Personalised medicine, new discoveries and studies on rare exposures or outcomes require large samples that are increasingly difficult for any single investigator to obtain. Collaborative work is limited by heterogeneities, both what is being collected and how it is defined. To develop a core set for data collection in rheumatoid arthritis (RA) research which (1) allows harmonisation of data collection in future observational studies, (2) acts as a common data model against which existing databases can be mapped and (3) serves as a template for standardised data collection in routine clinical practice to support generation of research-quality data. A multistep, international multistakeholder consensus process was carried out involving voting via online surveys and two face-to-face meetings. A core set of 21 items ('what to collect') and their instruments ('how to collect') was agreed: age, gender, disease duration, diagnosis of RA, body mass index, smoking, swollen/tender joints, patient/evaluator global, pain, quality of life, function, composite scores, acute phase reactants, serology, structural damage, treatment and comorbidities. The core set should facilitate collaborative research, allow for comparisons across studies and harmonise future data from clinical practice via electronic medical record systems.
- Keywords
- outcomes research, quality indicators, rheumatoid arthritis,
- MeSH
- Consensus MeSH
- Humans MeSH
- Observational Studies as Topic methods standards MeSH
- Arthritis, Rheumatoid * MeSH
- Data Collection methods standards MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Consensus Development Conference MeSH
- Research Support, Non-U.S. Gov't MeSH
OBJECTIVES: We aimed to compare various methods for imputing disease activity in longitudinally collected observational data of patients with axial spondyloarthritis (axSpA). METHODS: We conducted a simulation study on data from 8583 axSpA patients from ten European registries. Disease activity was assessed by the Axial Spondyloarthritis Disease Activity Score (ASDAS) and the corresponding low disease activity (LDA; ASDAS<2.1) state at baseline, 6 and 12 months. We focused on cross-sectional methods which impute missing values of an individual at a particular time point based on the available information from other individuals at that time point. We applied nine single and five multiple imputation methods, covering mean, regression and hot deck methods. The performance of each imputation method was evaluated via relative bias and coverage of 95% confidence intervals for the mean ASDAS and the derived proportion of patients in LDA. RESULTS: Hot deck imputation methods outperformed mean and regression methods, particularly when assessing LDA. Multiple imputation procedures provided better coverage than the corresponding single imputation ones. However, none of the evaluated methods produced unbiased estimates with adequate coverage across all time points, with performance for missing baseline data being worse than for missing follow-up data. Predictive mean and weighted predictive mean hot deck imputation procedures consistently provided results with low bias. CONCLUSIONS: This study contributes to the available methods for imputing disease activity in observational research. Hot deck imputation using predictive mean matching exhibited the highest robustness and is thus our suggested approach.
- Keywords
- Axial Spondyloarthritis, Epidemiology, Interleukin-17, Tumour Necrosis Factor Inhibitors,
- MeSH
- Axial Spondyloarthritis * diagnosis epidemiology MeSH
- Adult MeSH
- Data Interpretation, Statistical MeSH
- Humans MeSH
- Longitudinal Studies MeSH
- Observational Studies as Topic * MeSH
- Cross-Sectional Studies MeSH
- Registries MeSH
- Spondylarthritis diagnosis MeSH
- Severity of Illness Index * MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe epidemiology MeSH
OBJECTIVE: Health data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful. MATERIALS AND METHODS: Twenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research. RESULTS: The health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study. DISCUSSION: This study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time. CONCLUSION: This study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence.
- Keywords
- OMOP common data model, data standardization, observational data,
- MeSH
- Global Health * MeSH
- Databases, Factual MeSH
- Electronic Health Records MeSH
- Medicine * MeSH
- Humans MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Observational Study MeSH
- Geographicals
- Europe MeSH
Background Conducting randomized controlled trials to investigate survival in a rare disease like pulmonary arterial hypertension has considerable ethical and logistical constraints. In many studies, such as the Study with an Endothelin Receptor Antagonist in Pulmonary Arterial Hypertension to Improve Clinical Outcome (SERAPHIN) randomized controlled trial, evaluating survival is further complicated by bias introduced by allowing active therapy among placebo-treated patients who clinically deteriorate. Methods and Results SERAPHIN enrolled and followed patients in the same time frame as the US Registry to Evaluate Early And Long-term PAH Disease Management, providing an opportunity to compare observed survival for SERAPHIN patients with predicted survival had they received real-world treatment as in the Registry to Evaluate Early And Long-term PAH Disease Management. From the Registry to Evaluate Early And Long-term PAH Disease Management (N=3515), 734 patients who met SERAPHIN eligibility criteria were selected and their data used to build a prediction model for time to death up to 3 years based on 10 baseline prognostic variables. The model was used to predict a survival curve for each of the 742 SERAPHIN patients via their baseline variables. The average of these predicted survival curves was compared with observed survival of the placebo (n=250) and macitentan 10 mg (n=242) groups using a log-rank test and Cox proportional hazard model. Observed mortality risk for patients randomized to placebo, 62% of whom were taking background pulmonary arterial hypertension therapy, tended to be lower than that predicted for all SERAPHIN patients (16% lower; P=0.259). The observed placebo survival curve closely approximated the predicted survival curve for the first 15 months. Beyond that time, observed risk of mortality decreased compared with predicted mortality, potentially reflecting the impact of crossover of patients in the placebo group to active therapy. Over 3 years, risk of mortality observed with macitentan 10 mg was 35% lower than predicted mortality ( P=0.010). Conclusions These analyses show that, in a rare disease, real-world observational data can complement randomized controlled trial data to overcome some challenges associated with assessing survival in the setting of a randomized controlled trial. Clinical Trial Registration https://www.clinicaltrials.gov . Unique identifiers: NCT00660179 and NCT00370214.
- Keywords
- hypertension, pulmonary, macitentan, prognosis, rare diseases, survival,
- MeSH
- Endothelin Receptor Antagonists adverse effects therapeutic use MeSH
- Antihypertensive Agents adverse effects therapeutic use MeSH
- Time Factors MeSH
- Adult MeSH
- Risk Assessment MeSH
- Clinical Trials, Phase III as Topic MeSH
- Middle Aged MeSH
- Humans MeSH
- Evidence-Based Medicine MeSH
- Multicenter Studies as Topic MeSH
- Pulmonary Arterial Hypertension diagnosis drug therapy mortality MeSH
- Observational Studies as Topic MeSH
- Cause of Death MeSH
- Disease Progression MeSH
- Randomized Controlled Trials as Topic MeSH
- Risk Factors MeSH
- Aged MeSH
- Treatment Outcome MeSH
- Rare Diseases diagnosis drug therapy mortality MeSH
- Check Tag
- Adult MeSH
- Middle Aged MeSH
- Humans MeSH
- Male MeSH
- Aged MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Names of Substances
- Endothelin Receptor Antagonists MeSH
- Antihypertensive Agents MeSH
INTRODUCTION: In ageing societies, the number of older adults with complex chronic conditions (CCCs) is rapidly increasing. Care for older persons with CCCs is challenging, due to interactions between multiple conditions and their treatments. In home care and nursing homes, where most older persons with CCCs receive care, professionals often lack appropriate decision support suitable and sufficient to address the medical and functional complexity of persons with CCCs. This EU-funded project aims to develop decision support systems using high-quality, internationally standardised, routine care data to support better prognostication of health trajectories and treatment impact among older persons with CCCs. METHODS AND ANALYSIS: Real-world data from older persons aged ≥60 years in home care and nursing homes, based on routinely performed comprehensive geriatric assessments using interRAI systems collected in the past 20 years, will be linked with administrative repositories on mortality and care use. These include potentially up to 51 million care recipients from eight countries: Italy, the Netherlands, Finland, Belgium, Canada, USA, Hong Kong and New Zealand. Prognostic algorithms will be developed and validated to better predict various health outcomes. In addition, the modifying impact of pharmacological and non-pharmacological interventions will be examined. A variety of analytical methods will be used, including techniques from the field of artificial intelligence such as machine learning. Based on the results, decision support tools will be developed and pilot tested among health professionals working in home care and nursing homes. ETHICS AND DISSEMINATION: The study was approved by authorised medical ethical committees in each of the participating countries, and will comply with both local and EU legislation. Study findings will be shared with relevant stakeholders, including publications in peer-reviewed journals and presentations at national and international meetings.
- Keywords
- decision making, epidemiology, geriatric medicine, health services administration & management,
- MeSH
- Algorithms MeSH
- Chronic Disease MeSH
- Humans MeSH
- Observational Studies as Topic MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Home Care Services * MeSH
- Aging MeSH
- Artificial Intelligence * MeSH
- Check Tag
- Humans MeSH
- Aged, 80 and over MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Roundabouts are one of the safest types of intersections. However, the needs to meet the requirements of operation, capacity, traffic organization and surrounding development lead to a variety of design solutions. One of such alternatives are turbo-roundabouts, which simplify drivers' decision making, limit lane changing in the roundabout, and induce low driving speed thanks to raised lane dividers. However, in spite of their generally positive reception, the safety impact of turbo-roundabouts has not been sufficiently studied. Given the low number of existing turbo-roundabouts and the statistical rarity of accident occurrence, the prevalent previously conducted studies applied only simple before-after designs or relied on traffic conflicts in micro-simulations. Nevertheless, the presence of raised lane dividers is acknowledged as an important feature of well performing and safe turbo-roundabouts. Following the previous Polish studies, the primary objective of the present study was assessment of influence of presence of lane dividers on road safety and developing a reliable and valid surrogate safety measure based on field data, which will circumvent the limitations of accident data or micro-simulations. The secondary objective was using the developed surrogate safety measure to assess and compare the safety levels of Polish turbo-roundabout samples with and without raised lane dividers. The surrogate safety measure was based on speed and lane behaviour. Speed was obtained from video observations and floating car data, which enabled the construction of representative speed profiles. Lane behaviour data was gathered from video observations. The collection of the data allowed for a relative validation of the method by comparing the safety performance of turbo-roundabouts with and without raised lane dividers. In the end, the surrogate measure was applied for evaluation of safety levels and enhancement of the existing safety performance functions, which combine traffic volumes, and speeds as a function of radii). The final models may help quantify the safety impact of different turbo-roundabout solutions.
- Keywords
- Floating car data, Lane divider, Speed, Surrogate safety measure, Turbo-Roundabout, Video observation,
- MeSH
- Video Recording MeSH
- Safety MeSH
- Accidents, Traffic prevention & control MeSH
- Risk Assessment MeSH
- Humans MeSH
- Automobile Driving statistics & numerical data MeSH
- Data Collection methods MeSH
- Built Environment * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: A minority of European countries have participated in international comparisons with high level data on lung cancer. However, the nature and extent of data collection across the continent is simply unknown, and without accurate data collection it is not possible to compare practice and set benchmarks to which lung cancer services can aspire. METHODS: Using an established network of lung cancer specialists in 37 European countries, a survey was distributed in December 2014. The results relate to current practice in each country at the time, early 2015. The results were compiled and then verified with co-authors over the following months. RESULTS: Thirty-five completed surveys were received which describe a range of current practice for lung cancer data collection. Thirty countries have data collection at the national level, but this is not so in Albania, Bosnia-Herzegovina, Italy, Spain and Switzerland. Data collection varied from paper records with no survival analysis, to well-established electronic databases with links to census data and survival analyses. CONCLUSION: Using a network of committed clinicians, we have gathered validated comparative data reporting an observed difference in data collection mechanisms across Europe. We have identified the need to develop a well-designed dataset, whilst acknowledging what is feasible within each country, and aspiring to collect high quality data for clinical research.
- Keywords
- Audit, Data collection, Datasets, Epidemiology, Lung Cancer,
- MeSH
- Databases, Factual statistics & numerical data MeSH
- Medical Oncology methods statistics & numerical data MeSH
- Humans MeSH
- Lung Neoplasms diagnosis therapy MeSH
- Data Collection methods statistics & numerical data MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Europe MeSH
The Minimum Redundancy Maximum Relevance (MRMR) approach to supervised variable selection represents a successful methodology for dimensionality reduction, which is suitable for high-dimensional data observed in two or more different groups. Various available versions of the MRMR approach have been designed to search for variables with the largest relevance for a classification task while controlling for redundancy of the selected set of variables. However, usual relevance and redundancy criteria have the disadvantages of being too sensitive to the presence of outlying measurements and/or being inefficient. We propose a novel approach called Minimum Regularized Redundancy Maximum Robust Relevance (MRRMRR), suitable for noisy high-dimensional data observed in two groups. It combines principles of regularization and robust statistics. Particularly, redundancy is measured by a new regularized version of the coefficient of multiple correlation and relevance is measured by a highly robust correlation coefficient based on the least weighted squares regression with data-adaptive weights. We compare various dimensionality reduction methods on three real data sets. To investigate the influence of noise or outliers on the data, we perform the computations also for data artificially contaminated by severe noise of various forms. The experimental results confirm the robustness of the method with respect to outliers.
In 2011 and 2012, the COPHES/DEMOCOPHES twin projects performed the first ever harmonized human biomonitoring survey in 17 European countries. In more than 1800 mother-child pairs, individual lifestyle data were collected and cadmium, cotinine and certain phthalate metabolites were measured in urine. Total mercury was determined in hair samples. While the main goal of the COPHES/DEMOCOPHES twin projects was to develop and test harmonized protocols and procedures, the goal of the current paper is to investigate whether the observed differences in biomarker values among the countries implementing DEMOCOPHES can be interpreted using information from external databases on environmental quality and lifestyle. In general, 13 countries having implemented DEMOCOPHES provided high-quality data from external sources that were relevant for interpretation purposes. However, some data were not available for reporting or were not in line with predefined specifications. Therefore, only part of the external information could be included in the statistical analyses. Nonetheless, there was a highly significant correlation between national levels of fish consumption and mercury in hair, the strength of antismoking legislation was significantly related to urinary cotinine levels, and we were able to show indications that also urinary cadmium levels were associated with environmental quality and food quality. These results again show the potential of biomonitoring data to provide added value for (the evaluation of) evidence-informed policy making.
- Keywords
- COPHES, DEMOCOPHES, External exposure data, Human biomonitoring, Interpretation,
- MeSH
- Biomarkers analysis urine MeSH
- Child MeSH
- Adult MeSH
- Data Interpretation, Statistical MeSH
- Cadmium analysis urine MeSH
- Cotinine urine MeSH
- Smoking legislation & jurisprudence urine MeSH
- Environmental Pollutants analysis urine MeSH
- Humans MeSH
- Urban Population statistics & numerical data MeSH
- Environmental Monitoring methods statistics & numerical data MeSH
- Seafood statistics & numerical data MeSH
- Surveys and Questionnaires standards MeSH
- Mercury analysis urine MeSH
- Rural Population statistics & numerical data MeSH
- Government Regulation MeSH
- Hair chemistry MeSH
- Environmental Exposure analysis statistics & numerical data MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
- Names of Substances
- Biomarkers MeSH
- Cadmium MeSH
- Cotinine MeSH
- Environmental Pollutants MeSH
- Mercury MeSH
As the world population ages, there will be an increasing need for effective therapies for aging-associated neurodegenerative disorders, which remain untreatable. Dementia due to Alzheimer's disease (AD) is one of the leading neurological diseases in the aging population. Current therapeutic approaches to treat this disorder are solely symptomatic, making the need for new molecular entities acting on the causes of the disease extremely urgent. One of the potential solutions is to use compounds that are already in the market. The structures have known pharmacokinetics, pharmacodynamics, toxicity profiles, and patient data available in several countries. Several drugs have been used successfully to treat diseases different from their original purposes, such as autoimmunity and peripheral inflammation. Herein, we divulge the repurposing of drugs in the area of neurodegenerative diseases, focusing on the therapeutic potential of antineoplastics to treat dementia due to AD and dementia. We briefly touch upon the shared pathological mechanism between AD and cancer and drug repurposing strategies, with a focus on artificial intelligence. Next, we bring out the current status of research on the development of drugs, provide supporting evidence from retrospective, clinical, and preclinical studies on antineoplastic use, and bring in new areas, such as repurposing drugs for the prion-like spreading of pathologies in treating AD.
- Keywords
- Alzheimer's disease, aging, antineoplastic, dementia, drug repurposing, neurodegenerative diseases,
- MeSH
- Alzheimer Disease * drug therapy MeSH
- Dementia * drug therapy MeSH
- Humans MeSH
- Observational Studies as Topic MeSH
- Drug Repositioning * MeSH
- Drug Evaluation, Preclinical MeSH
- Antineoplastic Agents * pharmacology therapeutic use chemistry MeSH
- Animals MeSH
- Check Tag
- Humans MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
- Names of Substances
- Antineoplastic Agents * MeSH
