Shorter and Longer Courses of Antibiotics for Common Infections and the Association With Reductions of Infection-Related Complications Including Hospital Admissions

BACKGROUND: Antimicrobial resistance is a serious global health concern that emphasizes completing treatment course. Recently, the effectiveness of short versus longer antibiotic courses has been questioned. This study investigated the duration of prescribed antibiotics, their effectiveness, and associated risk of infection-related complications. METHODS: Clinical Practice Research Datalink identified 4 million acute infection episodes prescribed an antibiotic in primary care between January 2014—June 2014, England. Prescriptions were categorized by duration. Risk of infection-related hospitalizations within 30 days was modelled overall and by infection type. Risk was assessed immediately after or within 30 days follow-up to measure confounders given similar and varying exposure, respectively. An interaction term with follow-up time assessed whether hazard ratios (HRs) remained parallel with different antibiotic durations. RESULTS: The duration of antibiotic courses increased over the study period (5.2–19.1%); 6–7 days were most common (66.9%). Most infection-related hospitalizations occurred with prescriptions of 8–15 days (0.21%), accompanied by greater risk of infection-related complications compared to patients who received a short prescription (HR: 1.75 [95% CI: 1.54–2.00]). Comparing HRs in the first 5 days versus remaining follow-up showed longer antibiotic courses were no more effective than shorter courses (1.02 [95% CI: 0.90–1.16] and 0.92 [95% CI: 0.75–1.12]). No variation by infection-type was observed. CONCLUSIONS: Equal effectiveness was found between shorter and longer antibiotic courses and the reduction of infection-related hospitalizations. Stewardship programs should recommend shorter courses of antibiotics for acute infections. Further research is required for treating patients with a complex medical history. Summary Prescribing of longer courses increased over the study period. The majority of hospitalizations occurred for patients receiving longer courses. Risk of developing a complication (immediate vs remaining follow-up) found longer courses were no more effective than shorter courses

Comparing antibiotic prescribing between clinicians in UK primary care: an analysis in a cohort study of eight different measures of antibiotic prescribing

Background There is a need to reduce antimicrobial uses in humans. Previous studies have found variations in antibiotic (AB) prescribing between practices in primary care. This study assessed variability of AB prescribing between clinicians. Methods Clinical Practice Research Datalink, which collects electronic health records in primary care, was used to select anonymised clinicians providing 500+ consultations during 2012-2017. Eight measures of AB prescribing were assessed, such as overall and incidental AB prescribing, repeat AB courses and extent of risk-based prescribing. Poisson regression models with random effect for clinicians were fitted. Results 6111 clinicians from 466 general practices were included. Considerable variability between individual clinicians was found for most AB measures. For example, the rate of AB prescribing varied between 77.4 and 350.3 per 1000 consultations; percentage of repeat AB courses within 30 days ranged from 13.1% to 34.3%; predicted patient risk of hospital admission for infection-related complications in those prescribed AB ranged from 0.03% to 0.32% (5th and 95th percentiles). The adjusted relative rate between clinicians in rates of AB prescribing was 5.23. Weak correlation coefficients (<0.5) were found between most AB measures. There was considerable variability in case mix seen by clinicians. The largest potential impact to reduce AB prescribing could be around encouraging risk-based prescribing and addressing repeat issues of ABs. Reduction of repeat AB courses to prescribing habit of median clinician would save 21 813 AB prescriptions per 1000 clinicians per year. Conclusions The wide variation seen in all measures of AB prescribing and weak correlation between them suggests that a single AB measure, such as prescribing rate, is not sufficient to underpin the optimisation of AB prescribing

Altered protein O-GlcNAcylation in placentas from mothers with diabetes causes aberrant endocytosis in placental trophoblast cells

From Springer Nature via Jisc Publications RouterHistory: received 2021-05-28, accepted 2021-09-27, registration 2021-10-06, pub-electronic 2021-10-19, online 2021-10-19, collection 2021-12Publication status: PublishedAbstract: Women with pre-existing diabetes have an increased risk of poor pregnancy outcomes, including disordered fetal growth, caused by changes to placental function. Here we investigate the possibility that the hexosamine biosynthetic pathway, which utilises cellular nutrients to regulate protein function via post-translationally modification with O-linked N-acetylglucosamine (GlcNAc), mediates the placental response to the maternal metabolic milieu. Mass spectrometry analysis revealed that the placental O-GlcNAcome is altered in women with type 1 (n = 6) or type 2 (n = 6) diabetes T2D (≥ twofold change in abundance in 162 and 165 GlcNAcylated proteins respectively compared to BMI-matched controls n = 11). Ingenuity pathway analysis indicated changes to clathrin-mediated endocytosis (CME) and CME-associated proteins, clathrin, Transferrin (TF), TF receptor and multiple Rabs, were identified as O-GlcNAcylation targets. Stimulating protein O-GlcNAcylation using glucosamine (2.5 mM) increased the rate of TF endocytosis by human placental cells (p = 0.02) and explants (p = 0.04). Differential GlcNAcylation of CME proteins suggests altered transfer of cargo by placentas of women with pre-gestational diabetes, which may contribute to alterations in fetal growth. The human placental O-GlcNAcome provides a resource to aid further investigation of molecular mechanisms governing placental nutrient sensing

Improving Our Understanding and Practice of Antibiotic Prescribing: A Study on the Use of Social Norms Feedback Letters in Primary Care

In the UK, 81% of all antibiotics are prescribed in primary care. Previous research has shown that a letter from the Chief Medical Officer (CMO) giving social norms feedback to General Practitioners (GPs) whose practices are high prescribers of antibiotics can decrease antibiotic prescribing. The aim of this study was to understand the best way for engaging with GPs to deliver feedback on prescribing behaviour that could be replicated at scale; and explore GP information requirements that would be needed to support prescribing behaviour change. Two workshops were devised utilising a participatory approach. Discussion points were noted and agreed with each group of participants. Minutes of the workshops and observation notes were taken. Data were analysed thematically. Four key themes emerged through the data analysis: (1) Our day-to-day reality, (2) GPs are competitive, (3) Face-to-face support, and (4) Empowerment and engagement. Our findings suggest there is potential for using behavioural science in the form of social norms as part of a range of engagement strategies in reducing antibiotic prescribing within primary care. This should include tailored and localised data with peer-to-peer comparisons

Risk of emergency hospital admission related to adverse events after antibiotic treatment in adults with a common infection: impact of COVID-19 and derivation and validation of risk prediction models

Background: With the global challenge of antimicrobial resistance intensified during the COVID-19 pandemic, evaluating adverse events (AEs) post-antibiotic treatment for common infections is crucial. This study aims to examines the changes in incidence rates of AEs during the COVID-19 pandemic and predict AE risk following antibiotic prescriptions for common infections, considering their previous antibiotic exposure and other long-term clinical conditions. Methods: With the approval of NHS England, we used OpenSAFELY platform and analysed electronic health records from patients aged 18–110, prescribed antibiotics for urinary tract infection (UTI), lower respiratory tract infections (LRTI), upper respiratory tract infections (URTI), sinusitis, otitis externa, and otitis media between January 2019 and June 2023. We evaluated the temporal trends in the incidence rate of AEs for each infection, analysing monthly changes over time. The survival probability of emergency AE hospitalisation was estimated in each COVID-19 period (period 1: 1 January 2019 to 25 March 2020, period 2: 26 March 2020 to 8 March 2021, period 3: 9 March 2021 to 30 June 2023) using the Kaplan–Meier approach. Prognostic models, using Cox proportional hazards regression, were developed and validated to predict AE risk within 30 days post-prescription using the records in Period 1. Results: Out of 9.4 million patients who received antibiotics, 0.6% of UTI, 0.3% of URTI, and 0.5% of LRTI patients experienced AEs. UTI and LRTI patients demonstrated a higher risk of AEs, with a noted increase in AE incidence during the COVID-19 pandemic. Higher comorbidity and recent antibiotic use emerged as significant AE predictors. The developed models exhibited good calibration and discrimination, especially for UTIs and LRTIs, with a C-statistic above 0.70. Conclusions: The study reveals a variable incidence of AEs post-antibiotic treatment for common infections, with UTI and LRTI patients facing higher risks. AE risks varied between infections and COVID-19 periods. These findings underscore the necessity for cautious antibiotic prescribing and call for further exploration into the intricate dynamics between antibiotic use, AEs, and the pandemic

Exploring Prior Antibiotic Exposure Characteristics for COVID-19 Hospital Admission Patients: OpenSAFELY

Previous studies have demonstrated the association between antibiotic use and severe COVID-19 outcomes. This study aimed to explore detailed antibiotic exposure characteristics among COVID-19 patients. Using the OpenSAFELY platform, which integrates extensive health data and covers 40% of the population in England, the study analysed 3.16 million COVID-19 patients with at least two prior antibiotic prescriptions. These patients were compared to up to six matched controls without hospitalisation records. A machine learning model categorised patients into ten groups based on their antibiotic exposure history over the three years before their COVID-19 diagnosis. The study found that for COVID-19 patients, the total number of prior antibiotic prescriptions, diversity of antibiotic types, broad-spectrum antibiotic prescriptions, time between first and last antibiotics, and recent antibiotic use were associated with an increased risk of severe COVID-19 outcomes. Patients in the highest decile of antibiotic exposure had an adjusted odds ratio of 4.8 for severe outcomes compared to those in the lowest decile. These findings suggest a potential link between extensive antibiotic use and the risk of severe COVID-19. This highlights the need for more judicious antibiotic prescribing in primary care, primarily for patients with higher risks of infection-related complications, which may better offset the potential adverse effects of repeated antibiotic use

Trans-omics Impact of Thymoproteasome in Cortical Thymic Epithelial Cells

The thymic function to produce self-protective and self-tolerant T cells is chiefly mediated by cortical thymic epithelial cells (cTECs) and medullary TECs (mTECs). Recent studies including single-cell transcriptomic analyses have highlighted a rich diversity in functional mTEC subpopulations. Because of their limited cellularity, however, the biochemical characterization of TECs, including the proteomic profiling of cTECs and mTECs, has remained unestablished. Utilizing genetically modified mice that carry enlarged but functional thymuses, here we show a combination of proteomic and transcriptomic profiles for cTECs and mTECs, which identified signature molecules that characterize a developmental and functional contrast between cTECs and mTECs. Our results reveal a highly specific impact of the thymoproteasome on proteasome subunit composition in cTECs and provide an integrated trans-omics platform for further exploration of thymus biology

Impact of COVID-19 on broad-spectrum antibiotic prescribing for common infections in primary care in England: a time-series analyses using OpenSAFELY and effects of predictors including deprivation.

BACKGROUND: The COVID-19 pandemic impacted the healthcare systems, adding extra pressure to reduce antimicrobial resistance. Therefore, we aimed to evaluate changes in antibiotic prescription patterns after COVID-19 started. METHODS: With the approval of NHS England, we used the OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system in primary care and selected patients prescribed antibiotics from 2019 to 2021. To evaluate the impact of COVID-19 on broad-spectrum antibiotic prescribing, we evaluated prescribing rates and its predictors and used interrupted time series analysis by fitting binomial logistic regression models. FINDINGS: Over 32 million antibiotic prescriptions were extracted over the study period; 8.7% were broad-spectrum. The study showed increases in broad-spectrum antibiotic prescribing (odds ratio [OR] 1.37; 95% confidence interval [CI] 1.36-1.38) as an immediate impact of the pandemic, followed by a gradual recovery with a 1.1-1.2% decrease in odds of broad-spectrum prescription per month. The same pattern was found within subgroups defined by age, sex, region, ethnicity, and socioeconomic deprivation quintiles. More deprived patients were more likely to receive broad-spectrum antibiotics, which differences remained stable over time. The most significant increase in broad-spectrum prescribing was observed for lower respiratory tract infection (OR 2.33; 95% CI 2.1-2.50) and otitis media (OR 1.96; 95% CI 1.80-2.13). INTERPRETATION: An immediate reduction in antibiotic prescribing and an increase in the proportion of broad-spectrum antibiotic prescribing in primary care was observed. The trends recovered to pre-pandemic levels, but the consequence of the COVID-19 pandemic on AMR needs further investigation. FUNDING: This work was supported by Health Data Research UK and by National Institute for Health Research

The impact of COVID-19 on antibiotic prescribing in primary care in England: Evaluation and risk prediction of appropriateness of type and repeat prescribing.

BACKGROUND: This study aimed to predict risks of potentially inappropriate antibiotic type and repeat prescribing and assess changes during COVID-19. METHODS: With the approval of NHS England, we used OpenSAFELY platform to access the TPP SystmOne electronic health record (EHR) system and selected patients prescribed antibiotics from 2019 to 2021. Multinomial logistic regression models predicted patient's probability of receiving inappropriate antibiotic type or repeat antibiotic course for each common infection. RESULTS: The population included 9.1 million patients with 29.2 million antibiotic prescriptions. 29.1% of prescriptions were identified as repeat prescribing. Those with same day incident infection coded in the EHR had considerably lower rates of repeat prescribing (18.0%) and 8.6% had potentially inappropriate type. No major changes in the rates of repeat antibiotic prescribing during COVID-19 were found. In the 10 risk prediction models, good levels of calibration and moderate levels of discrimination were found. CONCLUSIONS: Our study found no evidence of changes in level of inappropriate or repeat antibiotic prescribing after the start of COVID-19. Repeat antibiotic prescribing was frequent and varied according to regional and patient characteristics. There is a need for treatment guidelines to be developed around antibiotic failure and clinicians provided with individualised patient information