Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum

Wolf, Achim; Dedman, Daniel; Campbell, Jennifer; Booth, Helen; Lunn, Darren; Chapman, Jennifer; Myles, Puja

doi:10.1093/ije/dyz034

Cited by 518 publications

(591 citation statements)

References 6 publications

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“…This prospective cohort study used a random data sample from the UK Clinical Practice Research Datalink (CPRD), consisting of anonymised electronic records of patients' clinical details taken from English general practices. 13,14 This was a secondary analysis of a CPRD dataset used in a study that investigated the risk of undiagnosed cancer in patients with thrombocytosis. 7 In that study, 40 000 patients with thrombocytosis, defined as a platelet count of 400-999 × 10 9 /l, and recorded from 2000 to 2013, were randomly selected from the CPRD.…”

Section: Methods Selection and Description Of Participantsmentioning

confidence: 99%

Association of non-malignant diseases with thrombocytosis: a prospective cohort study in general practice

et al. 2020

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BackgroundThrombocytosis is an excess of platelets, which is diagnosed as a platelet count >400 × 109/l. An association of thrombocytosis with undiagnosed cancer has recently been established, but the association with non-malignant disease has not been studied in primary care.AimTo examine, in English primary care, the 1-year incidence of non-malignant diseases in patients with new thrombocytosis and the incidence of pre-existing non-malignant diseases in patients who develop new thrombocytosis.Design and settingProspective cohort study using English Clinical Practice Research Datalink data from 2000 to 2013.MethodNewly incident and pre-existing rates of non-malignant diseases associated with thrombocytosis were compared between patients with thrombocytosis and age- and sex-matched patients with a normal platelet count. Fifteen candidate non-malignant diseases were identified from literature searches.ResultsIn the thrombocytosis cohort of 39 850 patients, 4579 (11.5%) were newly diagnosed with any one of the candidate diseases, compared with 443 out of 9684 patients (4.6%) in the normal platelet count cohort (relative risk [RR] 2.5, 95% confidence intervals [CI] = 2.3 to 2.8); iron-deficiency anaemia was the most common new diagnosis (4.5% of patients with thrombocytosis, RR 4.9, 95% CI = 4.0 to 6.1). A total of 22 612 (57.0%) patients with thrombocytosis had a pre-existing non-malignant diagnosis compared with 4846 patients (50%) in the normal platelet count cohort (odds ratio 1.3, 95% CI = 1.2 to 1.4). There was no statistically significant difference in cancer diagnoses between patients with and without pre-existing disease in the thrombocytosis cohort.ConclusionThrombocytosis is associated with several non-malignant diseases. Clinicians can use these findings as part of their holistic diagnostic approach to help guide further investigations and management of patients with thrombocytosis.

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Section: Methods Selection and Description Of Participantsmentioning

confidence: 99%

Association of non-malignant diseases with thrombocytosis: a prospective cohort study in general practice

et al. 2020

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“…When a practice agrees to contribute their patient data to CPRD Aurum, CPRD receives a full historic collection of the coded part of the practice's electronic health records, which includes data on deceased patients and those who have left the practice. The coded clinical record includes symptoms, diagnoses, prescriptions, immunisations, tests, lifestyle factors, and referrals recorded by the general practitioner (GP) or other practice staff but does not include free text medical notes 6 . The November 2019 release of the CPRD Aurum database included a total of 27.5 million patients (including deceased and transferred patients) from 1042 practices of whom 9.7 million were currently registered with a GP 6 .…”

Section: Data and Modellingmentioning

confidence: 99%

“…CPRD is a real-world research service supporting retrospective and prospective public health and clinical studies. It is jointly sponsored by the Medicines and Healthcare products Regulatory Agency and the National Institute for Health Research, as part of the Department of Health and Social Care 6 . Finally, we make conclusions and recommendations about the advantages and disadvantages of using synthetic data for rapid development of AI systems in healthcare.…”

Section: Introductionmentioning

confidence: 99%

Generating high-fidelity synthetic patient data for assessing machine learning healthcare software

Wang²,

et al. 2020

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There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low.

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“…UK primary care electronic health records data (e.g. Clinical Practice Research Datalink) have been shown to be of good quality in terms of representativeness, coverage, validity, and consistency in records of comorbidities and prescriptions [20]. Hospitalisation data used in the study were complete as SUS data captures all hospitalisation information for patients and its recorded outcomes, which has also been proven to have good validity including those experiencing events outside of the CCG catchment [21].…”

Section: Strengths and Limitationsmentioning

confidence: 99%

Cardiovascular risks and bleeding with non-vitamin K antagonist oral anticoagulant versus warfarin in patients with type 2 diabetes: a tapered matching cohort study

Zhao

Simmons

2020

Cardiovasc Diabetol

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Background We compared the risk of bleeding and cardiovascular disease (CVD) events between non-vitamin K antagonist oral anticoagulant (NOAC) and warfarin in people with type 2 diabetes (T2DM). Methods 862 Incident NOAC users and 626 incident warfarin users with T2DM were identified from within 40 UK general practice (1/4/2017–30/9/2018). Outcomes included incident hospitalisation for bleeding, CVD and re-hospitalisation for CVD within 12 months since first anticoagulant prescription, identified from linked hospitalisation data. A tapered matching method was applied to form comparison cohorts: coarsened exact matching restricted the comparison to areas of sufficient overlap in missingness and characteristics: (i) demographic characteristics; (ii) clinical measurements; (iii) prior bleeding and CVD history; (iv) prescriptions with bleeding; (v) anti-hypertensive treatment(s); (vi) anti-diabetes treatment(s). Entropy balancing sequentially balanced NOAC and warfarin users on their distribution of (i–vi). Weighted logistic regression modelling estimated outcome odds ratios (ORs), using entropy balancing weights from steps i–vi. Results The 12-month ORs of bleeding with NOAC (n = 582) vs matched/balanced warfarin (n = 486) were 1.93 (95% confidence interval 0.97–3.84), 2.14 (1.03–4.44), 2.31 (1.10–4.85), 2.42 (1.14–5.14), 2.41 (1.12–5.18), and 2.51 (1.17–5.38) through steps i–vi. ORs for CVD re-hospitalisation was increased with NOAC treatment through steps i–vi: 2.21 (1.04–4.68), 2.13 (1.01–4.52), 2.47 (1.08–5.62), 2.46 (1.02–5.94), 2.51 (1.01–6.20), and 2.66 (1.02–6.94). Conclusions Incident NOAC use among T2DM is associated with increased risk of bleeding hospitalisation and CVD re-hospitalisation compared with incident warfarin use. For T2DM, caution is required in prescribing NOACs as first anticoagulant treatment. Further large-scale replication studies in external datasets are warranted.

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Data resource profile: Clinical Practice Research Datalink (CPRD) Aurum

Cited by 518 publications

References 6 publications

Association of non-malignant diseases with thrombocytosis: a prospective cohort study in general practice

Association of non-malignant diseases with thrombocytosis: a prospective cohort study in general practice

Generating high-fidelity synthetic patient data for assessing machine learning healthcare software

Cardiovascular risks and bleeding with non-vitamin K antagonist oral anticoagulant versus warfarin in patients with type 2 diabetes: a tapered matching cohort study

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