Curator – a data curation tool for clinical real-world evidence

Objective This research aims to establish an efficient, systematic, reproducible, and transparent solution for advanced curation of real-world data, which are highly complex and represent an invaluable source of information for academia and industry. Materials and methods We propose a novel software solution that splits the statistical analytical pipeline into two phases. The first phase is implemented through Curator, which performs data engineering and data modelling on deidentified real-world data to achieve advanced curation and provides selected information ready to be analyzed in the second phase by statistical packages. Curator is made of a suite of Python programs and uses MySQL as its database management system. Curator has been utilised with several UK primary and secondary care data sources. Results Curator has been used in 25 completed clinical and health economics research studies. Their output has been published in 2 NIHR-funded reports and 33 prestigious international peer-reviewed journals and presented at 38 global conferences. Curator has consistently reduced research time and costs by over 36% and made research more reproducible and transparent. Discussion Curator fits in well with recent UK governmental guidelines that recognise health data curation as a complex standalone technical challenge. Curator has been used extensively on UK real-world data and can handle several linked datasets. However, for Curator to be accessed by a wider audience, it needs to become more user-friendly. Conclusion Curator has proven to be a cost-effective and trustworthy data curation tool, which should be developed further and made available to third parties

Risk of hip, subtrochanteric, and femoral shaft fractures among mid and long term users of alendronate: nationwide cohort and nested case-control study

Objectives To determine the skeletal safety and efficacy of long term (≥10 years) alendronate use in patients with osteoporosis. Design Open register based cohort study containing two nested case control studies. Setting Nationwide study of population of Denmark. Participants 61 990 men and women aged 50-94 at the start of treatment, who had not previously taken alendronate, 1996-2007. Interventions Treatment with alendronate. Main outcome measures Incident fracture of the subtrochanteric femur or femoral shaft (ST/FS) or the hip. Non-fracture controls from the cohort were matched to fracture cases by sex, year of birth, and year of initiation of alendronate treatment. Conditional logistic regression models were fitted to calculate odds ratios with and without adjustment for comorbidity and comedications. Sensitivity analyses investigated subsequent treatment with other drugs for osteoporosis. Results 1428 participants sustained a ST/FS (incidence rate 3.4/1000 person years, 95% confidence interval 3.2 to 3.6), and 6784 sustained a hip fracture (16.2/1000 person years, 15.8 to 16.6). The risk of ST/FS was lower with high adherence to treatment with alendronate (medication possession ratio (MPR, a proxy for compliance) >80%) compared with poor adherence (MPR 80% was associated with a decreased risk of hip fracture (0.73, 0.68 to 0.78; P<0.001) as was longer term cumulative use for 5-10 dose years (0.74, 0.67 to 0.83; P<0.001) or ≥10 dose years (0.74, 0.56 to 0.97; P=0.03). Conclusions These findings support an acceptable balance between benefit and risk with treatment with alendronate in terms of fracture outcomes, even for over 10 years of continuous use

CPRD GOLD and linked ONS mortality records: Reconciling guidelines

Background The Clinical Practice Research Datalink (CPRD) GOLD is an extremely influential U.K. primary care dataset for epidemiological research having a number of published papers based on its data much bigger than any other U.K. primary care dataset. The Office for National Statistics (ONS) death data for England can be linked to GOLD at the patient level and are considered the gold standard on mortality. GOLD, which also holds death data, has been recently assessed against ONS linked dataset and the accuracy of its dates of death has been deemed sufficient for the majority of observational studies. However, there is a lack of guidance on how to manage the challenges existing when ONS mortality and GOLD datasets are linked, including linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancy. Objectives Provide reconciling guidelines on how to make maximum and at the same time trustworthy use of mortality information coming from both GOLD and ONS linked datasets with the aim of improving the quality, reproducibility, transparency and comparison of clinical research. Method and results We have developed recommendations on how to manage mortality data coming from both GOLD and linked ONS, taking into account linkage coverage period, linkage correctness likelihood, linkage regional limitations and data discrepancies between these two datasets. We have also implemented these guidelines in an SQL algorithm for researchers to use. Conclusion We have provided detailed guidelines on the reconciliation of mortality data between GOLD and ONS linked death datasets, taking into account both their strengths and limitations. The consistent application of these guidelines made practical by an SQL algorithm, has the potential to improve clinical research quality, reproducibility, transparency and comparison

Riesgo de fractura asociado a los estadios previos al diagnóstico de la diabetes tipo 2 : estudio de caso-control anidado (cohorte DIAFOS)

El objetivo es comparar la prevalencia de fractura en casos incidentes de diabetes y en controles apareados.L'objectiu és comparar la prevalença de fractura en casos incidents de diabetis i controls aparellats

Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis

Introduction: Machine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored. Methods: We used real-world data comparing antihypertensive users to non-users with 69 negative control outcomes, and plasmode simulations to study the performance of ML methods in PS and DRS estimation. We conducted a cohort study using UK primary care records. Further, we conducted a plasmode simulation with synthetic treatment and outcome mimicking empirical data distributions. We compared four PS and DRS estimation methods: 1. Reference: Logistic regression including clinically chosen confounders. 2. Logistic regression with L1 regularisation (LASSO). 3. Multi-layer perceptron (MLP). 4. Extreme Gradient Boosting (XgBoost). Covariate balance, coverage of the null effect of negative control outcomes (real-world data) and bias based on the absolute difference between observed and true effects (for plasmode) were estimated. 632,201 antihypertensive users and nonusers were included. Results: ML methods outperformed the reference method for PS estimation in some scenarios, both in terms of covariate balance and coverage/bias. Specifically, XgBoost achieved the best performance. DRS-based methods performed worse than PS in all tested scenarios. Discussion: We found that ML methods could be reliable alternatives for PS estimation. ML-based DRS methods performed worse than PS ones, likely given the rarity of outcomes

Epidemiologia de la fractura a l'atenció primària de catalunya

Utilitzant una base de dades amb informació clínica extreta de l'historial d'Atenció Primària (AP) a Catalunya, hem portat a terme un estudi de cohorts retrospectiu l'any 2009, incloent individus 50 anys. Hem identificat les fractures osteoporòtiques majors utilitzant codis CIE-10. 2.011.430 individus van ser inclosos. La incidència total va ser de 10'91/1.000 persones-any. La fractura més freqüent entre les dones va ser la d'avantbraç i entre els homes la vertebral simptomàtica. Totes les fractures van augmentar amb l'edat però es van observar diferents patrons segons localització. Aquesta informació és rellevant per la planificació dels serveis d'AP al nostre país

Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP).

BACKGROUND: Computerised databases of primary care clinical records are widely used for epidemiological research. In Catalonia, the Information System for the Development of Research in Primary Care (SIDIAP) aims to promote the development of research based on high-quality validated data from primary care electronic medical records. OBJECTIVE: The purpose of this study is to create and validate a scoring system (Registry Quality Score, RQS) that will enable all primary care practices (PCPs) to be selected as providers of researchusable data based on the completeness of their registers. METHODS: Diseases that were likely to be representative of common diagnoses seen in primary care were selected for RQS calculations. The observed/expected cases ratio was calculated for each disease. Once we had obtained an estimated value for this ratio for each of the selected conditions we added up the ratios calculated for each condition to obtain a final RQS. Rate comparisons between observed and published prevalences of diseases not included in the RQS calculations (atrial fibrillation, diabetes, obesity, schizophrenia, stroke, urinary incontinence and Crohn's disease) were used to set the RQS cutoff which will enable researchers to select PCPs with research-usable data. RESULTS: Apart from Crohn's disease, all prevalences were the same as those published from the RQS fourth quintile (60th percentile) onwards. This RQS cut-off provided a total population of 1 936 443 (39.6% of the total SIDIAP population). CONCLUSIONS: SIDIAP is highly representative of the population of Catalonia in terms of geographical, age and sex distributions. We report the usefulness of rate comparison as a valid method to establish research-usable data within primary care electronic medical records

Opioid use in knee or hip osteoarthritis: a region-wide population-based cohort study

Objective To quantify opioid use in knee and hip osteoarthritis (OA) patients, and to estimate the proportion of opioids in the population attributable to OA patients. Design Population-based cohort study. Methods We included 751579 residents in southern Sweden, aged ≥35 years in 2015. Doctor-diagnosed knee or hip OA between 1998 and 2015 was the exposure. Dispensed weak and strong opioids were identified between November 2013 and October 2015 from the Swedish Prescribed Drug Register (SPDR). We determined age- and sex-standardized 12-month period prevalence of opioid use from November 2014 until October 2015 and calculated prevalence ratios and incidence rate ratios adjusted for age, sex, and other socio-demographic variables. We estimated the population attributable fraction (PAF) of incident opioid use attributable to OA patients. Results The 12-month prevalence of opioid use among OA patients was 23.7% [95% confidence intervals (CI) 23.3–24.2], which was two-fold higher compared to individuals without knee or hip OA: prevalence ratio: 2.1 [95% CI 2.1–2.1]. Similarly, OA patients were more likely to have an incident opioid dispensation, especially for strong opioids (incidence rate ratio: 2.6 [95% CI 2.5–2.7]). population attributable fractions (PAF) of incident opioid use attributable to OA patients was 12%, 9% for weak and 17% for strong opioids. Conclusions Every fourth patient with knee or hip OA has opioids dispensed over a 1-year period, and 12% of incident opioid dispensations are attributable to OA and/or its related comorbidities. These results highlight that patients with knee and hip OA constitute a group of patients with an alarmingly high use of opioids