Acceptability of data linkage to identify women at risk of postnatal complication for the development of digital risk prediction tools and interventions to better optimise postnatal care, a qualitative descriptive study design.

BACKGROUND: Pregnancy acts as a cardiovascular stress test. Although many complications resolve following birth, women with hypertensive disorder of pregnancy have an increased risk of developing cardiovascular disease (CVD) long-term. Monitoring postnatal health can reduce this risk but requires better methods to identity high-risk women for timely interventions. METHODS: Employing a qualitative descriptive study design, focus groups and/or interviews were conducted, separately engaging public contributors and clinical professionals. Diverse participants were recruited through social media convenience sampling. Semi-structured, facilitator-led discussions explored perspectives of current postnatal assessment and attitudes towards linking patient electronic healthcare data to develop digital tools for identifying postpartum women at risk of CVD. Participant perspectives were gathered using post-it notes or a facilitator scribe and analysed thematically. RESULTS: From 27 public and seven clinical contributors, five themes regarding postnatal check expectations versus reality were developed, including 'limited resources', 'low maternal health priority', 'lack of knowledge', 'ineffective systems' and 'new mum syndrome'. Despite some concerns, all supported data linkage to identify women postnatally, targeting intervention to those at greater risk of CVD. Participants outlined potential benefits of digitalisation and risk prediction, highlighting design and communication needs for diverse communities. CONCLUSIONS: Current health system constraints in England contribute to suboptimal postnatal care. Integrating data linkage and improving education on data and digital tools for maternal healthcare shows promise for enhanced monitoring and improved future health. Recognised for streamlining processes and risk prediction, digital tools may enable more person-centred care plans, addressing the gaps in current postnatal care practice.

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.

Sepsis and case fatality rates and associations with deprivation, ethnicity, and clinical characteristics: population-based case-control study with linked primary care and hospital data in England.

PURPOSE: Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infection. The purpose of the study was to measure the associations of specific exposures (deprivation, ethnicity, and clinical characteristics) with incident sepsis and case fatality. METHODS: Two research databases in England were used including anonymized patient-level records from primary care linked to hospital admission, death certificate, and small-area deprivation. Sepsis cases aged 65-100 years were matched to up to six controls. Predictors for sepsis (including 60 clinical conditions) were evaluated using logistic and random forest models; case fatality rates were analyzed using logistic models. RESULTS: 108,317 community-acquired sepsis cases were analyzed. Severe frailty was strongly associated with the risk of developing sepsis (crude odds ratio [OR] 14.93; 95% confidence interval [CI] 14.37-15.52). The quintile with most deprived patients showed an increased sepsis risk (crude OR 1.48; 95% CI 1.45-1.51) compared to least deprived quintile. Strong predictors for sepsis included antibiotic exposure in prior 2 months, being house bound, having cancer, learning disability, and diabetes mellitus. Severely frail patients had a case fatality rate of 42.0% compared to 24.0% in non-frail patients (adjusted OR 1.53; 95% CI 1.41-1.65). Sepsis cases with recent prior antibiotic exposure died less frequently compared to non-users (adjusted OR 0.7; 95% CI 0.72-0.76). Case fatality strongly decreased over calendar time. CONCLUSION: Given the variety of predictors and their level of associations for developing sepsis, there is a need for prediction models for risk of developing sepsis that can help to target preventative antibiotic therapy.

Rapid systematic review on risks and outcomes of sepsis: the influence of risk factors associated with health inequalities.

BACKGROUND AND AIMS: Sepsis is a serious and life-threatening condition caused by a dysregulated immune response to an infection. Recent guidance issued in the UK gave recommendations around recognition and antibiotic treatment of sepsis, but did not consider factors relating to health inequalities. The aim of this study was to summarise the literature investigating associations between health inequalities and sepsis. METHODS: Searches were conducted in Embase for peer-reviewed articles published since 2010 that included sepsis in combination with one of the following five areas: socioeconomic status, race/ethnicity, community factors, medical needs and pregnancy/maternity. RESULTS: Five searches identified 1,402 studies, with 50 unique studies included in the review after screening (13 sociodemographic, 14 race/ethnicity, 3 community, 3 care/medical needs and 20 pregnancy/maternity; 3 papers examined multiple health inequalities). Most of the studies were conducted in the USA (31/50), with only four studies using UK data (all pregnancy related). Socioeconomic factors associated with increased sepsis incidence included lower socioeconomic status, unemployment and lower education level, although findings were not consistent across studies. For ethnicity, mixed results were reported. Living in a medically underserved area or being resident in a nursing home increased risk of sepsis. Mortality rates after sepsis were found to be higher in people living in rural areas or in those discharged to skilled nursing facilities while associations with ethnicity were mixed. Complications during delivery, caesarean-section delivery, increased deprivation and black and other ethnic minority race were associated with post-partum sepsis. CONCLUSION: There are clear correlations between sepsis morbidity and mortality and the presence of factors associated with health inequalities. To inform local guidance and drive public health measures, there is a need for studies conducted across more diverse setting and countries.

Outcomes following cardiac resynchronisation therapy in older people.

INTRODUCTION: Older patients may be less likely to receive cardiac resynchronisation therapy (CRT) for the management of heart failure. We aimed to describe the differences in clinical response, complications, and subsequent outcomes following CRT implantation compared to younger patients. METHODS: We conducted a retrospective cohort study of unselected, consecutive patients implanted with CRT devices between March 2008 and July 2017. We recorded complications, symptomatic and echocardiographic response, hospitalisation for heart failure, and all-cause mortality comparing patients aged <70, 70-79 and ≥ 80 years. RESULTS: Five hundred and seventy-four patients (median age 76 years [interquartile range 68-81], 73.3% male) received CRT. At baseline, patients aged ≥80 years had worse symptoms, were more likely to have co-morbidities, and less likely to be receiving comprehensive medical therapy, although left ventricular function was similar. Older patients were less likely to receive CRT-defibrillators compared to CRT-pacemakers. Complications were infrequent and not more common in older patients. Age was not a predictor of symptomatic or echocardiographic response to CRT (67.2%, 71.2% and 62.6% responders in patients aged <70, 70-79 and ≥ 80 years, respectively; P = 0.43), and time to first heart failure hospitalisation was similar across age groups (P = 0.28). Ten-year survival was lower for older patients (49.9%, 23.9% and 6.8% in patients aged <70, 70-79 and ≥ 80 years, respectively; P < 0.001). CONCLUSIONS: The benefits of CRT on symptoms and left ventricular function were not different in older patients despite a greater burden of co-morbidities and less optimal medical therapy. These findings support the use of CRT in an ageing population.

The impact of the COVID-19 pandemic on rates and predictors of missed hospital appointments in multiple outpatient clinics of The Royal Hospital, Sultanate of Oman: a retrospective study.

BACKGROUND: The global outbreak of the COVID-19 pandemic resulted in significant changes in the delivery of health care services such as attendance of scheduled outpatient hospital appointments. This study aimed to evaluate the impact of COVID-19 on the rate and predictors of missed hospital appointment in the Sultanate of Oman. METHODS: A retrospective single-centre analysis was conducted to determine the effect of COVID-19 on missed hospital appointments at various clinics at The Royal Hospital (tertiary referral hospital) in Muscat, Sultanate of Oman. The study population included scheduled face-to-face and virtual appointments between January 2019 and March 2021. Logistic regression models were used with interaction terms (post COVID-19) to assess changes in the predictors of missed appointments. RESULTS: A total of 34, 3149 scheduled appointments was analysed (320,049 face-to-face and 23,100 virtual). The rate of missed face-to-face hospital appointments increased from 16.9% pre to 23.8% post start of COVID-19, particularly in early pandemic (40.5%). Missed hospital appointments were more frequent (32.2%) in virtual clinics (post COVID-19). Increases in missed face-to-face appointments varied by clinic (Paediatrics from 19.3% pre to 28.2% post; Surgery from 12.5% to 25.5%; Obstetrics & Gynaecology from 8.4% to 8.5%). A surge in the frequency of missed appointments was seen during national lockdowns for face-to-face and virtual appointments. Most predictors of missed appointments did not demonstrate any appreciable changes in effect (i.e., interaction term not statistically significant). Distance of patient residence to the hospital revealed no discernible changes in the relative effect pre and post COVID-19 for both face-to-face and virtual clinic appointments. CONCLUSION: The rate of missed visits in most clinics was directly impacted by COVID-19. The case mix of patients who missed their appointments did not change. Virtual appointments, introduced after start of the pandemic, also had substantial rates of missed appointments and cannot be viewed as the single approach that can overcome the problem of missing hospital appointments.

Clinician acceptability of an antibiotic prescribing knowledge support system for primary care: a mixed-method evaluation of features and context.

BACKGROUND: Overprescribing of antibiotics is a major concern as it contributes to antimicrobial resistance. Research has found highly variable antibiotic prescribing in (UK) primary care, and to support more effective stewardship, the BRIT Project (Building Rapid Interventions to optimise prescribing) is implementing an eHealth Knowledge Support System. This will provide unique individualised analytics information to clinicians and patients at the point of care. The objective of the current study was to gauge the acceptability of the system to prescribing healthcare professionals and highlight factors to maximise intervention uptake. METHODS: Two mixed-method co-design workshops were held online with primary care prescribing healthcare professionals (n = 16). Usefulness ratings of example features were collected using online polls and online whiteboards. Verbal discussion and textual comments were analysed thematically using inductive (participant-centred) and deductive perspectives (using the Theoretical Framework of Acceptability). RESULTS: Hierarchical thematic coding generated three overarching themes relevant to intervention use and development. Clinician concerns (focal issues) were safe prescribing, accessible information, autonomy, avoiding duplication, technical issues and time. Requirements were ease and efficiency of use, integration of systems, patient-centeredness, personalisation, and training. Important features of the system included extraction of pertinent information from patient records (such as antibiotic prescribing history), recommended actions, personalised treatment, risk indicators and electronic patient communication leaflets. Anticipated acceptability and intention to use the knowledge support system was moderate to high. Time was identified as a focal cost/ burden, but this would be outweighed if the system improved patient outcomes and increased prescribing confidence. CONCLUSION: Clinicians anticipate that an eHealth knowledge support system will be a useful and acceptable way to optimise antibiotic prescribing at the point of care. The mixed method workshop highlighted issues to assist person-centred eHealth intervention development, such as the value of communicating patient outcomes. Important features were identified including the ability to efficiently extract and summarise pertinent information from the patient records, provide explainable and transparent risk information, and personalised information to support patient communication. The Theoretical Framework of Acceptability enabled structured, theoretically sound feedback and creation of a profile to benchmark future evaluations. This may encourage a consistent user-focused approach to guide future eHealth intervention development.

A first step towards best practice recommendations for the design and statistical analyses of pragmatic clinical trials: A modified Delphi approach.

AIM: Pragmatic clinical trials (PCTs) are randomized trials implemented through routine clinical practice, where design parameters of traditional randomized controlled trials are modified to increase generalizability. However, this may introduce statistical challenges. We aimed to identify these challenges and discuss possible solutions leading to best practice recommendations for the design and analysis of PCTs. METHODS: A modified Delphi method was used to reach consensus among a panel of 11 experts in clinical trials and statistics. Statistical issues were identified in a focused literature review and aggregated with insights and possible solutions from experts collected through a series of survey iterations. Issues were ranked according to their importance. RESULTS: Twenty-seven articles were included and combined with experts' insight to generate a list of issues categorized into participants, recruiting sites, randomization, blinding and intervention, outcome (selection and measurement) and data analysis. Consensus was reached about the most important issues: risk of participants' attrition, heterogeneity of "usual care" across sites, absence of blinding, use of a subjective endpoint and data analysis aligned with the trial estimand. Potential issues should be anticipated and preferably be addressed in the trial protocol. The experts provided solutions regarding data collection and data analysis, which were considered of equal importance. DISCUSSION: A set of important statistical issues in PCTs was identified and approaches were suggested to anticipate and/or minimize these through data analysis. Any impact of choosing a pragmatic design feature should be gauged in the light of the trial estimand.

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.SummaryPrescribing 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.

The Safety of Delayed Versus Immediate Antibiotic Prescribing for Upper Respiratory Tract Infections.

BACKGROUND: This study aimed to evaluate the clinical safety of delayed antibiotic prescribing for upper respiratory tract infections (URTIs), which is recommended in treatment guidelines for less severe cases. METHODS: Two population-based cohort studies used the English Clinical Practice Research Databank and Welsh Secure Anonymized Information Linkage, containing electronic health records from primary care linked to hospital admission records. Patients with URTI and prescriptions of amoxicillin, clarithromycin, doxycycline, erythromycin, or phenoxymethylpenicillin were identified. Patients were stratified according to delayed and immediate prescribing relative to URTI diagnosis. Outcome of interest was infection-related hospital admission after 30 days. RESULTS: The population included 1.82 million patients with an URTI and antibiotic prescription; 91.7% had an antibiotic at URTI diagnosis date (immediate) and 8.3% had URTI diagnosis in 1-30 days before (delayed). Delayed antibiotic prescribing was associated with a 52% increased risk of infection-related hospital admissions (adjusted hazard ratio, 1.52; 95% confidence interval, 1.43-1.62). The probability of delayed antibiotic prescribing was unrelated to predicted risks of hospital admission. Analyses of the number needed to harm showed considerable variability across different patient groups (median with delayed antibiotic prescribing, 1357; 2.5% percentile, 295; 97.5% percentile, 3366). CONCLUSIONS: This is the first large population-based study examining the safety of delayed antibiotic prescribing. Waiting to treat URTI was associated with increased risk of hospital admission, although delayed antibiotic prescribing was used similarly between high- and low-risk patients. There is a need to better target delayed antibiotic prescribing to URTI patients with lower risks of complications.

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.

The effectiveness of frequent antibiotic use in reducing the risk of infection-related hospital admissions: results from two large population-based cohorts.

BACKGROUND: Previous research reported that individuals prescribed antibiotics frequently develop antimicrobial resistance. The objective of this study was to evaluate whether frequent antibiotic use is associated with reduced hospital admissions for infection-related complications. METHODS: Population-based cohort study analysing electronic health records from primary care linked to hospital admission records. The study population included patients prescribed a systemic antibiotic, recent record of selected infections and no history of chronic obstructive pulmonary disease. Propensity-matched cohorts were identified based on quintiles of prior antibiotic use in 3 years before. RESULTS: A total of 1.8 million patients were included. Repeated antibiotic use was frequent. The highest rates of hospital admissions for infection-related complications were observed shortly after antibiotic start in all prior exposure quintiles. For patients with limited prior antibiotic use, rates then dropped quickly and substantially. In contrast, reductions over time were substantially less in patients with frequent prior antibiotic use, with rates remaining elevated over the following 6 months. In patients without comorbidity comparing the highest to lowest prior exposure quintiles in the Clinical Practice Research Databank, the IRRs were 1.18 [95% CI 0.90-1.55] in the first 3 days after prescription, 1.44 [95% CI 1.14-1.81] in the days 4-30 after and 3.22 [95% CI 2.29-4.53] in the 3-6 months after. CONCLUSIONS: Repeated courses of antibiotics, although common practice, may have limited benefit and indicator of adverse outcomes. A potential mechanism is that antibiotics may cause dysbiosis (perturbations of intestinal microbiota), contributing to colonization with resistant bacteria. Antibiotics should be used judiciously and only periodically unless indicated. Antimicrobial stewardship should include activities focusing on the substantive number of patients who repeatedly but intermittently get antibiotics.

Antibiotic choice in UK general practice: rates and drivers of potentially inappropriate antibiotic prescribing.

OBJECTIVES: To identify the rates of potentially inappropriate antibiotic choice when prescribing for common infections in UK general practices. To examine the predictors of such prescribing and the clustering effects at the practice level. METHODS: The rates of potentially inappropriate antibiotic choice were estimated using 1 151 105 consultations for sinusitis, otitis media and externa, upper respiratory tract infection (URTI) and lower respiratory tract infection (LRTI) and urinary tract infection (UTI), using the Clinical Practice Research Datalink (CPRD). Multilevel logistic regression was used to identify the predictors of inappropriate prescribing and to quantify the clustering effect at practice level. RESULTS: The rates of potentially inappropriate prescriptions were highest for otitis externa (67.3%) and URTI (38.7%) and relatively low for otitis media (3.4%), sinusitis (2.2%), LRTI (1.5%) and UTI in adults (2.3%) and children (0.7%). Amoxicillin was the most commonly prescribed antibiotic for all respiratory tract infections, except URTI. Amoxicillin accounted for 62.3% of prescriptions for otitis externa and 34.5% of prescriptions for URTI, despite not being recommended for these conditions. A small proportion of the variation in the probability of an inappropriate choice was attributed to the clustering effect at practice level (8% for otitis externa and 23% for sinusitis). Patients with comorbidities were more likely to receive a potentially inappropriate antibiotic for URTI, LRTI and UTI in adults. Patients who received any antibiotic in the 12 months before consultation were more likely to receive a potentially inappropriate antibiotic for all conditions except otitis externa. CONCLUSIONS: Antibiotic prescribing did not always align with prescribing guidelines, especially for URTIs and otitis externa. Future interventions might target optimizing amoxicillin use in primary care.

Antibiotic prescribing for common infections in UK general practice: variability and drivers.

OBJECTIVES: To examine variations across general practices and factors associated with antibiotic prescribing for common infections in UK primary care to identify potential targets for improvement and optimization of prescribing. METHODS: Oral antibiotic prescribing for common infections was analysed using anonymized UK primary care electronic health records between 2000 and 2015 using the Clinical Practice Research Datalink (CPRD). The rate of prescribing for each condition was observed over time and mean change points were compared with national guideline updates. Any correlation between the rate of prescribing for each infectious condition was estimated within a practice. Predictors of prescribing were estimated using logistic regression in a matched patient cohort (1:1 by age, sex and calendar time). RESULTS: Over 8 million patient records were examined in 587 UK general practices. Practices varied considerably in their propensity to prescribe antibiotics and this variance increased over time. Change points in prescribing did not reflect updates to national guidelines. Prescribing levels within practices were not consistent for different infectious conditions. A history of antibiotic use significantly increased the risk of receiving a subsequent antibiotic (by 22%-48% for patients with three or more antibiotic prescriptions in the past 12 months), as did higher BMI, history of smoking and flu vaccinations. Other drivers for receiving an antibiotic varied considerably for each condition. CONCLUSIONS: Large variability in antibiotic prescribing between practices and within practices was observed. Prescribing guidelines alone do not positively influence a change in prescribing, suggesting more targeted interventions are required to optimize antibiotic prescribing in the UK.

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.

The Impact of Oral Antibiotics Prior to Cancer Diagnosis on Overall Patient Survival: Findings from an English Population-Based Cohort Study.

BACKGROUND: There is limited evidence in humans as to whether antibiotics impact the effectiveness of cancer treatments. Rodent studies have shown that disruption in gut microbiota due to antibiotics decreases cancer therapy effectiveness. We evaluated the associations between the antibiotic treatment of different time periods before cancer diagnoses and long-term mortality. METHODS: Using the Clinical Practice Research Datalink GOLD, linked to the Cancer Registry's and the Office for National Statistics' mortality records, we delineated a study cohort that involved cancer patients who were prescribed antibiotics 0-3 months; 3-24 months; or more than 24 months before cancer diagnosis. Patients' exposure to antibiotics was compared according to the recency of prescriptions and time-to-event (all-cause mortality) by applying Cox models. RESULTS: 111,260 cancer patients from England were included in the analysis. Compared with antibiotic prescriptions that were issued in the past, patients who had been prescribed antibiotics shortly before cancer diagnosis presented an increased hazard ratio (HR) for mortality. For leukaemia, the HR in the Cancer Registry was 1.32 (95% CI 1.16-1.51), for lymphoma it was 1.22 (1.08-1.36), for melanoma it was 1.28 (1.10-1.49), and for myeloma it was 1.19 (1.04-1.36). Increased HRs were observed for cancer of the uterus, bladder, and breast and ovarian and colorectal cancer. CONCLUSIONS: Antibiotics that had been issued within the three months prior to cancer diagnosis may reduce the effectiveness of chemotherapy and immunotherapy. Judicious antibiotic prescribing is needed among cancer patients.

The impact of the COVID-19 pandemic on the treatment of common infections in primary care and the change to antibiotic prescribing in England.

BACKGROUND: There is concern that the COVID-19 pandemic altered the management of common infections in primary care. This study aimed to evaluate infection-coded consultation rates and antibiotic use during the pandemic and how any change may have affected clinical outcomes. METHODS: With the approval of NHS England, a retrospective cohort study using the OpenSAFELY platform analysed routinely collected electronic health data from GP practices in England between January 2019 and December 2021. Infection coded consultations and antibiotic prescriptions were used estimate multiple measures over calendar months, including age-sex adjusted prescribing rates, prescribing by infection and antibiotic type, infection consultation rates, coding quality and rate of same-day antibiotic prescribing for COVID-19 infections. Interrupted time series (ITS) estimated the effect of COVID-19 pandemic on infection-coded consultation rates. The impact of the pandemic on non- COVID-19 infection-related hospitalisations was also estimated. RESULTS: Records from 24 million patients were included. The rate of infection-related consultations fell for all infections (mean reduction of 39% in 2020 compared to 2019 mean rate), except for UTI which remained stable. Modelling infection-related consultation rates highlighted this with an incidence rate ratio of 0.44 (95% CI 0.36-0.53) for incident consultations and 0.43 (95% CI 0.33-0.54) for prevalent consultations. Lower respiratory tract infections (LRTI) saw the largest reduction of 0.11 (95% CI 0.07-0.17). Antibiotic prescribing rates fell with a mean reduction of 118.4 items per 1000 patients in 2020, returning to pre-pandemic rates by summer 2021. Prescribing for LRTI decreased 20% and URTI increased 15.9%. Over 60% of antibiotics were issued without an associated same-day infection code, which increased during the pandemic. Infection-related hospitalisations reduced (by 62%), with the largest reduction observed for pneumonia infections (72.9%). Same-day antibiotic prescribing for COVID-19 infection increased from 1 to 10.5% between the second and third national lockdowns and rose again during 2022. CONCLUSIONS: Changes to consultations and hospital admissions may be driven by reduced transmission of non-COVID-19 infections due to reduced social mixing and lockdowns. Inconsistencies in coding practice emphasises the need for improvement to inform new antibiotic stewardship policies and prevent resistance to novel infections.

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.

Associations of health-related quality of life with major adverse cardiovascular and cerebrovascular events for individuals with ischaemic heart disease: systematic review, meta-analysis and evidence mapping.

OBJECTIVE: To investigate the association between health-related quality of life (HRQoL) and major adverse cardiovascular and cerebrovascular events (MACCE) in individuals with ischaemic heart disease (IHD). METHODS: Medline(R), Embase, APA PsycINFO and CINAHL (EBSCO) from inception to 3 April 2023 were searched. Studies reporting association of HRQoL, using a generic or cardiac-specific tool, with MACCE or components of MACCE for individuals with IHD were eligible for inclusion. Risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale to assess the quality of the studies. Descriptive synthesis, evidence mapping and random-effects meta-analysis were performed stratified by HRQoL measures and effect estimates. Between-study heterogeneity was assessed using the Higgins I2 statistic. RESULTS: Fifty-one articles were included with a total of 134 740 participants from 53 countries. Meta-analysis of 23 studies found that the risk of MACCE increased with lower baseline HeartQoL score (HR 1.49, 95% CI 1.16 to 1.93) and Short Form Survey (SF-12) physical component score (PCS) (HR 1.39, 95% CI 1.28 to 1.51). Risk of all-cause mortality increased with a lower HeartQoL (HR 1.64, 95% CI 1.34 to 2.01), EuroQol 5-dimension (HR 1.17, 95% CI 1.12 to 1.22), SF-36 PCS (HR 1.29, 95% CI 1.19 to 1.41), SF-36 mental component score (HR 1.18, 95% CI 1.08 to 1.30). CONCLUSIONS: This study found an inverse association between baseline values or change in HRQoL and MACCE or components of MACCE in individuals with IHD, albeit with between-study heterogeneity. Standardisation and routine assessment of HRQoL in clinical practice may help risk stratify individuals with IHD for tailored interventions. PROSPERO REGISTRATION NUMBER: CRD42021234638.

Repeated antibiotic exposure and risk of hospitalisation and death following COVID-19 infection (OpenSAFELY): a matched case-control study.

Background: Identifying potential risk factors related to severe COVID-19 outcomes is important. Repeated intermittent antibiotic use is known be associated with adverse outcomes. This study aims to examine whether prior frequent antibiotic exposure is associated with severe COVID-19 outcomes. Methods: With the approval of NHS England, we used the OpenSAFELY platform, which integrated primary and secondary care, COVID-19 test, and death registration data. This matched case-control study included 0.67 million patients (aged 18-110 years) from an eligible 2.47 million patients with incident COVID-19 by matching with replacement. Inclusion criteria included registration within one general practice for at least 3 years and infection with incident COVID-19. Cases were identified according to different severity of COVID-19 outcomes. Cases and eligible controls were 1:6 matched on age, sex, region of GP practice, and index year and month of COVID-19 infection. Five quintile groups, based on the number of previous 3-year antibiotic prescriptions, were created to indicate the frequency of prior antibiotic exposure. Conditional logistic regression used to compare the differences between case and control groups, adjusting for ethnicity, body mass index, comorbidities, vaccination history, deprivation, and care home status. Sensitivity analyses were done to explore potential confounding and the effects of missing data. Findings: Based on our inclusion criteria, between February 1, 2020 and December 31, 2021, 98,420 patients were admitted to hospitals and 22,660 died. 55 unique antibiotics were prescribed. A dose-response relationship between number of antibiotic prescriptions and risk of severe COVID-19 outcome was observed. Patients in the highest quintile with history of prior antibiotic exposure had 1.80 times greater odds of hospitalisation compared to patients without antibiotic exposure (adjusted odds ratio [OR] 1.80, 95% Confidence Interval [CI] 1.75-1.84). Similarly, the adjusted OR for hospitalised patients with death outcomes was 1.34 (95% CI 1.28-1.41). Larger number of prior antibiotic type was also associated with more severe COVID-19 related hospital admission. The adjusted OR of quintile 5 exposure (the most frequent) with more than 3 antibiotic types was around 2 times larger than quintile 1 (only 1 type; OR 1.80, 95% CI 1.75-1.84 vs. OR 1.03, 95% CI 1.01-1.05). Interpretation: Our observational study has provided evidence that antibiotic exposure frequency and diversity may be associated with COVID-19 severity, potentially suggesting adverse effects of repeated intermittent antibiotic use. Future work could work to elucidate causal links and potential mechanisms. Antibiotic stewardship should put more emphasis on long-term antibiotic exposure and its adverse outcome to increase the awareness of appropriate antibiotics use. Funding: Health Data Research UK and National Institute for Health Research.