Protocol for the effective feedback to improve primary care prescribing safety (EFIPPS) study : a cluster randomised controlled trial using ePrescribing data

High-risk prescribing in primary care is common and causes considerable harm. Feedback interventions to improve care are attractive because they are relatively cheap to widely implement. There is good evidence that feedback has small to moderate effects, but the most recent Cochrane review called for more high-quality, large trials that explicitly test different forms of feedback. The study is a three-arm cluster-randomised trial with general practices being randomised and outcomes measured at patient level. 262 practices in three Scottish Health Board areas have been randomised (94% of all possible practices). The two active arms receive different forms of prescribing safety data feedback, with rates of high-risk prescribing compared with a ‘usual care’ arm. Sample size estimation used baseline data from participating practices. With 85 practices randomised to each arm, then there is 93% power to detect a 25% difference in the percentage of high-risk prescribing (from 6.1% to 4.5%) between the usual care arm and each intervention arm. The primary outcome is a composite of six high-risk prescribing measures (antipsychotic prescribing to people aged ≥75 years; non-steroidal anti-inflammatory drug (NSAID) prescribing to people aged ≥75 without gastroprotection; NSAID prescribing to people prescribed aspirin/clopidogrel without gastroprotection; NSAID prescribing to people prescribed an ACE inhibitor/angiotensin receptor blocker and a diuretic; NSAID prescription to people prescribed an oral anticoagulant without gastroprotection; aspirin/clopidogrel prescription to people prescribed an oral anticoagulant without gastroprotection). The primary analysis will use multilevel modelling to account for repeated measurement of outcomes in patients clustered within practices. The study was reviewed and approved by the NHS Tayside Committee on Medical Research Ethics B (11/ES/0001). The study will be disseminated via a final report to the funder with a publicly available research summary, and peer reviewed publications

Use of direct oral anticoagulants in patients with atrial fibrillation in Scotland : applying a coherent framework to drug utilisation studies

Purpose: To report the use of direct oral anticoagulants (DOAC) for stroke prevention in patients with atrial fibrillation (AF) in Scotland and advocate the standardisation of drug utilisation research methods. Methods: Retrospective cohort study using linked administrative data. Patients included those with a diagnosis of AF (confirmed in hospital) who received a first prescription for a DOAC (dabigatran, rivaroxaban, apixaban) from September 2011 to June 2014. Drug utilisation measures included discontinuation, persistence, and adherence. Results: 5398 patients (mean CHA2DS2-VASc score 2.98 [SD 1.71], 89.7% with ≥ 6 concomitant medicines) were treated with DOACs for a median of 228 days (IQR 105 – 425). Of 35.6% who discontinued DOAC treatment, 11.0% switched to warfarin and 48.3% re-initiated DOACs. Persistence after 12 and 18 months were 75.9% and 69.8%, respectively. Differences between individual DOACs were observed: discontinuation rates ranged from 20.4% (apixaban) to 60.6% (dabigatran), and 12 months persistence from 60.1% (dabigatran) to 85.5% (apixaban). Adherence to treatment with all DOACs was good: overall DOAC median medication refill adherence (MRA) was 102.9% (IQR 88.9% – 115.5%), and 82.3% of patients had an MRA > 80%. Conclusions: In Scotland, adherence to DOAC treatment was good and switching from DOAC to warfarin was low. However, discontinuation and persistence rates were variable – although treatment interruptions were often temporary. To decrease the inconsistencies in drug utilisation methods and facilitate meaningful study comparison, the use of a coherent framework – using a combination of discontinuation, persistence and adherence – and the standardisation of measurements is advocated

Bayesian hierarchical approaches for multiple outcomes in routinely collected healthcare data

Background: Routinely collected healthcare data provides a rich environment for the investigation of drug performance in the general population, while also offering the possibility of assessing rare outcomes. The statistical analysis of this data poses a number of challenges. The data may be biased and lack the structure and balance provided by the drugs’ clinical trials. Outcomes are often modelled individually with an associated lack of control for multiple comparisons, as well as a difficulty in assessing multiple risks. Methods: Bayesian models provide methods for analysing multiple clinical outcomes, using relationships between outcomes and handling the types of multiple comparison issues which may occur when using multiple single-variate approaches. Lack of balance within the data may be catered for by dividing the population into clusters with similar characteristics, allowing within cluster inferences to be made. A Bayesian hierarchical model for multiple outcomes is proposed and applied to data from a safety and effectiveness study of direct oral anticoagulants (DOACs) in Scotland 2009 – 2015. Results: The Bayesian modelling results were comparable to the results from the original safety and effectiveness study, with the additional benefit of balancing patient clusters and controlling for relationships in the data. Conclusion: Bayesian hierarchical models are a suitable approach for modelling routinely collected healthcare data. There is the possibility of moving to an integrated Bayesian approach, with the inclusion of treatment relationships; uncertainty regarding cluster membership; and treatment allocation in the model, eventually leading to more reliable treatment decisions

Imputing missing quality of life data as covariate in survival analysis of the International Breast Cancer Study Group Trials VI and VII

Quality of life (QoL) was an important endpoint in the adjuvant breast cancer trials International Breast Cancer Study Group (IBCSG) Trial VI and VII. Here, QoL was considered as a time-dependent effect. The hypothesis explored is that poorer QoL throughout the trial is associated with poorer disease-free survival (DFS) and vice-versa. Potential bias in the parameter estimates is an important concern associated with missing observations. Standard simple and multiple imputation methods were applied to missing QoL assessments before analysis in a time-dependent Cox model. There was no evidence that the patient's QoL is related to the patient's DFS

Comparative safety and effectiveness of direct oral anticoagulants in patients with atrial fibrillation in clinical practice in Scotland

To compare the clinical effectiveness and safety of direct oral anticoagulants (DOACs) in patients with atrial fibrillation (AF) in routine clinical practice. Retrospective cohort study using linked administrative data. The study population (n=14,577) included patients with a diagnosis of AF (confirmed in hospital) who initiated DOAC treatment in Scotland between August 2011 and December 2015. Multivariate Cox proportional hazard models were used to estimate hazard ratios of thromboembolic events, mortality, and bleeding events. No differences between the DOACs were observed in the risks of stroke, systemic embolism, or cardiovascular death. In contrast, the risk of myocardial infarction was higher among apixaban patients in comparison to rivaroxaban (1.67 [1.02 - 2.71]), and all-cause mortality was higher among rivaroxaban patients in contrast to both apixaban (1.22 [1.01 - 1.47]) and dabigatran (1.55 [1.16 - 2.05]); rivaroxaban patients also had a higher risk of pulmonary embolism than apixaban patients (5.27 [1.79 - 15.53]). The risk of other major bleeds was higher among rivaroxaban patients compared to apixaban (1.50 [1.10 - 2.03]) and dabigatran (1.58 [1.01 - 2.48]); the risks of gastro-intestinal bleeds and overall bleeding were higher among rivaroxaban patients than among apixaban patients (1.48 [1.01 - 2.16] and 1.52[1.21 - 1.92], respectively). All DOACs were similarly effective in preventing strokes and systemic embolisms, while patients being treated with rivaroxaban exhibited the highest bleeding risks. Observed differences in the risks of all-cause mortality, myocardial infarction, and pulmonary embolism warrant further research. [Abstract copyright: This article is protected by copyright. All rights reserved.

Data linkage and statistical modelling to provide stratified risk assessment for HAI

Objectives: The use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. Approach: We demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. Results: In the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. Conclusion: Despite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making

Data linkage and statistical modelling to provide stratified risk assessment for HAI

Objectives: The use of “real-time” data to support individual patient management and outcome assessment requires the development of risk assessment models. This could be delivered through a learning health system by the building robust statistical analysis tools onto the existing linked data held by NHS Scotland’s Infection Intelligence Platform (IIP) and developed within the Scottish Healthcare Associated Infection Prevention Institute (SHAIPI). This project will create prediction models for the risk of acquiring a healthcare associated infection (HAI), and particular outcomes, at the point of GP consultation/ hospital admission which could aid clinical decision making. Approach: We demonstrate the capability using the HAI Clostridium difficile (CDI) from 2010-2013. Using linked national individual level data on community prescribing, hospitalisations, infections and death records we extracted all cases of CDI and by comparing to matched population-based controls, examined the impact of prior hospital admissions, care home residence, comorbidities, exposure to gastric acid suppressive drugs and antibiotic exposure, defined as both cumulative (total defined daily dose (DDD)) and temporal antimicrobial exposure in the previous 6 months, to the risk of CDI acquisition. Antimicrobial exposure was considered for all drugs and the higher risk broad spectrum antibiotics (4Cs). Associations are assessed using conditional logistic regression. Using cross-validation we assess the ability of the model to accurately predict CDI infection. Risk scores for acquisition of CDI are estimated by combining these predictions with age and gender population incidence. Results: In the period 2010-2013 there were 1446 cases of CDI with matched 7964 controls. A significant dose-response relationship for exposure to any antimicrobial (1-7 DDDs OR=2.3 rising to OR=4.4 for 29+ DDDs) and, with elevated risk, to the 4C group (1-7 DDDs OR=3.8 rising to OR=17.9 for 29+ DDDs). Exposure elevates CDI risk most in the month after prescription but for 4C antimicrobials the elevated risk remains 6 months later (4C OR=12.4 within 1 month, OR=2.6 4-6 months later). The risk of CDI was also increased with more co-morbidities, previous hospitalisations, care home residency, increased number of prescriptions, and gastric acid suppression. Conclusion: Despite limitations to current application in practice,(paucity of patient level in-hospital prescribing data and constraints of the timeliness of the data), when fully developed this system will enable risk classification to identify patients most at risk of HAI and adverse outcomes to aid clinical decision making

Cumulative and temporal associations between antimicrobial prescribing and community-associated <i>Clostridium difficile</i> infection:population-based case-control study using administrative data

Background. Community-associated (CA) Clostridium difficile infection (CDI) is a major public health problem. This study estimates the magnitude of the association between temporal and cumulative prescription of antimicrobials in primary care and CA-CDI. CA-CDI is defined as cases without prior hospitalisation in the previous 12 weeks who were either tested outside of hospital or tested within 2 days of admission to hospital. Methods. Three National patient level datasets –covering CDI cases, community prescriptions and hospitalisations were linked by the NHS Scotland unique patient identifier, the community health index, CHI. All validated cases of CDI from August 2010 to July 2013 were extracted and up to six population-based controls were matched to each case from the CHI register for Scotland. Statistical analysis used conditional logistic regression. Results. 1446 unique cases of CA-CDI were linked with 7964 age, sex and location matched controls. Cumulative exposure to any antimicrobial in the previous 6 months has a monotonic dose-response association with CA-CDI. Individuals with excess of 28 defined daily doses (DDD) to any antimicrobial (19.9% of cases) had an odds ratio (OR)=4.4 (95% CI:3.4-5.6) compared to those unexposed. Individuals exposed to 29+ DDD of high risk antimicrobials (cephalosporins, clindamycin co-amoxiclav, or fluoroquinolones) had an OR=17.9 (95% CI:7.6-42.2). Elevated CA-CDI risk following high risk antimicrobial exposure was greatest in the first month (OR=12.5 (8.9-17.4)) but was still present 4-6 months later (OR=2.6 (1.7-3.9)). Cases exposed to 29+DDD had prescription patterns more consistent with repeated therapeutic courses, using different antimicrobials, than long term prophylactic use. Conclusions. This analysis demonstrated temporal and dose-response associations between CA-CDI risk and antimicrobials with an impact of exposure to high risk antimicrobials remaining 4-6 months later

Cost burden of Clostridioides difficile infection to the health service:A retrospective cohort study in Scotland

BACKGROUND:Clostridioides difficile infection (CDI) is associated with high healthcare demands and related costs. AIM:To evaluate the healthcare and economic burden of CDI in hospitalised patients with community- (HOCA-CDI) or hospital-associated CDI (HOHA-CDI) in the National Health Service in Scotland. METHODS:A retrospective cohort study was conducted, examining data between August 2010 and July 2013 from four patient-level Scottish datasets, linked to death data. Data examined included prior antimicrobial prescriptions in the community, hospitalisations, length of stay and mortality. Each CDI case was matched to three hospital-based controls on the basis of age, gender, hospital and date of admission. Descriptive economic evaluations were based on bed-day costs for different types of wards. FINDINGS:Overall, 3304 CDI cases were included in the study. CDI was associated with additional median lengths of stay of 7.2 days for HOCA-CDI and 12.0 days for HOHA-CDI compared with their respective, matched controls. The 30-day mortality rate was 6.8% for HOCA-CDI and 12.4% for HOHA-CDI. Overall, recurrence within 90 days of the first CDI episode occurred in 373/2740 (13.6%) survivors. The median additional expenditure for each initial CDI case compared with matched controls was £1713. In the 6 months after the index hospitalisation, the cost associated with a CDI case was £5126 higher than for controls. CONCLUSION:Using routinely collected national data, we demonstrate the substantial burden of CDI on healthcare services, including lengthy hospital stays and readmissions, which increase the costs of managing patients with CDI compared with matched controls

Data feedback and behavioural change intervention to improve primary care prescribing safety (EFIPPS):multicentre, three arm, cluster randomised controlled trial

Objective: To evaluate the effectiveness of feedback on safety of prescribing compared with moderately enhanced usual care. Design: Three arm, highly pragmatic cluster randomised trial. Setting and participants: 262/278 (94%) primary care practices in three Scottish health boards. Interventions: Practices were randomised to: "usual care," consisting of emailed educational material with support for searching to identify patients (88 practices at baseline, 86 analysed); usual care plus feedback on practice's high risk prescribing sent quarterly on five occasions (87 practices, 86 analysed); or usual care plus the same feedback incorporating a behavioural change component (87 practices, 86 analysed). Main outcome measures: The primary outcome was a patient level composite of six prescribing measures relating to high risk use of antipsychotics, non-steroidal anti-inflammatories, and antiplatelets. Secondary outcomes were the six individual measures. The primary analysis compared high risk prescribing in the two feedback arms against usual care at 15 months. Secondary analyses examined immediate change and change in trend of high risk prescribing associated with implementation of the intervention within each arm. Results: In the primary analysis, high risk prescribing as measured by the primary outcome fell from 6.0% (3332/55 896) to 5.1% (2845/55 872) in the usual care arm, compared with 5.9% (3341/56 194) to 4.6% (2587/56 478) in the feedback only arm (odds ratio 0.88 (95% confidence interval 0.80 to 0.96) compared with usual care; P=0.007) and 6.2% (3634/58 569) to 4.6% (2686/58 582) in the feedback plus behavioural change component arm (0.86 (0.78 to 0.95); P=0.002). In the pre-specified secondary analysis of change in trend within each arm, the usual care educational intervention had no effect on the existing declining trend in high risk prescribing. Both types of feedback were associated with significantly more rapid decline in high risk prescribing after the intervention compared with before. Conclusions: Feedback of prescribing safety data was effective at reducing high risk prescribing. The intervention would be feasible to implement at scale in contexts where electronic health records are in general use