal function (>90mL/min) was associated with lower teicoplanin serum concentrations (21.4mg/L[±10.1] versus 29.7mg/L[SD±14], p = 0.0178) in the thrice weekly dosed group but not with the once daily dosed group (mean 28.2mg/L[±9.4] versus 23.7mg/L[±9.9], p = 0.2201). ConclusionsThis study supports thrice weekly teicoplanin as a convenient and effective OPAT for administration in the OPAT setting. Therapeutic drug monitoring is advised to adjust for intra-patient variability.
Abdulaal A, Patel A, Al-Hindawi A, et al., 2021, Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis (Preprint)
<sec> <title>BACKGROUND</title> <p>The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow.</p> </sec> <sec> <title>OBJECTIVE</title> <p>Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting.</p> </sec> <sec> <title>METHODS</title> <p>Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study.</p> </sec> <sec> <title>RESULTS</title> <p>All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of “excellent.” The clinicians found the app intuitive and easy to use, with the
Rela M, Opel S, Williams S, et al., 2021, Operating Room Fomites as Potential Sources for Microbial Transmission in Burns Theatres, European Burn Journal, Vol: 2, Pages: 1-8, ISSN: 2673-1991
Background: Burn patients are susceptible to healthcare-associated infections. Contaminated surfaces play a role in microbial transmission. This study aimed to quantify the degree of contamination of burns theatre fomites during routine clinical use. Methods: The Patslide Patient Transfer Board (PAT slide) and operating table were investigated using two methods—bacterial swabs to culture viable organisms and adenosine triphosphate (ATP) swabs to measure biological material. Both items were sampled four times a day: before the first case, immediately after a case, immediately before the next case after cleaning and after the terminal clean. Results: Among 82 bacterial samples, four organisms were isolated, including Staphylococcus aureus, Enterobacter cloacae (E. cloacae) x2 and Pseudomonas aeruginosa (P. aeruginosa), all from the PAT slide. The E. cloacae persisted after cleaning. In 9/82 swabs, the ATP count was >10 relative light units (RLU). In all cases where an organism was identified, the ATP count was >10 RLU. Hence the sensitivity and specificity of ATP > 10 RLU in detecting an organism were 100% and 94% respectively. Conclusions: Within burns theatres, there are instances of bacterial contamination on surfaces that persist despite cleaning. ATP luminometers as a point-of-care device may have a role in determining the cleanliness of surfaces, potentially minimizing onwards-bacterial transmission.
McKean AR, Batten G, Macneal P, et al., 2021, Utilising multiplex PCR technology for rapid microbial diagnosis in hand and upper limb infections, Journal of Plastic, Reconstructive & Aesthetic Surgery, Vol: 74, Pages: 223-243, ISSN: 1748-6815
Rajput J, Moore LS, Mughal N, et al., 2020, Evaluating the risk of hyperkalaemia and acute kidney injury with cotrimoxazole: a retrospective observational study, Clinical Microbiology and Infection, Vol: 26, Pages: 1651-1657, ISSN: 1198-743X
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 aims to evaluate predictors of cotrimoxazole-associated AKI and hyperkalaemia in a clinical setting. METHOD: Patients prescribed cotrimoxazole were identified using electronic-healthcare records over three years (01/04/2016-31/03/2019). Individual risk-factors were recognised. Serum creatinine and potassium trends were analysed over the subsequent 21-days. AKI and hyperkalaemic patients 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 co-trimoxazole, 42 (19.6%, 95%CI 14.6-25.7%) met AKI criteria and 33 (15.4%, 95%CI 11.0-21.1%) developed hyperkalaemia. Low baseline eGFR (<60mls/min/1,73m2, 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 eGFR (<60mls/min/1.73m2, 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 (<1920mg/day) was associated with lower AKI and hyperkalaemia risk (p=0.007 and 0.019, respectively). Early (within 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 (>0.6mmol/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 pre-existing cardiac disorders should be evaluated before prescribing cotrimoxazole. Serum creatinine and potassium monitoring within first 2-4 days of treatment
Pallett SJC, Jones R, Pallett MA, et al., 2020, Characterising differential antibody response is integral to future SARS-CoV-2 serostudies, Journal of Infection, Vol: 81, Pages: E28-E30, ISSN: 0163-4453
Moore LSP, 2020, Near-patient SARS-CoV-2 molecular platforms: new-old tools for new-old problems, The Lancet Respiratory Medicine, Vol: 8, Pages: 1161-1163, ISSN: 2213-2600
Skolimowska K, Rayment M, Jones R, et al., 2020, Non-invasive saliva specimens for the diagnosis of COVID-19: caution in mild outpatient cohorts with low prevalence, CLINICAL MICROBIOLOGY AND INFECTION, Vol: 26, Pages: 1711-1713, ISSN: 1198-743X
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Denny S, Rawson T, Satta G, et al., 2020, Bacteraemia variation during the COVID-19 pandemic; a multi-centre UK secondary care ecological analysis., Publisher: Research Square
Objectives – We investigated for change in blood stream infections (BSI) with Enterobacterales, coagulase negative staphylococci (CoNS), Streptococcus pneumoniae, and Staphylococcus aureus during the first UK wave of SARS-CoV-2 across six London hospitals.Methods – A retrospective multicentre ecological analysis was undertaken evaluating all blood cultures taken from adults from 01 April 2017 to 30 April 2020 across six acute hospitals in London. Linear trend analysis and ARIMA models allowing for seasonality were used to look for significant variation.Results –119,584 blood cultures were included. At the height of the UK SARS-CoV-2 first wave in April 2020, Enterobacterales bacteraemias were at an historic low across two London trusts (63/3814, 1.65%), whilst CoNS were at an historic high (173/3814, 4.25%). This differed significantly for both Enterobacterales (p=0.013) and CoNS (p<0.01), when compared with prior periods, even allowing for seasonal variation. S. pneumoniae (p=0.631) and S. aureus (p=0.617) BSI did not vary significant throughout the study period.Conclusions – Significantly fewer than expected Enterobacteriales BSI occurred during the UK peak of the COVID-19 pandemic; identifying potential causes, including potential unintended consequences of national self-isolation public health messaging, is essential. High rates of CoNS BSI, presumably representing contamination associated with increased use of personal protective equipment, may result in inappropriate antimicrobial use and indicates a clear area for intervention during further waves.
Abdulaal A, Patel A, Charani E, et al., 2020, Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes, BMC Medical Informatics and Decision Making, Vol: 20, Pages: 1-11, ISSN: 1472-6947
Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.
Abdulaal A, Patel A, Charani E, et al., 2020, Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes
<jats:title>Abstract</jats:title> <jats:p>Background Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.MethodBetween March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.Results Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. ConclusionWe demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmenta
Ponsford MJ, Gkatzionis A, Walker VM, et al., 2020, Cardiometabolic Traits, Sepsis, and Severe COVID-19 A Mendelian Randomization Investigation, CIRCULATION, Vol: 142, Pages: 1791-1793, ISSN: 0009-7322
Gibani MM, Toumazou C, Sohbati M, et al., 2020, Assessing a novel, lab-free, point-of-care test for SARS-CoV-2 (CovidNudge): a diagnostic accuracy study., The Lancet Microbe, Vol: 1, Pages: e300-e307, ISSN: 2666-5247
Background: Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing. Methods: Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing. Findings: We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied
Prendecki M, Clarke C, Gleeson S, et al., 2020, Detection of SARS-CoV-2 antibodies in kidney transplant recipients., Journal of the American Society of Nephrology, Vol: 31, Pages: 1-8, ISSN: 1046-6673
Kidney transplant recipients and other patient groups receiving immunosuppression have a poor prognosis following presentation with symptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.1 The immune response to SARS-CoV-2 in an immunocompromised population has not been systematically reported. Recognition that humoral immune responses against common viral infections are blunted in such patients has led to their exclusion from validation studies of serologic assays for SARS-CoV-2.2,3 In this study, we analyze the seroprevalence of SARS-CoV-2 antibodies in a transplant population. In order to ensure the accuracy of the seroprevalence rate, we also evaluate the performance of different serologic assays within this patient cohort.
Abdulaal A, Patel A, Charani E, et al., 2020, Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes
<jats:title>Abstract</jats:title> <jats:p><jats:bold>Background: </jats:bold>Accurately predicting patient outcomes in SARS-CoV-2 could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2. <jats:bold>Method: </jats:bold>Between March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration. <jats:bold>Results: </jats:bold>Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. <jats:bold>Conclusion: </jats:bold>We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior kno
Patel A, Abdulaal A, Ariyanayagam D, et al., 2020, Investigating the association between ethnicity and health outcomes in SARS-CoV-2 in a London secondary care population, PLoS One, Vol: 15, Pages: 1-12, ISSN: 1932-6203
BackgroundBlack, Asian and minority ethnic (BAME) populations are emerging as a vulnerable group in the severe acute respiratory syndrome coronavirus disease (SARS-CoV-2) pandemic. We investigated the relationship between ethnicity and health outcomes in SARS-CoV-2.Methods and findingsWe conducted a retrospective, observational analysis of SARS-CoV-2 patients across two London teaching hospitals during March 1 –April 30, 2020. Routinely collected clinical data were extracted and analysed for 645 patients who met the study inclusion criteria. Within this hospitalised cohort, the BAME population were younger relative to the white population (61.70 years, 95% CI 59.70–63.73 versus 69.3 years, 95% CI 67.17–71.43, p<0.001). When adjusted for age, sex and comorbidity, ethnicity was not a predictor for ICU admission. The mean age at death was lower in the BAME population compared to the white population (71.44 years, 95% CI 69.90–72.90 versus, 77.40 years, 95% CI 76.1–78.70 respectively, p<0.001). When adjusted for age, sex and comorbidities, Asian patients had higher odds of death (OR 1.99: 95% CI 1.22–3.25, p<0.006).ConclusionsBAME patients were more likely to be admitted younger, and to die at a younger age with SARS-CoV-2. Within the BAME cohort, Asian patients were more likely to die but despite this, there was no difference in rates of admission to ICU. The reasons for these disparities are not fully understood and need to be addressed. Investigating ethnicity as a clinical risk factor remains a high public health priority. Studies that consider ethnicity as part of the wider socio-cultural determinant of health are urgently needed.
Abdulaal A, Patel A, Charani E, et al., 2020, Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes
<jats:title>Abstract</jats:title> <jats:p><jats:bold>Background </jats:bold>Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.<jats:bold>Method </jats:bold>Between March 1 - April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: 1) a Cox regression model and 2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.<jats:bold>Results </jats:bold>Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI): 73.8 - 91.1 and 90.0%, 95% CI: 81.2 - 95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI: 91.1 - 94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI: 85.7 - 88.2), p=0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively. <jats:bold>Conclusion </jats:bold>We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for u
Davies A, Teare L, Falder S, et al., 2020, Consensus demonstrates four indicators needed to standardize burn wound infection reporting across trials in a single-country study (ICon-B study), JOURNAL OF HOSPITAL INFECTION, Vol: 106, Pages: 217-225, ISSN: 0195-6701
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Hughes S, Troise O, Donaldson H, et al., 2020, Bacterial and fungal coinfection among hospitalised patients with COVID-19: A retrospective cohort study in a UK secondary care setting, Clinical Microbiology and Infection, Vol: 26, Pages: 1395-1399, ISSN: 1198-743X
ObjectivesWe investigate the incidence of bacterial and fungal co-infection of hospitalised patients with confirmed SARS-CoV-2 in this retrospective observational study across two London hospitals during the first UK wave of COVID-19.MethodsA retrospective case-series of hospitalised patients with confirmed SARS-CoV-2 by PCR was analysed across two acute NHS hospitals (February 20–April 20; each isolate reviewed independently in parallel). This was contrasted to a control group of influenza positive patients admitted during 2019/20 flu season. Patient demographics, microbiology, and clinical outcomes were analysed.Results836 patients with confirmed SARS-CoV-2 were included; 27/836(3.2%) had early confirmed bacterial isolates identified (0-5 days post-admission) rising to 51/836(6.1%) throughout admission. Blood cultures, respiratory samples, pneumococcal or legionella urinary antigens, and respiratory viral PCR panels were obtained from 643(77%), 112(13%), 249(30%), 246(29%) and 250(30%) COVID-19 patients, respectively. A positive blood culture was identified in 60(7.1%) patients, of which 39/60 were classified as contaminants. Bacteraemia secondary to respiratory infection was confirmed in two cases (1 community-acquired K. pneumoniae and 1 ventilator-associated E. cloacae). Line-related bacteraemia was identified in six patients (3 candida, 2 Enterococcus spp. and 1 Pseudomonas aeruginosa). All other community acquired bacteraemias(16) were attributed to non-respiratory infection. Zero concomitant pneumococcal, legionella or influenza infection was detected. A low yield of positive respiratory cultures was identified; S. aureus 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 co-infection were identified in the control group of confirmed influenza i
Mark PJ, Gkatzionis A, Walker V, et al., 2020, Cardiometabolic traits, sepsis and severe covid-19 with respiratory failure: a Mendelian randomization investigation, Circulation, Vol: 142, Pages: 1791-1793, ISSN: 0009-7322
Objectives: To investigate whether there is a causal effect of cardiometabolic traits on risk of sepsis and severe covid-19.Design: Mendelian randomisation analysis.Setting: UK Biobank and HUNT study population-based cohorts for risk of sepsis, and genome-wide association study summary data for risk of severe covid-19 with respiratory failure.Participants: 12,455 sepsis cases (519,885 controls) and 1,610 severe covid-19 with respiratory failure cases (2,205 controls).Exposure: Genetic variants that proxy body mass index (BMI), lipid traits, systolic blood pressure, lifetime smoking score, and type 2 diabetes liability - derived from studies considering between 188,577 to 898,130 participants. Main outcome measures: Risk of sepsis and severe covid-19 with respiratory failure.Results: Higher genetically proxied BMI and lifetime smoking score were associated with increased risk of sepsis in both UK Biobank (BMI: odds ratio 1.38 per standard deviation increase, 95% confidence interval [CI] 1.27 to 1.51; smoking: odds ratio 2.81 per standard deviation increase, 95% CI 2.09-3.79) and HUNT (BMI: 1.41, 95% CI 1.18 to 1.69; smoking: 1.93, 95% CI 1.02-3.64). Higher genetically proxied BMI and lifetime smoking score were also associated with increased risk of severe covid-19, although with wider confidence intervals (BMI: 1.75, 95% CI 1.20 to 2.57; smoking: 3.94, 95% CI 1.13 to 13.75). There was limited evidence to support associations of genetically proxied lipid traits, systolic blood pressure or type 2 diabetes liability with risk of sepsis or severe covid-19. Similar findings were generally obtained when using Mendelian randomization methods that are more robust to the inclusion of pleiotropic variants, although the precision of estimates was reduced.Conclusions: Our findings support a causal effect of elevated BMI and smoking on risk of sepsis and severe covid-19. Clinical and public health interventions targeting obesity and smoking are likely to reduce sepsis and covid
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