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.

Adverse drug reactions and hospital admissions: Large case-control study of patients aged 65-100 years using linked English primary care and hospital data.

BACKGROUND: Adverse drug reactions (ADRs) are common and a leading cause of injury. However, information on ADR risks of individual medicines is often limited. The aim of this hypothesis-generating study was to assess the relative importance of ADR-related and emergency hospital admission for large group of medication classes. METHODS: This study was a propensity-matched case-control study in English primary care. Data sources were Clinical Practice Research Databank and Aurum with longitudinal, anonymized, patient level electronic health records (EHRs) from English general practices linked to hospital records. Cases aged 65-100 with ADR-related or emergency hospital admission were matched to up to six controls by age, sex, morbidity and propensity scores for hospital admission risk. Medication groups with systemic administration as listed in the British National Formulary (used by prescribers for medication advice). Prescribing in the 84 days before the index date was assessed. Only medication groups with 50+ cases exposed were analysed. The outcomes of interest were ADR-related and emergency hospital admissions. Conditional logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CI). RESULTS: The overall population included 121 546 cases with an ADR-related and 849 769 cases with emergency hospital admission. The percentage of hospitalizations with an ADR-related code for admission diagnosis was 1.83% and 6.58% with an ADR-related code at any time during hospitalization. A total of 137 medication groups was included in the main ADR analyses. Of these, 13 (9.5%) had statistically non-significant adjusted ORs, 58 (42.3%) statistically significant ORs between 1.0 and 1.5, 37 (27.0%) between 1.5-2.0, 18 (13.1%) between 2.0-3.0 and 11 (8.0%) 3.0 or higher. Several classes of antibiotics (including penicillins) were among medicines with largest ORs. Evaluating the 14 medications most often associated with ADRs, a strong association was found between the number of these medicines and the risk of ADR-related hospital admission (adjusted OR of 7.53 (95% CI 7.15-7.93) for those exposed to 6+ of these medicines). CONCLUSIONS AND RELEVANCE: There is a need for a regular systematic assessment of the harm-benefit ratio of medicines, harvesting the information in large healthcare databases and combining it with causality assessment of individual case histories.

Combinations of medicines in patients with polypharmacy aged 65-100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions.

BACKGROUND: Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. METHODS: We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. RESULTS: In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65-7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. CONCLUSIONS: Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients.

Clinical Relevance of Drug-Drug Interactions With Antibiotics as Listed in a National Medication Formulary: Results From Two Large Population-Based Case-Control Studies in Patients Aged 65-100 Years Using Linked English Primary Care and Hospital Data.

This study evaluated drug-drug interactions (DDIs) between antibiotic and nonantibiotic drugs listed with warnings of severe outcomes in the British National Formulary based on adverse drug reaction (ADR) detectable with routine International Classification of Diseases, Tenth Revision coding. Data sources were Clinical Practice Research Databank GOLD and Aurum anonymized electronic health records from English general practices linked to hospital admission records. In propensity-matched case-control study, outcomes were ADR or emergency admissions. Analyzed were 121,546 ADR-related admission cases matched to 638,238 controls. For most antibiotics, adjusted odds ratios (aORs) for ADR-related hospital admission were large (aOR for trimethoprim 4.13; 95% confidence interval (CI), 3.97-4.30). Of the 51 DDIs evaluated for ADR-related admissions, 38 DDIs (74.5%) had statistically increased aORs of concomitant exposure compared with nonexposure (mean aOR 3.96; range 1.59-11.42); for the 89 DDIs for emergency hospital admission, the results were 75 (84.3%) and mean aOR 2.40; range 1.43-4.17. Changing reference group to single antibiotic exposure reduced aORs for concomitant exposure by 76.5% and 83.0%, respectively. Medicines listed to cause nephrotoxicity substantially increased risks that were related to number of medicines (aOR was 2.55 (95% CI, 2.46-2.64) for current use of 1 and 10.44 (95% CI, 7.36-14.81) for 3 or more medicines). In conclusion, no evidence of substantial risk was found for multiple DDIs with antibiotics despite warnings of severe outcomes in a national formulary and flagging in electronic health record software. It is proposed that the evidence base for inclusion of DDIs in national formularies be strengthened and made publicly accessible and indiscriminate flagging, which compounds alert fatigue, be reduced.

Clinical and health inequality risk factors for non-COVID-related sepsis during the global COVID-19 pandemic: a national case-control and cohort study.

Background: Sepsis, characterised by significant morbidity and mortality, is intricately linked to socioeconomic disparities and pre-admission clinical histories. This study aspires to elucidate the association between non-COVID-19 related sepsis and health inequality risk factors amidst the pandemic in England, with a secondary focus on their association with 30-day sepsis mortality. Methods: With the approval of NHS England, we harnessed the OpenSAFELY platform to execute a cohort study and a 1:6 matched case-control study. A sepsis diagnosis was identified from the incident hospital admissions record using ICD-10 codes. This encompassed 248,767 cases with non-COVID-19 sepsis from a cohort of 22.0 million individuals spanning January 1, 2019, to June 31, 2022. Socioeconomic deprivation was gauged using the Index of Multiple Deprivation score, reflecting indicators like income, employment, and education. Hospitalisation-related sepsis diagnoses were categorised as community-acquired or hospital-acquired. Cases were matched to controls who had no recorded diagnosis of sepsis, based on age (stepwise), sex, and calendar month. The eligibility criteria for controls were established primarily on the absence of a recorded sepsis diagnosis. Associations between potential predictors and odds of developing non-COVID-19 sepsis underwent assessment through conditional logistic regression models, with multivariable regression determining odds ratios (ORs) for 30-day mortality. Findings: The study included 224,361 (10.2%) cases with non-COVID-19 sepsis and 1,346,166 matched controls. The most socioeconomic deprived quintile was associated with higher odds of developing non-COVID-19 sepsis than the least deprived quintile (crude OR 1.80 [95% CI 1.77-1.83]). Other risk factors (after adjusting comorbidities) such as learning disability (adjusted OR 3.53 [3.35-3.73]), chronic liver disease (adjusted OR 3.08 [2.97-3.19]), chronic kidney disease (stage 4: adjusted OR 2.62 [2.55-2.70], stage 5: adjusted OR 6.23 [5.81-6.69]), cancer, neurological disease, immunosuppressive conditions were also associated with developing non-COVID-19 sepsis. The incidence rate of non-COVID-19 sepsis decreased during the COVID-19 pandemic and rebounded to pre-pandemic levels (April 2021) after national lockdowns had been lifted. The 30-day mortality risk in cases with non-COVID-19 sepsis was higher for the most deprived quintile across all periods. Interpretation: Socioeconomic deprivation, comorbidity and learning disabilities were associated with an increased odds of developing non-COVID-19 related sepsis and 30-day mortality in England. This study highlights the need to improve the prevention of sepsis, including more precise targeting of antimicrobials to higher-risk patients. Funding: The UK Health Security Agency, Health Data Research UK, and National Institute for Health Research.

Consistency of ranking was evaluated as new measure for prediction model stability: longitudinal cohort study.

OBJECTIVE: Clinical risk prediction models are generally assessed on population level with a lack of measures that evaluate their stability at predicting risks of individual patients. This study evaluated the use of ranking as a measure to assess individual level stability between risk prediction models. STUDY DESIGN AND SETTING: A large patient cohort (3.66 million patients with 0.11 million cardiovascular events) extracted from the Clinical Practice Research Datalink was used in the exemplar of cardiovascular disease risk prediction. RESULTS: It was found that 15 models (including machine learning and statistical models) had similar population-level model performance (C statistics about 0.88). For patients with high absolute risks, the models were more consistent in ranking of risk predictions (interquartile range (IQR) of differences in rank percentiles -0.6 to 1.0), but inconsistent in absolute risk (IQR of differences in absolute risk -18.8 to 9.0). At low risk, the reverse was true with inconsistent ranking but more consistent absolute risk. CONCLUSION: Consistency of ranking of individual risk predictions is a useful measure to assess risk prediction models providing complementary information to absolute risk stability. Model developing guidelines including "TRIPOD" and "PROBAST" should incorporate ranking to assess individual level stability between risk prediction models.

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.

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.

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.

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.

Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.

OBJECTIVE: To assess the consistency of machine learning and statistical techniques in predicting individual level and population level risks of cardiovascular disease and the effects of censoring on risk predictions. DESIGN: Longitudinal cohort study from 1 January 1998 to 31 December 2018. SETTING AND PARTICIPANTS: 3.6 million patients from the Clinical Practice Research Datalink registered at 391 general practices in England with linked hospital admission and mortality records. MAIN OUTCOME MEASURES: Model performance including discrimination, calibration, and consistency of individual risk prediction for the same patients among models with comparable model performance. 19 different prediction techniques were applied, including 12 families of machine learning models (grid searched for best models), three Cox proportional hazards models (local fitted, QRISK3, and Framingham), three parametric survival models, and one logistic model. RESULTS: The various models had similar population level performance (C statistics of about 0.87 and similar calibration). However, the predictions for individual risks of cardiovascular disease varied widely between and within different types of machine learning and statistical models, especially in patients with higher risks. A patient with a risk of 9.5-10.5% predicted by QRISK3 had a risk of 2.9-9.2% in a random forest and 2.4-7.2% in a neural network. The differences in predicted risks between QRISK3 and a neural network ranged between -23.2% and 0.1% (95% range). Models that ignored censoring (that is, assumed censored patients to be event free) substantially underestimated risk of cardiovascular disease. Of the 223 815 patients with a cardiovascular disease risk above 7.5% with QRISK3, 57.8% would be reclassified below 7.5% when using another model. CONCLUSIONS: A variety of models predicted risks for the same patients very differently despite similar model performances. The logistic models and commonly used machine learning models should not be directly applied to the prediction of long term risks without considering censoring. Survival models that consider censoring and that are explainable, such as QRISK3, are preferable. The level of consistency within and between models should be routinely assessed before they are used for clinical decision making.

Correction to: The role of real-world data in the development of treatment guidelines: a case study on guideline developers' opinions about using observational data on antibiotic prescribing in primary care.

An amendment to this paper has been published and can be accessed via the original article.

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.

Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease.

OBJECTIVE: To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3). DESIGN: Longitudinal cohort study. SETTINGS: 392 general practices (including 3.6 million patients) linked to hospital admission data. METHODS: Variation in data quality was assessed using Sáez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices. RESULTS: There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients. CONCLUSIONS: The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.

The role of real-world data in the development of treatment guidelines: a case study on guideline developers' opinions about using observational data on antibiotic prescribing in primary care.

BACKGROUND: Antimicrobial resistance (AMR) is a prominent threat to public health. Although many guidelines have been developed over the years to tackle this issue, their impact on health care practice varies. Guidelines are often based on evidence from clinical trials, but these have limitations, particularly in the breadth and generalisability of the evidence and evaluation of the guidelines' uptake. The aim of this study was to investigate how national and local guidelines for managing common infections are developed and explore guideline committee members' opinions about using real-world observational evidence in the guideline development process. METHODS: Six semi-structured interviews were completed with participants who had contributed to the development or adjustment of national or local guidelines on antimicrobial prescribing over the past 5 years (from the English National Institute for Health and Care Excellence (NICE)). Interviews were audio recorded and transcribed verbatim. Data was analysed thematically. This also included review of policy documents including guidelines, reports and minutes of guideline development group meetings that were available to the public. RESULTS: Three key themes emerged through our analysis: perception versus actual guideline development process, using other types of evidence in the guideline development process, and guidelines are not enough to change antibiotic prescribing behaviour. In addition, our study was able to provide some insight between the documented and actual guideline development process within NICE, as well as how local guidelines are developed, including differences in types of evidence used. CONCLUSIONS: This case study indicates that there is the potential for a wider range of evidence to be included as part of the guideline development process at both the national and local levels. There was a general agreement that the inclusion of observational data would be appropriate in enhancing the guideline development process, as well providing a potential solution for monitoring guideline use in clinical practice, and improving the implementation of treatment guidelines in primary care.

Do population-level risk prediction models that use routinely collected health data reliably predict individual risks?

The objective of this study was to assess the reliability of individual risk predictions based on routinely collected data considering the heterogeneity between clinical sites in data and populations. Cardiovascular disease (CVD) risk prediction with QRISK3 was used as exemplar. The study included 3.6 million patients in 392 sites from the Clinical Practice Research Datalink. Cox models with QRISK3 predictors and a frailty (random effect) term for each site were used to incorporate unmeasured site variability. There was considerable variation in data recording between general practices (missingness of body mass index ranged from 18.7% to 60.1%). Incidence rates varied considerably between practices (from 0.4 to 1.3 CVD events per 100 patient-years). Individual CVD risk predictions with the random effect model were inconsistent with the QRISK3 predictions. For patients with QRISK3 predicted risk of 10%, the 95% range of predicted risks were between 7.2% and 13.7% with the random effects model. Random variability only explained a small part of this. The random effects model was equivalent to QRISK3 for discrimination and calibration. Risk prediction models based on routinely collected health data perform well for populations but with great uncertainty for individuals. Clinicians and patients need to understand this uncertainty.

The use of wearable technology to monitor physical activity in patients with COPD: a literature review.

Background: Physical activity is an important predictor for survival in patients with COPD. Wearable technology, such as pedometer or accelerometer, may offer an opportunity to quantify physical activity and evaluate related health benefits in these patients. Objectives: To assess the performance of wearable technology in monitoring and improving physical activity in COPD patients from published studies. Methods: Literature search of Medline, Cochrane, Dare, Embase and PubMed databases was made to find relevant articles that used wearable technology to monitor physical activity in COPD patients. Results: We identified 13 studies that used wearable technology, a pedometer or an accelerator, to monitor physical activity in COPD patients. Of these, six studies were randomized controlled trials (RCTs) which used the monitors as part of the intervention. Two studies reported the same outcomes and comparable units. They had measured the difference that the intervention makes on the number of steps taken daily by the patients. The results were highly heterogeneous with I2=92%. The random-effects model gave an effect outcome on the number of steps taken daily of 1,821.01 [-282.71; 3,924.74] in favor of the wearable technology. Four of the 13 studies have reported technical issues with the use of the wearable technology, including high signal-to-noise ratio, memory storage problems and inaccuracy of counts. While other studies did not mention any technical issues, it is not clear whether these did not experience them or chose not to report them. Conclusions: Our literature search has shown that data on the use of wearable technology to monitor physical activity in COPD patients are limited by the small number of studies and their heterogeneous study design. Further research and better-designed RCTs are needed to provide reliable results before physical activity monitors can be implemented routinely for COPD patients.

Relationship between prescribing of antibiotics and other medicines in primary care: a cross-sectional study.

BACKGROUND: High levels of antibiotic prescribing are a major concern as they drive antimicrobial resistance. It is currently unknown whether practices that prescribe higher levels of antibiotics also prescribe more medicines in general. AIM: To evaluate the relationship between antibiotic and general prescribing levels in primary care. DESIGN AND SETTING: Cross-sectional study in 2014-2015 of 6517 general practices in England using NHS digital practice prescribing data (NHS-DPPD) for the main study, and of 587 general practices in the UK using the Clinical Practice Research Datalink for a replication study. METHOD: Linear regression to assess determinants of antibiotic prescribing. RESULTS: NHS-DPPD practices prescribed an average of 576.1 antibiotics per 1000 patients per year (329.9 at the 5th percentile and 808.7 at the 95th percentile). The levels of prescribing of antibiotics and other medicines were strongly correlated. Practices with high levels of prescribing of other medicines (a rate of 27 159.8 at the 95th percentile) prescribed 80% more antibiotics than low-prescribing practices (rate of 8815.9 at the 5th percentile). After adjustment, NHS-DPPD practices with high prescribing of other medicines gave 60% more antibiotic prescriptions than low-prescribing practices (corresponding to higher prescribing of 276.3 antibiotics per 1000 patients per year). Prescribing of non-opioid painkillers and benzodiazepines were also strong indicators of the level of antibiotic prescribing. General prescribing levels were a much stronger driver for antibiotic prescribing than other risk factors, such as deprivation. CONCLUSION: The propensity of GPs to prescribe medications generally is an important driver for antibiotic prescribing. Interventions that aim to optimise antibiotic prescribing will need to target general prescribing behaviours, in addition to specifically targeting antibiotics.

Cancer recording in patients with and without type 2 diabetes in the Clinical Practice Research Datalink primary care data and linked hospital admission data: a cohort study.

OBJECTIVES AND SETTING: Conflicting results from studies using electronic health records to evaluate the associations between type 2 diabetes and cancer fuel concerns regarding potential biases. This study aimed to describe completeness of cancer recording in UK primary care data linked to hospital admissions records. DESIGN: Patients aged 40+ years with insulin or oral antidiabetic prescriptions in Clinical Practice Research Datalink (CPRD) primary care without type 1 diabetes were matched by age, sex and general practitioner practice to non-diabetics. Those eligible for linkage to Hospital Episode Statistics Admitted Patient Care (HES APC), and with follow-up during April 1997-December 2006 were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Cancer recording and date of first record of cancer were compared. Characteristics of patients with cancer most likely to have the diagnosis recorded only in a single data source were assessed. Relative rates of cancer estimated from the two datasets were compared. PARTICIPANTS: 53 585 patients with type 2 diabetes matched to 47 435 patients without diabetes were included. RESULTS: Of all cancers (excluding non-melanoma skin cancer) recorded in CPRD, 83% were recorded in HES APC. 94% of cases in HES APC were recorded in CPRD. Concordance was lower when restricted to same-site cancer records, and was negatively associated with increasing age. Relative rates for cancer were similar in both datasets. CONCLUSIONS: Good concordance in cancer recording was found between CPRD and HES APC among type 2 diabetics and matched controls. Linked data may reduce misclassification and increase case ascertainment when analysis focuses on site-specific cancers.