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

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

From Springer Nature via Jisc Publications RouterHistory: registration 2020-05-18, pub-electronic 2020-05-26, online 2020-05-26, collection 2020-12Publication status: PublishedAn amendment to this paper has been published and can be accessed via the original article

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

Cohort Multiple Randomised Controlled Trials (cmRCT) design: efficient but biased? A simulation study to evaluate the feasibility of the Cluster cmRCT design

Background The Cohort Multiple Randomised Controlled Trial (cmRCT) is a newly proposed pragmatic trial design; recently several cmRCT have been initiated. This study tests the unresolved question of whether differential refusal in the intervention arm leads to bias or loss of statistical power and how to deal with this. Methods We conduct simulations evaluating a hypothetical cluster cmRCT in patients at risk of cardiovascular disease (CVD). To deal with refusal, we compare the analysis methods intention to treat (ITT), per protocol (PP) and two instrumental variable (IV) methods: two stage predictor substitution (2SPS) and two stage residual inclusion (2SRI) with respect to their bias and power. We vary the correlation between treatment refusal probability and the probability of experiencing the outcome to create different scenarios. Results We found ITT to be biased in all scenarios, PP the most biased when correlation is strong and 2SRI the least biased on average. Trials suffer a drop in power unless the refusal rate is factored into the power calculation. Conclusions The ITT effect in routine practice is likely to lie somewhere between the ITT and IV estimates from the trial which differ significantly depending on refusal rates. More research is needed on how refusal rates of experimental interventions correlate with refusal rates in routine practice to help answer the question of which analysis more relevant. We also recommend updating the required sample size during the trial as more information about the refusal rate is gained

Randomised Evaluations of Accepted Choices in Treatment (REACT) trials: large-scale pragmatic trials within databases of routinely collected electronic healthcare records

Databases of electronic health records (EHR), such as the General Practice Research Database (GPRD), provide a unique opportunity to conduct large scale pragmatic trials. This paper describes the infrastructure as being implemented for two feasibility pragmatic trials within GPRD

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.

BackgroundPolypharmacy 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.MethodsWe 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.ResultsIn 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.ConclusionsPolypharmacy 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

Expert Guidance on Target Product Profile Development for AMR Diagnostic Tests

Diagnostics are widely considered crucial in the fight against antimicrobial resistance (AMR), which is expected to kill 10 million people annually by 2030. Nevertheless, there remains a substantial gap between the need for AMR diagnostics versus their development and implementation. To help address this problem, target product profiles (TPP) have been developed to focus developers’ attention on the key aspects of AMR diagnostic tests. However, during discussion between a multisectoral working group of 51 international experts from industry, academia and healthcare, it was noted that specific AMR-related TPPs could be extended by incorporating the interdependencies between the key characteristics associated with the development of such TPPs. Subsequently, the working group identified 46 characteristics associated with six main categories (i.e., Intended Use, Diagnostic Question, Test Description, Assay Protocol, Performance and Commercial). The interdependencies of these characteristics were then identified and mapped against each other to generate new insights for use by stakeholders. Specifically, it may not be possible for diagnostics developers to achieve all of the recommendations in every category of a TPP and this publication indicates how prioritising specific TPP characteristics during diagnostics development may influence (or not) a range of other TPP characteristics associated with the diagnostic. The use of such guidance, in conjunction with specific TPPs, could lead to more efficient AMR diagnostics development

Prediction of cardiovascular risk using Framingham, ASSIGN and QRISK2: how well do they predict individual rather than population risk?

BACKGROUND: The objective of this study was to evaluate the performance of risk scores (Framingham, Assign and QRISK2) in predicting high cardiovascular disease (CVD) risk in individuals rather than populations. METHODS AND FINDINGS: This study included 1.8 million persons without CVD and prior statin prescribing using the Clinical Practice Research Datalink. This contains electronic medical records of the general population registered with a UK general practice. Individual CVD risks were estimated using competing risk regression models. Individual differences in the 10-year CVD risks as predicted by risk scores and competing risk models were estimated; the population was divided into 20 subgroups based on predicted risk. CVD outcomes occurred in 69,870 persons. In the subgroup with lowest risks, risk predictions by QRISK2 were similar to individual risks predicted using our competing risk model (99.9% of people had differences of less than 2%); in the subgroup with highest risks, risk predictions varied greatly (only 13.3% of people had differences of less than 2%). Larger deviations between QRISK2 and our individual predicted risks occurred with calendar year, different ethnicities, diabetes mellitus and number of records for medical events in the electronic health records in the year before the index date. A QRISK2 estimate of low 10-year CVD risk (<15%) was confirmed by Framingham, ASSIGN and our individual predicted risks in 89.8% while an estimate of high 10-year CVD risk (≥ 20%) was confirmed in only 48.6% of people. The majority of cases occurred in people who had predicted 10-year CVD risk of less than 20%. CONCLUSIONS: Application of existing CVD risk scores may result in considerable misclassification of high risk status. Current practice to use a constant threshold level for intervention for all patients, together with the use of different scoring methods, may inadvertently create an arbitrary classification of high CVD risk