Development and validation of a multivariable prediction model for infection-related complications in patients with common infections in UK primary care and the extent of risk-based prescribing of antibiotics.

BACKGROUND: Antimicrobial resistance is driven by the overuse of antibiotics. This study aimed to develop and validate clinical prediction models for the risk of infection-related hospital admission with upper respiratory infection (URTI), lower respiratory infection (LRTI) and urinary tract infection (UTI). These models were used to investigate whether there is an association between the risk of an infection-related complication and the probability of receiving an antibiotic prescription. METHODS: The study used electronic health record data from general practices contributing to the Clinical Practice Research Datalink (CPRD GOLD) and Welsh Secure Anonymised Information Linkage (SAIL), both linked to hospital records. Patients who visited their general practitioner with an incidental URTI, LRTI or UTI were included and followed for 30 days for hospitalisation due to infection-related complications. Predictors included age, gender, clinical and medication risk factors, ethnicity and socioeconomic status. Cox proportional hazards regression models were used with predicted risks independently validated in SAIL. RESULTS: The derivation and validation cohorts included 8.1 and 2.7 million patients in CPRD and SAIL, respectively. A total of 7125 (0.09%) hospital admissions occurred in CPRD and 7685 (0.28%) in SAIL. Important predictors included age and measures of comorbidity. Initial attempts at validating in SAIL (i.e. transporting the models with no adjustment) indicated the need to recalibrate the models for age and underlying incidence of infections; internal bootstrap validation of these updated models yielded C-statistics of 0.63 (LRTI), 0.69 (URTI) and 0.73 (UTI) indicating good calibration. For all three infection types, the rate of antibiotic prescribing was not associated with patients' risk of infection-related hospital admissions. CONCLUSION: The risk for infection-related hospital admissions varied substantially between patients, but prescribing of antibiotics in primary care was not associated with risk of hospitalisation due to infection-related complications. Our findings highlight the potential role of clinical prediction models to help inform decisions of prescribing of antibiotics in primary care.

Protocol for evaluating the fitness for purpose of an artificial intelligence product for radiology reporting in the BreastScreen New South Wales breast cancer screening programme.

INTRODUCTION: Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using 'enriched' datasets and/or not considering the interaction between the algorithm and human readers. This study will address these limitations by comparing the accuracy of a workflow using AI alongside radiologists on a large consecutive cohort of examinations from a breast cancer screening programme. The study will combine the strengths of a large retrospective design with the benefit of prospective data collection. It will test this technology without risk to screening programme participants nor the need to wait for follow-up data. With a sample of 2 years of consecutive screening examinations, it is likely the largest test of this technology to date. The study will help determine whether this technology can safely be introduced into the BreastScreen New South Wales (NSW) population-based screening programme to address radiology workforce risks without compromising cancer detection rates or increasing false-positive recalls. METHODS AND ANALYSIS: A retrospective, consecutive cohort of digital mammography screens from 658 207 examinations from BreastScreen NSW will be reinterpreted by the Lunit Insight MMG AI product. The cohort includes 4383 screen-detected and 1171 interval cancers. The results will be compared with radiologist single reading and the AI results will also be used to replace the second reader in a double-reading model. New adjudication reading will be performed where the AI disagrees with the first reader. Recall rates and cancer detection rates of combined AI-radiologist reading will be compared with the rates obtained at the time of screening. ETHICS AND DISSEMINATION: This study has ethical approval from the NSW Health Population Health Services Research Ethics Committee (2022/ETH02397). Findings will be published in peer-reviewed journals and presented at conferences. The findings of this evaluation will be provided to programme managers, governance bodies and other stakeholders in Australian breast cancer screening programmes.

High-value zeolitic material from bagasse fly ash: utilization for dye elimination.

Bagasse fly ash (BFA), a sugar industry waste, was used to prepare zeolitic material (ZFA) by means of alkaline hydrothermal treatment. ZFA showed improved morphology as a result of this treatment. The adsorption of the reactive dyes turquoise blue (TB) and brilliant magenta (BM), on both BFA and ZFA, was investigated in a batch contact system. A series of batch experiments revealed that optimal dye removal occurs at a 200 mg/L to 300 mg/L solute concentration, 60 minutes of agitation time, 5 g/L to 10 g/L adsorbent dose, a pH level of 2 to 4, and a temperature of 298 K. ZFA showed enhanced adsorption capacity as compared to BFA. According to the Langmuir equation, the maximum adsorption capacity was 12.66 mg/g and 45.45 mg/g for turquoise blue and brilliant magenta dyes, respectively, on BFA; and 21.74 mg/g and 100.00 mg/g for turquoise blue and brilliant magenta dyes, respectively, on ZFA. Kinetic studies showed that the correlation coefficients best fit with the pseudo-second-order kinetic model, confirming that the adsorption rate was controlled by a hemisorptions process.

Infection-related complications after common infection in association with new antibiotic prescribing in primary care: retrospective cohort study using linked electronic health records.

OBJECTIVE: Determine the association of incident antibiotic prescribing levels for common infections with infection-related complications and hospitalisations by comparing high with low prescribing general practitioner practices. DESIGN RETROSPECTIVE COHORT STUDY: Retrospective cohort study. DATA SOURCE: UK primary care records from the Clinical Practice Research Datalink (CPRD GOLD) and SAIL Databank (SAIL) linked with Hospital Episode Statistics (HES) data, including 546 CPRD, 346 CPRD-HES and 338 SAIL-HES practices. EXPOSURES: Initial general practice visit for one of six common infections and the proportion of antibiotic prescribing in each practice. MAIN OUTCOME MEASURES: Incidence of infection-related complications (as recorded in general practice) or infection-related hospital admission within 30 days after consultation for a common infection. RESULTS: A practice with 10.4% higher antibiotic prescribing (the IQR) was associated with a 5.7% lower rate of infection-related hospital admissions (adjusted analysis, 95% CI 3.3% to 8.0%). The association varied by infection with larger associations in hospital admissions with lower respiratory tract infection (16.1%; 95% CI 12.4% to 19.7%) and urinary tract infection (14.7%; 95% CI 7.6% to 21.1%) and smaller association in hospital admissions for upper respiratory tract infection (6.5%; 95% CI 3.5% to 9.5%) The association of antibiotic prescribing levels and hospital admission was largest in patients aged 18-39 years (8.6%; 95% CI 4.0% to 13.0%) and smallest in the elderly aged 75+ years (0.3%; 95% CI -3.4% to 3.9%). CONCLUSIONS: There is an association between lower levels of practice level antibiotic prescribing and higher infection-related hospital admissions. Indiscriminately reducing antibiotic prescribing may lead to harm. Greater focus is needed to optimise antibiotic use by reducing inappropriate antibiotic prescribing and better targeting antibiotics to patients at high risk of infection-related complications.

Developing the infrastructure to support the optimisation of antibiotic prescribing using the learning healthcare system to improve healthcare services in the provision of primary care in England.

INTRODUCTION: The learning healthcare system (LHS) underpinned by data analysis and feedback to clinical care providers is thought to improve quality of care. The work aimed to implement an LHS for antibiotic prescribing in primary care in England. METHOD: Deidentified patient-level data from general practices were processed and analysed at regular intervals (fortnightly increments). A dashboard application was developed and implemented displaying analytical graphics to give periodic feedback to clinicians, tailored to each clinical site. Benchmarking parameters were established by the analysis of two large national primary care datasets allowing peer-to-peer comparisons. To date, the dashboard is available to 70 English practices. CONCLUSIONS: Successful implementation and uptake of the secure technical LHS infrastructure for the analysis and feedback to clinicians of their antibiotic prescribing demonstrate a great appetite for this type of frequent prescribing review in primary care, combining advanced data analytics with tailored feedback.

Antibiotic prescribing patterns in general medical practices in England: Does area matter?

Antimicrobial resistance is an important public health concern. As most antibiotics are prescribed in primary care, understanding prescribing patterns in General Medical (GP) practices is vital. The aim of this study was a spatial pattern analysis of antibiotic prescribing rates in GP practices in England and to examine the association of potential clusters with area level socio-economic deprivation. The pattern analysis identified a number of hot and cold spots of antibiotic prescribing, with hot spots predominantly in the North of England. Spatial regression showed that patient catchments of hot spot practices were significantly more deprived than patient catchments of cold spot practices, especially in the domains of income, employment, education and health. This study suggests the presence of area level drivers resulting in clusters of high and low prescribing. Consequently, area level strategies may be needed for antimicrobial stewardship rather than national level strategies.

Seizure modeling of Pb(II) and Cd(II) from aqueous solution by chemically modified sugarcane bagasse fly ash: isotherms, kinetics, and column study.

Heavy metal pollution is a common environmental problem all over the world. The purpose of the research is to examine the applicability of bagasse fly ash (BFA)-an agricultural waste of sugar industry used for the synthesis of zeolitic material. The zeolitic material are used for the uptake of Pb(II) and Cd(II) heavy metal. Bagasse fly ash is used as a native material for the synthesis of zeolitic materials by conventional hydrothermal treatment without (conventional zeolitic bagasse fly ash (CZBFA)) and with electrolyte (conventional zeolitic bagasse fly ash in electrolyte media (ECZBFA)) media. Heavy metal ions Pb(II) and Cd(II) were successfully seized from aqueous media using these synthesized zeolitic materials. In this study, the zeolitic materials were well characterized by different instrumental methods such as Brunauer-Emmett-Teller, XRF, Fourier transform infrared spectroscopy, powder X-ray diffraction, and scanning electron microscopic microphotographs. The presence of analcime, phillipsite, and zeolite P in adsorbents confirms successful conversion of native BFA into zeolitic materials. Seizure modeling of Pb(II) and Cd(II) was achieved by batch sorption experiments, isotherms, and kinetic studies. These data were used to compare and evaluate the zeolitic materials as potential sorbents for the uptake of heavy metal ions from an aqueous media. The Langmuir isotherm correlation coefficient parameters best fit the equilibrium data which indicate the physical sorption. Pseudo-second-order and intra-particle diffusion model matches best which indicates that the rate of sorption was controlled by film diffusion. The column studies were performed for the practical function of sorbents, and breakthrough curves were obtained, which revealed higher sorption capacity as compared to batch method. Synthesized zeolitic material (CZBFA and ECZBFA), a low-cost sorbent, was proven as potential sorbent for the uptake of Pb(II) and Cd(II) heavy metal ions.