Clinical Practice Research Datalink

Background: The predictable localization of the neurovascular supply along the ligamentous suspension, composed of the horizontal septum, vertical ligaments, and superficial fascia, has increased safety in breast reduction. Lymphatic drainage of the breast has always been described as running close to vascular supply. However, the correlation between the lymphatic course and ligamentous suspension has not yet been considered. This study aimed to visualize the relationship, direction of lymphatic flow, and predefined separation of lobes along the horizontal septum. Methods: To investigate central drainage, methylene blue was injected subareolarly in five breasts of female cadavers before blunt preparation of the horizontal septum in anatomical studies. To visualize central and peripheral drainage, lymphographin was injected into one of the three different sites in 14 breasts; the dynamic flow of drainage was observed during subsequent septum-based breast reductions in clinical settings. Results: In all anatomical studies, a predefined section of the glandular layers allowed access to central clearance along the stained horizontal septum. Clinical investigations similarly showed clearance along the corresponding part of the ligamentous suspension, most reliably along the horizontal septum. The affected quadrant of the breast, its relation to the nipple-areola complex, and the anterior-posterior axis toward the thoracic wall mainly determine the direction of lymphatic flow. Interconnections along the ligaments may explain the unpredictability of final clearance directions. Conclusions: This study shows the horizontal septum as a guiding structure for central mammary drainage. This may encourage a septum-based approach for refinement of procedures such as oncoplastic, irradiation, and lymphedema treatments.

Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine healthdata. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Patient and public involvement and engagement (PPIE) can provide valuable insights into the experiences of those living with and affected by a disease or health condition. Inclusive collaboration between patients, the public and researchers can lead to productive relationships, ensuring that health research addresses patient needs. Guidelines are available to support effective PPIE; however, evaluation of the impact of PPIE strategies in health research is limited. In this Review, we evaluate the impact of PPIE in the 'Therapies for Long COVID in non-hospitalised individuals' (TLC) Study, using a combination of group discussions and interviews with patient partners and researchers. We identify areas of good practice and reflect on areas for improvement. Using these insights and the results of a survey, we synthesize two checklists of considerations for PPIE, and we propose that research teams use these checklists to optimize the impact of PPIE for both patients and researchers in future studies.

RationaleClinical trials are the gold standard for testing interventions. COVID-19 has further raised their public profile and emphasised the need to deliver better, faster, more efficient trials for patient benefit. Considerable overlap exists between data required for trials and data already collected routinely in electronic healthcare records (EHRs). Opportunities exist to use these in innovative ways to decrease duplication of effort and speed trial recruitment, conduct and follow-up.ApproachThe National Institute of Health Research (NIHR), Health Data Research UK and Clinical Practice Research Datalink co-organised a national workshop to accelerate the agenda for ‘data-enabled clinical trials’. Showcasing successful examples and imagining future possibilities, the plenary talks, panel discussions, group discussions and case studies covered: design/feasibility; recruitment; conduct/follow-up; collecting benefits/harms; and analysis/interpretation.ReflectionSome notable studies have successfully accessed and used EHR to identify potential recruits, support randomised trials, deliver interventions and supplement/replace trial-specific follow-up. Some outcome measures are already reliably collected; others, like safety, need detailed work to meet regulatory reporting requirements. There is a clear need for system interoperability and a ‘route map’ to identify and access the necessary datasets. Researchers running regulatory-facing trials must carefully consider how data quality and integrity would be assessed. An experience-sharing forum could stimulate wider adoption of EHR-based methods in trial design and execution.DiscussionEHR offer opportunities to better plan clinical trials, assess patients and capture data more efficiently, reducing research waste and increasing focus on each trial’s specific challenges. The short-term emphasis should be on facilitating patient recruitment and for postmarketing authorisation trials where research-relevant outcome measures are readily collectable. Sharing of case studies is encouraged. The workshop directly informed NIHR’s funding call for ambitious data-enabled trials at scale. There is the opportunity for the UK to build upon existing data science capabilities to identify, recruit and monitor patients in trials at scale.

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

ObjectivesTo describe the benefits and limitations of using individual and combinations of linked English electronic health data to identify incident cancers.Design and settingOur descriptive study uses linked English Clinical Practice Research Datalink primary care; cancer registration; hospitalisation and death registration data.Participants and measuresWe implemented case definitions to identify first site-specific cancers at the 20 most common sites, based on the first ever cancer diagnosis recorded in each individual or commonly used combination of data sources between 2000 and 2014. We calculated positive predictive values and sensitivities of each definition, compared with a gold standard algorithm that used information from all linked data sets to identify first cancers. We described completeness of grade and stage information in the cancer registration data set.Results165 953 gold standard cancers were identified. Positive predictive values of all case definitions were ≥80% and ≥94% for the four most common cancers (breast, lung, colorectal and prostate). Sensitivity for case definitions that used cancer registration alone or in combination was ≥92% for the four most common cancers and ≥80% across all cancer sites except bladder cancer (65% using cancer registration alone). For case definitions using linked primary care, hospitalisation and death registration data, sensitivity was ≥89% for the four most common cancers, and ≥80% for all cancer sites except kidney (69%), oral cavity (76%) and ovarian cancer (78%). When primary care or hospitalisation data were used alone, sensitivities were generally lower and diagnosis dates were delayed. Completeness of staging data in cancer registration data was high from 2012 (minimum 76.0% in 2012 and 86.4% in 2014 for the four most common cancers).ConclusionsAscertainment of incident cancers was good when using cancer registration data alone or in combination with other data sets, and for the majority of cancers when using a combination of primary care, hospitalisation and death registration data.

BackgroundSince 2010 the National Institute for Health and Care Excellence has recommended screening adults for excessive alcohol consumption to try and help prevent alcohol-use disorders. Little is known about the extent to which these recommendations are followed, and the resulting completeness and validity of alcohol-related data recording in primary care.ObjectiveTo investigate the completeness and accuracy of recording of alcohol use within primary care records in the UK.Design and settingCross-sectional study in the Clinical Practice Research Datalink.ParticipantsWe included all adult patients registered on 1st January 2018 with ≥1 year of follow-up.Primary and secondary outcome measuresWe calculated prevalence of alcohol consumption recording overall and within patient groups. We then validated alcohol consumption data against recommended screening tools (Alcohol Use Disorders Identification Test (AUDIT)) as the gold standard. We also calculated how prevalence of alcohol recording changed over the preceding decade.ResultsIn 2018, among 1.8 million registered adult patients, just over half (51.9%) had a record for a code related to alcohol in the previous 5 years. Recording of alcohol consumption was more common among women, older people, ex-smokers and those from more deprived areas, who were overweight/obese, or with comorbidities. A quarter of patients had units per week recorded in the last 5 years, but <10% had an AUDIT or Fast Alcohol Screening Test (FAST) alcohol screening test score. The recorded alcohol measures corresponded to results from gold standard AUDIT scores. The distribution of consumption among current drinkers was similar to the Health Survey for England.ConclusionsHalf of adults in UK primary care have no recorded alcohol consumption data. When consumption is recorded, we have demonstrated internal and external validity of the data, suggesting greater recording may help identify opportunities for interventions to reduce harms.

1564 Background: Breast cancer survivors are the largest group of cancer survivors in the United Kingdom (UK). Having had a breast cancer diagnosis may adversely affect the patient’s mental health. We aimed to estimate the long-term risk of anxiety and depression in women with history of breast cancer compared to those who have never had cancer. Methods: We conducted a matched population-based cohort study, using data from the Clinical Practice Research Datalink (CPRD) GOLD primary care database. The exposed cohort included all adult women diagnosed with breast cancer between 1987 and 2018; the unexposed group included women with no cancer history, matched to exposed women in a 4:1 ratio on primary care practice and age. Cox regression models stratified on matched set were used to estimate hazard ratios of the association between breast cancer survivorship and anxiety and depression. Results: 59,972 women (mean 62 years; standard deviation (SD) 14.0) had history of breast cancer. The median follow-up time was 3.0 years (SD 4.4), which amounted to 256,186 person-years under observation. The comparison group included 240,387 women followed up over 3.5 years (SD 4.5) (1,163,819 person-years). The incidence of anxiety in breast cancer survivors was 0.08 (95% confidence interval (95%) 0.07-0.08) per 1000 person-years, and the incidence of depression was 70 (95%CI 68-71) per 1000 person-years. The risks of both depression and anxiety were raised in breast cancer survivors compared with controls, and this appeared to be driven by the first 3 years following diagnosis (Table). Conclusions: Breast cancer survivors in the UK had significantly higher risk anxiety and depression diagnosed in primary care for three years following diagnosis than women who never had cancer. Risk of anxiety and depression in breast cancer survivors compared to women who did not have cancer by time since diagnosis. [Table: see text]

Clinical Practice Research Datalink (CPRD) is a UK government, not-for-profit research service that has been supplying anonymized primary care data for public health research for more than 30 years. In October 2017 CPRD launched a new data resource called CPRD Aurum. CPRD Aurum is a database containing routinely collected data from primary care practices in England, capturing diagnoses, symptoms, prescriptions, referrals and tests for over 19 million patients as of September 2018 (Figure Primary care data in CPRD Aurum have been linked to national secondary care databases as well as deprivation and death registration data (Table