Population Pharmacokinetics of Intraventricular Vancomycin in Neonatal Ventriculitis, A Preterm Pilot Study

AIM: Intraventricular vancomycin is an effective treatment for neonatal ventriculitis, as the cerebrospinal fluid (CSF) vancomycin levels reach adequate concentrations to achieve microbiological cure. There is no robust data on intraventricular vancomycin pharmacokinetics in the preterm population. This pilot population pharmacokinetic modelling study examines the pharmacokinetic behaviour of intraventricular vancomycin in the preterm population of < 28 weeks gestation, to inform the feasibility of future prospective studies. METHODS: The study comprised 8 preterm infants with neonatal ventriculitis (median gestation age 25.3 weeks; range 23.9 - 27.7). Population pharmacokinetics (non-linear mixed effects modelling) were described with one- and two-compartment models to fit plasma concentrations of vancomycin. A CSF compartment was added to the plasma modelling and mass transfer examined. Three covariates (serum creatinine, ventricular index (VI) and CSF protein) were tested on the final model. Area under the curve (AUC) and average CSF concentration (C average) predictions were generated from the final model and compared with time to microbiological cure. RESULTS: A one-compartment model provided the best fit to the data. There was no appreciable transfer between plasma and CSF. None of the covariates provided a significant reduction in the objective function value (OFV). Generally, time to sterilisation with higher CSF AUC (0-24) and C average tends to be shorter, however this should be interpreted with caution as data is erratic. CONCLUSION: This pilot population pharmacokinetic analysis provides important information to warrant changes in the management of intraventricular vancomycin treatment in the preterm population, such as the current use of VI as a dosing parameter. Further study with a larger data pool is necessary to investigate the influence of VI on CSF vancomycin and ascertain dosing strategies

Microcontroller based Bidirectional Energy Metering for Domestic User

The present energy scenario is greatly alarming the energy consumers to conserve as fossil fuels are going to deplete shortly. In this regard the renewable energy systems are really gaining momentum. Solar and wind roof top fittings are becoming popular choice for the domestic user based on their financial affordability. Solar panels generate energy to power a building’s electrical systems. In most cases, builders acknowledge that the solar panels installed on the roof will not always be sufficient for the building’s electrical needs, so the building is also connected to the main utility grid. However, sometimes during clear days the solar panels generate surplus power beyond the needs of the building. At these times, the surplus power is exported into the main utility grid. Most utility companies offer credits to buildings that export power in this manner. “Bi-directional Meter” means a consumer meter for measuring, indicating and recording quanta of electricity flowing in opposite directions (export to the licensee’s distribution system and import by the consumer from distribution system) in Kwh including any other quantity as per the requirement. Net metering / bidirectional metering record both import and export energy values giving prime focus on utilizing self produced electricity by renewable energy sources and excess or surplus to be  sold to utilities or grid. It results into reduction of electricity bills. Solar photovoltaic system is used significantly in net metering. The design of microcontroller based bidirectional energy meter is low cost, affordable to consumer for domestic application and efficient. The current work focusses on the development of one of the type of bidirectional metering which accounts the net usage, export and the monetory exchange. The intelligent controller makes use of the measuring setup and controls all the major activities of the proposed work

Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections

Background A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible. Methodology Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen. Results A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups. Conclusion Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers

Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: Qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

\ua9 Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Objective To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic. Design Semistructured qualitative interview study. Setting National Health Service (NHS) trusts/health boards in England and Wales. Participants Clinicians from NHS trusts/health boards in England and Wales. Method Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions. Results During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed. Conclusion This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic. Trial registration number ISRCTN66682918

Consensus-based antimicrobial resistance and stewardship competencies for UK undergraduate medical students.

BACKGROUND: In the UK there is limited coverage of antimicrobial stewardship across postgraduate curricula and evidence that final year medical students have insufficient and inconsistent antimicrobial stewardship teaching. A national undergraduate curriculum for antimicrobial resistance and stewardship is required to standardize an adequate level of understanding for all future doctors. OBJECTIVES: To provide a UK national consensus on competencies for antimicrobial resistance and stewardship for undergraduate medical education. METHODS: Using the modified Delphi method over two online survey rounds, an expert panel comprising leads for infection teaching from 25 UK medical schools reviewed competency descriptors for antimicrobial resistance and stewardship education. RESULTS: There was a response rate of 100% with all 28 experts who agreed to take part completing both survey rounds. Following the first-round survey, of the initial 55 descriptors, 43 reached consensus (78%). The second-round survey included the 12 descriptors from the first round in which agreement had not been reached, four amended descriptors and 12 new descriptors following qualitative feedback from the panel members. Following the second-round survey, a total of 58 consensus-based competency descriptors within six overarching domains were identified. CONCLUSIONS: The consensus-based competency descriptors defined here can be used to inform standards, design curricula, develop assessment tools and direct UK undergraduate medical education

A retrospective propensity-score-matched cohort study of the impact of procalcitonin testing on antibiotic use in hospitalized patients during the first wave of COVID-19

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.BACKGROUND: Procalcitonin (PCT) is a blood marker used to help diagnose bacterial infections and guide antibiotic treatment. PCT testing was widely used/adopted during the COVID-19 pandemic in the UK. OBJECTIVES: Primary: to measure the difference in length of early (during first 7 days) antibiotic prescribing between patients with COVID-19 who did/did not have baseline PCT testing during the first wave of the pandemic. Secondary: to measure differences in length of hospital/ICU stay, mortality, total days of antibiotic prescribing and resistant bacterial infections between these groups. METHODS: Multi-centre, retrospective, observational, cohort study using patient-level clinical data from acute hospital Trusts/Health Boards in England/Wales. Inclusion: patients ≥16 years, admitted to participating Trusts/Health Boards and with a confirmed positive COVID-19 test between 1 February 2020 and 30 June 2020. RESULTS: Data from 5960 patients were analysed: 1548 (26.0%) had a baseline PCT test and 4412 (74.0%) did not. Using propensity-score matching, baseline PCT testing was associated with an average reduction in early antibiotic prescribing of 0.43 days [95% confidence interval (CI): 0.22-0.64 days, P &lt; 0.001) and of 0.72 days (95% CI: 0.06-1.38 days, P = 0.03] in total antibiotic prescribing. Baseline PCT testing was not associated with increased mortality or hospital/ICU length of stay or with the rate of antimicrobial-resistant secondary bacterial infections. CONCLUSIONS: Baseline PCT testing appears to have been an effective antimicrobial stewardship tool early in the pandemic: it reduced antibiotic prescribing without evidence of harm. Our study highlights the need for embedded, rapid evaluations of infection diagnostics in the National Health Service so that even in challenging circumstances, introduction into clinical practice is supported by evidence for clinical utility. STUDY REGISTRATION NUMBER: ISRCTN66682918

The cost-effectiveness of procalcitonin for guiding antibiotic prescribing in individuals hospitalized with COVID-19: part of the PEACH study

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.Background: Many hospitals introduced procalcitonin (PCT) testing to help diagnose bacterial coinfection in individuals with COVID-19, and guide antibiotic decision-making during the COVID-19 pandemic in the UK. Objectives: Evaluating cost-effectiveness of using PCT to guide antibiotic decisions in individuals hospitalized with COVID-19, as part of a wider research programme. Methods: Retrospective individual-level data on patients hospitalized with COVID-19 were collected from 11 NHS acute hospital Trusts and Health Boards from England and Wales, which varied in their use of baseline PCT testing during the first COVID-19 pandemic wave. A matched analysis (part of a wider analysis reported elsewhere) created groups of patients whose PCT was/was not tested at baseline. A model was created with combined decision tree/Markov phases, parameterized with quality-of-life/unit cost estimates from the literature, and used to estimate costs and quality-adjusted life years (QALYs). Cost-effectiveness was judged at a \ua320000/QALY threshold. Uncertainty was characterized using bootstrapping. Results: People who had baseline PCT testing had shorter general ward/ICU stays and spent less time on antibiotics, though with overlap between the groups’ 95% CIs. Those with baseline PCT testing accrued more QALYs (8.76 versus 8.62) and lower costs (\ua39830 versus \ua310 700). The point estimate was baseline PCT testing being dominant over no baseline testing, though with uncertainty: the probability of cost-effectiveness was 0.579 with a 1 year horizon and 0.872 with a lifetime horizon. Conclusions: Using PCT to guide antibiotic therapy in individuals hospitalized with COVID-19 is more likely to be cost-effective than not, albeit with uncertainty

Procalcitonin to guide antibiotic use during the first wave of COVID-19 in English and Welsh hospitals: integration and triangulation of findings from quantitative and qualitative sources

\ua9 Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group.Aim To integrate the quantitative and qualitative data collected as part of the PEACH (Procalcitonin: Evaluation of Antibiotic use in COVID-19 Hospitalised patients) study, which evaluated whether procalcitonin (PCT) testing should be used to guide antibiotic prescribing and safely reduce antibiotic use among patients admitted to acute UK National Health Service (NHS) hospitals. Design Triangulation to integrate quantitative and qualitative data. Setting and participants Four data sources in 148 NHS hospitals in England and Wales including data from 6089 patients. Method A triangulation protocol was used to integrate three quantitative data sources (survey, organisation-level data and patient-level data: data sources 1, 2 and 3) and one qualitative data source (clinician interviews: data source 4) collected as part of the PEACH study. Analysis of data sources initially took place independently, and then, key findings for each data source were added to a matrix. A series of interactive discussion meetings took place with quantitative, qualitative and clinical researchers, together with patient and public involvement (PPI) representatives, to group the key findings and produce seven statements relating to the study objectives. Each statement and the key findings related to that statement were considered alongside an assessment of whether there was agreement, partial agreement, dissonance or silence across all four data sources (convergence coding). The matrix was then interpreted to produce a narrative for each statement. Objective To explore whether PCT testing safely reduced antibiotic use during the first wave of the COVID-19 pandemic. Results Seven statements were produced relating to the PEACH study objective. There was agreement across all four data sources for our first key statement, € During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing\u27. The second statement was related to this key statement, € During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing safely reduced antibiotic prescribing\u27. Partial agreement was found between data sources 3 (quantitative patient-level data) and 4 (qualitative clinician interviews). There were no data regarding safety from data sources 1 or 2 (quantitative survey and organisational-level data) to contribute to this statement. For statements three and four, € PCT was not used as a central factor influencing antibiotic prescribing\u27, and € PCT testing reduced antibiotic prescribing in the emergency department (ED)/acute medical unit (AMU),\u27 there was agreement between data source 2 (organisational-level data) and data source 4 (interviews with clinicians). The remaining two data sources (survey and patient-level data) contributed no data on this statement. For statement five, € PCT testing reduced antibiotic prescribing in the intensive care unit (ICU)\u27, there was disagreement between data sources 2 and 3 (organisational-level data and patient-level data) and data source 4 (clinician interviews). Data source 1 (survey) did not provide data on this statement. We therefore assigned dissonance to this statement. For statement six, € There were many barriers to implementing PCT testing during the first wave of COVID-19\u27, there was partial agreement between data source 1 (survey) and data source 4 (clinician interviews) and no data provided by the two remaining data sources (organisational-level data and patient-level data). For statement seven, € Local PCT guidelines/protocols were perceived to be valuable\u27, only data source 4 (clinician interviews) provided data. The clinicians expressed that guidelines were valuable, but as there was no data from the other three data sources, we assigned silence to this statement. Conclusion There was agreement between all four data sources on our key finding € during the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing\u27. Data, methodological and investigator triangulation, and a transparent triangulation protocol give validity to this finding

Procalcitonin to guide antibiotic use during the first wave of COVID-19 in English and Welsh hospitals: integration and triangulation of findings from quantitative and qualitative sources

Aim To integrate the quantitative and qualitative data collected as part of the PEACH (Procalcitonin: Evaluation of Antibiotic use in COVID-19 Hospitalised patients) study, which evaluated whether procalcitonin (PCT) testing should be used to guide antibiotic prescribing and safely reduce antibiotic use among patients admitted to acute UK National Health Service (NHS) hospitals. Design Triangulation to integrate quantitative and qualitative data. Setting and participants Four data sources in 148 NHS hospitals in England and Wales including data from 6089 patients. Method A triangulation protocol was used to integrate three quantitative data sources (survey, organisation-level data and patient-level data: data sources 1, 2 and 3) and one qualitative data source (clinician interviews: data source 4) collected as part of the PEACH study. Analysis of data sources initially took place independently, and then, key findings for each data source were added to a matrix. A series of interactive discussion meetings took place with quantitative, qualitative and clinical researchers, together with patient and public involvement (PPI) representatives, to group the key findings and produce seven statements relating to the study objectives. Each statement and the key findings related to that statement were considered alongside an assessment of whether there was agreement, partial agreement, dissonance or silence across all four data sources (convergence coding). The matrix was then interpreted to produce a narrative for each statement. Objective To explore whether PCT testing safely reduced antibiotic use during the first wave of the COVID-19 pandemic. Results Seven statements were produced relating to the PEACH study objective. There was agreement across all four data sources for our first key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. The second statement was related to this key statement, ‘During the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing safely reduced antibiotic prescribing’. Partial agreement was found between data sources 3 (quantitative patient-level data) and 4 (qualitative clinician interviews). There were no data regarding safety from data sources 1 or 2 (quantitative survey and organisational-level data) to contribute to this statement. For statements three and four, ‘PCT was not used as a central factor influencing antibiotic prescribing’, and ‘PCT testing reduced antibiotic prescribing in the emergency department (ED)/acute medical unit (AMU),’ there was agreement between data source 2 (organisational-level data) and data source 4 (interviews with clinicians). The remaining two data sources (survey and patient-level data) contributed no data on this statement. For statement five, ‘PCT testing reduced antibiotic prescribing in the intensive care unit (ICU)’, there was disagreement between data sources 2 and 3 (organisational-level data and patient-level data) and data source 4 (clinician interviews). Data source 1 (survey) did not provide data on this statement. We therefore assigned dissonance to this statement. For statement six, ‘There were many barriers to implementing PCT testing during the first wave of COVID-19’, there was partial agreement between data source 1 (survey) and data source 4 (clinician interviews) and no data provided by the two remaining data sources (organisational-level data and patient-level data). For statement seven, ‘Local PCT guidelines/protocols were perceived to be valuable’, only data source 4 (clinician interviews) provided data. The clinicians expressed that guidelines were valuable, but as there was no data from the other three data sources, we assigned silence to this statement. Conclusion There was agreement between all four data sources on our key finding ‘during the first wave of the pandemic (01/02/2020-30/06/2020), PCT testing reduced antibiotic prescribing’. Data, methodological and investigator triangulation, and a transparent triangulation protocol give validity to this finding