scholarly journals Use of the CANHEART ‘big data’ registry to conduct a large randomized registry clinical trial to improve lipid management in Ontario, Canada

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Anam Khan ◽  
Jing Jia ◽  
Anna Chu ◽  
Jacob Udell ◽  
Michael Farkouh ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Big Data ◽  
Cardiovascular Risk ◽  
Population Based ◽  
Eligibility Criterion ◽  
Lipid Management ◽  
Data Registry ◽  
Cluster Randomized ◽  
Statin Use

IntroductionCreation of registries using linked population-based databases could potentially be used to conduct large registry-based clinical trials. As part of the CANHEART-Strategy for Patient-Oriented Research (SPOR) innovative clinical trials initiative, we explored the practicality of using the linked CANHEART registry to conduct a cluster-randomized trial aimed at improving lipid management. Objectives and ApproachThe CANHEART registry (www.canheart.ca) was created through the linkage of 19 population-based health databases in Ontario, Canada, providing individual-level socio-demographic, geographic, hospitalization, disease testing/screening, mortality, prescription medication, and behavior/lifestyle information. Using CANHEART defined eligibility criterion, small and medium-sized, high cardiovascular-risk health regions (defined as having acute myocardial infarction, stroke or cardiovascular death rates greater than the Ontario average) are being randomly allocated to receive either the intervention (availability of a lipid management ‘toolbox’) or standard care. Cohort linkages to additional years of data will occur regularly over the 3-year trial to ascertain the primary outcome of appropriate statin prescribing rates.   \section*{Results} Record linkage enabled us to determine baseline characteristics of 835,345 patients aged 40-75 as of January 2016, being treated in the 28 study-eligible regions by 2,012 family physicians. Preceding the study, the baseline statin use rate was 35.7\% (in 66-75 year olds) across these regions and the cardiovascular event rate ranged from 3.78-5.64 events/1000 person-years. A randomization procedure yielded 14 regions in both the intervention and control arms which did not differ significantly in socio-demographic characteristics, traditional cardiovascular risk factors, disease history, prevalence of statin use, or access to healthcare indicators. Working groups have been established to operationalize the lipid management tools that will be made available in the intervention regions. Analysis of newly linked participant data will permit outcome ascertainment at trial completion.   \section*{Conclusion/Implications} Our work demonstrates the feasibility of using the CANHEART ‘big data’ registry to conduct a large, cluster-randomized clinical trial aimed at improving lipid management, without requiring any primary data collection. Broader use of this methodology has the potential to change the existing paradigm for conducting pragmatic clinical trial research.

Is the future for clinical trials internet-based? A cluster randomized clinical trial

2005 ◽  
Vol 2 (1) ◽  
pp. 72-79 ◽  
Author(s):  
Jennifer Litchfield ◽  
Jenny Freeman ◽  
Henrik Schou ◽  
Mark Elsley ◽  
Robert Fuller ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trial ◽  
The Future ◽  
Cluster Randomized

scholarly journals Population-Based Linkage of Big Data in Dental Research

2018 ◽  
Vol 15 (11) ◽  
pp. 2357 ◽  
Author(s):  
Tim Joda ◽  
Tuomas Waltimo ◽  
Christiane Pauli-Magnus ◽  
Nicole Probst-Hensch ◽  
Nicola Zitzmann
Keyword(s):  
Clinical Trials ◽  
Big Data ◽  
Healthcare Services ◽  
Population Based ◽  
Services Research ◽  
Research Education ◽  
Data Anonymization ◽  
Dental Research ◽  
Epidemiological Surveys ◽  
Patient Consent

Population-based linkage of patient-level information opens new strategies for dental research to identify unknown correlations of diseases, prognostic factors, novel treatment concepts and evaluate healthcare systems. As clinical trials have become more complex and inefficient, register-based controlled (clinical) trials (RC(C)T) are a promising approach in dental research. RC(C)Ts provide comprehensive information on hard-to-reach populations, allow observations with minimal loss to follow-up, but require large sample sizes with generating high level of external validity. Collecting data is only valuable if this is done systematically according to harmonized and inter-linkable standards involving a universally accepted general patient consent. Secure data anonymization is crucial, but potential re-identification of individuals poses several challenges. Population-based linkage of big data is a game changer for epidemiological surveys in Public Health and will play a predominant role in future dental research by influencing healthcare services, research, education, biotechnology, insurance, social policy and governmental affairs.

scholarly journals Linking big data for cardiovascular health surveillance – opportunities and challenges using the CANHEART cohort

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Anna Chu ◽  
Deirdre Hennessy ◽  
Sharon Johnston ◽  
Jacob Udell ◽  
Dennis Ko ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Big Data ◽  
Risk Factor ◽  
Cardiovascular Risk ◽  
Population Health ◽  
Cardiovascular Risk Factor ◽  
Cardiovascular Health ◽  
Population Based ◽  
Health Surveillance ◽  
Risk Factor Prevalence

IntroductionOur increasing ability to link large population-based health administrative datasets to create ‘big data’ cohorts offers unique opportunities to conduct health and health services surveillance at lower costs than traditional methods using surveys or primary data collection. However, comparability of findings from big data with traditional methods is unknown. Objectives and ApproachIn the CArdiovascular HEalth in Ambulatory Care Research Team (CANHEART) ‘big data’ initiative, we linked 19 population-based health databases to obtain baseline and 5-year follow-up health information on a cohort of 9.8 million adult residents of Ontario, Canada as of January 2008. We compared cardiovascular risk factor prevalence with results from 3500 participants in the 2007-09 Canadian Health Measures Survey (CHMS), a traditional population health surveillance survey. Additionally, we determined cardiovascular preventative care use and clinical event rates by sex and age. Planned linkages to new data sources will enable continued cohort surveillance of population health-related and care indicators. ResultsCholesterol and glucose levels determined from the CANHEART cohort were comparable to the CHMS, whereas blood pressure values and obesity rates were substantially higher. Overall, receipt of cardiovascular preventive care in the CANHEART cohort was high, with 85.7% of males and 91.8% of females having blood pressure assessments, and 67.8% of males and 79.4% of females having weight assessments. Cholesterol and diabetes screening rates among those recommended for screening were over 75%. Incidence of myocardial infarction, stroke or cardiovascular death was 51% higher among males than females (3.8 and 2.5 events per 1000 person-years, respectively). Challenges encountered in analyzing data included treatment of repeated and time-varying measures, selection of valid diagnostic and physician billing codes, changing coding practices and handling of missing and outlying data. Conclusion/ImplicationsComparability of cardiovascular risk factor prevalence using linked administrative data with survey methods varies by indicator. Selection biases amongst survey participants and different measurement methods could explain discrepancies. The added ability to examine health care indicators longitudinally and by subgroup supports use of linked population-based data to enhance health surveillance.

EPID-11. A POPULATION STUDY OF CLINICAL TRIAL ACCRUAL FOR WOMEN AND MINORITIES IN NEURO-ONCOLOGY FOLLOWING THE NIH REVITALIZATION ACT

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi88-vi88
Author(s):  
Sheantel Reihl ◽  
Nirav Patil ◽  
Ramin Morshed ◽  
Mulki Mehari ◽  
alexander Aabedi ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Disease Incidence ◽  
Population Based ◽  
World Health ◽  
Mortality Data ◽  
Incidence And Mortality ◽  
Clinical Trial Accrual ◽  
Expected Mortality ◽  
Study Participants

Abstract INTRODUCTION The NIH Revitalization Act, implemented 29 years ago, set to improve the representation of women and minorities in clinical trials. In this study, we investigate the progress made in neuro-oncology in all phase therapeutic clinical trials for neuro-epithelial central nervous system tumors in comparison to their demographic-specific age-adjusted disease incidence and mortality. METHODS Registry study of all published clinical trials for World Health Organization (WHO) defined neuro-epithelial CNS tumors between January 2000 and December 2019. Study participants for trials were obtained from PubMed and ClinicalTrials.gov. Population-based data from the CBTRUS for incidence analyses. SEER-18 Incidence-Based Mortality data was used for mortality analysis. Descriptive statistics, Fisher exact, and c2 tests were used to analyze the data. RESULTS Among 662 published clinical trial articles representing 49, 907 accrued participants, 62.5% of study participants were men and 37.5% were women (P< 0.0001) representing a mortality specific over-accrual for men (P= 0.001) and under-accrual for women (P= 0.001). Whites, Asians, Blacks, and Hispanics represented 91.7%, 1.5%, 2.6%, and 1.7% of trial participants. Compared with their US cancer mortality, Blacks (47% of expected mortality, P=.008), Hispanics (17% of expected mortality, P< .001) and Asians (33% of expected mortality, P< .001) were underrepresented compared with Whites (114% of expected mortality, P< .001). CONCLUSIONS Nearly 30 years since the Revitalization Act, minorities and women are consistently underrepresented when compared with their demographic-specific incidence and mortality in therapeutic clinical trials for neuroepithelial tumors. This study provides a framework for investigating cancer clinical trial accrual and offers guidance regarding workforce factors associated with enrollment of vulnerable patients.

Survival of Patients with Chronic Myelocytic Leukemia:Comparisons of Estimates From Clinical Trial Settings and Population-Based Cancer Registrtation.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 899-899
Author(s):  
Dianne Pulte ◽  
Adam Gondos ◽  
Teresa Redaniel ◽  
Hermann Brenner
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Stem Cell ◽  
General Population ◽  
Chronic Phase ◽  
Population Based ◽  
Seer Database ◽  
Time Period ◽  
The Difference ◽  
New Treatments

Abstract Abstract 899 Background: The gold standard for determination of the superiority of new treatments is the clinical trial. However, patients in clinical trials tend to be “ideal patients”, i.e. are otherwise healthy except for the condition being examined, have good performance status, may be younger than the average patient with the disease under study, etc, and thus results from clinical trials may not pertain to all patients in the general population. Five year survival for patients with chronic myelocytic leukemic (CML) in recent clinical trials are as high as 95%. However, population based studies of CML show much lower 5-year survival rates. In this study, we compare survival in clinical trials to survival for patients identified from the SEER database as being diagnosed with CML during the same years as the relevant trial was recruiting patients. Methods: We examined survival of patients in randomized controlled trials of treatment of CML between 1980 and 2005 and compared the survival data obtained in these trials to survival of CML patients in the general population of the United States using the Surveillance, Epidemiology, and End Results (SEER) database in the same years as the years of recruitment for the trial. Because age may be a factor in survival, we also calculated age adapted survival for patients in the SEER database for each trial by calculating survival for patients of the same age as the age range given in the trial or for a fifty year interval centered around the median age of patients in the relevant trial. Results: 27 trials were identified for data extraction. Median age on clinical trials varied from 37 to 60, whereas the median age of CML patients in the SEER database was 62. The majority of trials recruited patients with chronic phase only. Two trials of patients in accelerated phase were identified. Five year survival on the clinical trials ranged from 30-40% in the earliest trials to 89% for the first trials of imatinib. Five year survival for patients in the general population over the same time period ranged from 22.2% in 1980-87 to 42.7% in 2000-01. Age adapted survival ranged from 26% to 60%. Overall 5-year survival calculated from the SEER database was uniformly lower than survival in clinical trials in the corresponding time period, although age adapted survival overlapped with survival in clinical trials in some cases (see figure). In general, survival from the SEER database was much lower than survival in the corresponding trial for trials of hematopoietic stem cell transplant or interferon and relatively close to that observed in clinical trials after age adaptation for other treatment types. Discussion: Survival in clinical trials of treatment for CML is higher than survival of patients with CML in the general population. The difference can be attributed to access to newer medications, a bias toward selecting younger, healthier patients for clinical trials, the requirement in most trials that patients be in the chronic phase of the disease, and time necessary for new treatments identified as superior by clinical trials to be adopted by practitioners. In particular, the difference in survival was larger for trials of more difficult to tolerate treatments such as interferon or stem cell transplantation. This finding underscores the need for population based studies to give a more realistic idea of survival of patients with a given malignancy in the general population. The inclusion of a more diverse patient population in clinical trials, including older and less fit patients, may reduce the disparity. Figure legend: Five year survival for patients in clinical trials (squares) and age adapted survival for patients in the SEER database (triangles.) In trials in which survival was different between treatment types, the bold squares represent the higher survival value, the lighter the lower survival. Date on the x-axis represents the middle year of recruitment for the trial. When the middle year of recruitment was the same for more than one trial, the values are staggered for clarity. Disclosures: No relevant conflicts of interest to declare.

scholarly journals 120 Cardiovascular risk profile and diseases in patients with sarcoidosis in the ACALM big data registry

2019 ◽  
Author(s):  
Satpal Ubhi ◽  
Paul Carter ◽  
Chris Miller ◽  
Ranjit More ◽  
Shajil Chalil ◽  
...  
Keyword(s):  
Big Data ◽  
Cardiovascular Risk ◽  
Risk Profile ◽  
Cardiovascular Risk Profile ◽  
Data Registry

Controversies Regarding Lipid Management and Statin Use for Cardiovascular Risk Reduction in Patients With CKD

2016 ◽  
Vol 67 (6) ◽  
pp. 965-977 ◽  
Author(s):  
Talar Markossian ◽  
Nicholas Burge ◽  
Benjamin Ling ◽  
Julia Schneider ◽  
Ivan Pacold ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Risk Reduction ◽  
Cardiovascular Risk Reduction ◽  
Lipid Management ◽  
Statin Use

scholarly journals ETHICAL ASPECTS OF THE USE OF INNOVATIVE INFORMATION TECHNOLOGIES IN CLINICAL TRIALS

2020 ◽  
Vol 1 ◽  
pp. 66-70
Author(s):  
Nikoleta Leventi ◽  
Alexandrina Vodenitcharova ◽  
Kristina Popova
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Big Data ◽  
Data Analytics ◽  
Information Technologies ◽  
Social Issues ◽  
Big Data Analytics ◽  
Healthcare Management ◽  
Ethical Aspects

A clinical trial, according to the WHO, “is any research study that prospectively assigns human participants or groups of humans to one or more health-related interventions to evaluate the effects on health outcomes. Interventions include but are not restricted to drugs, cells and other biological products, surgical procedures, radiological procedures, devices, behavioural treatments, process-of-care changes, preventive care, etc”. The application of innovative information technologies like artificial intelligence and big data analytics in clinical trial processes is a new challenge. Such systems are useful tools, and promise to enhance the healthcare management, and to optimize clinical outcomes and economic effectiveness. However, their use raises ethical and social issues. In this direction, the European Commission in June 2018 set up the High Level Expert Group on AI, which offers guidance on a comprehensive framework for trustworthy AI. Trustworthy AI consists of three components, which should be met during the entire life cycle of the system: (1) it should be lawful, (2) it should be ethical, and (3) it should be robust. In this article we used the focus group methodology to obtain information from experts about the ethical aspects raised when innovative information technologies like artificial intelligence and big data analytics are used in clinical trials. Feedback from the experts was also gathered regarding the usage of the proposed guidelines for trustworthy AI, as an evaluation tool for the particular case of clinical trials.

scholarly journals Alternative clinical trial designs

2020 ◽  
Vol 5 (1) ◽  
pp. e000420
Author(s):  
John A Harvin ◽  
Ben L Zarzaur ◽  
Raminder Nirula ◽  
Benjamin T King ◽  
Ajai K Malhotra
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Randomized Clinical Trials ◽  
Trauma Centers ◽  
Cluster Randomization ◽  
Stepped Wedge ◽  
Pragmatic Trials ◽  
Clinical Trial Designs ◽  
Cluster Randomized ◽  
Injured Patients

High-quality clinical trials are needed to advance the care of injured patients. Traditional randomized clinical trials in trauma have challenges in generating new knowledge due to many issues, including logistical difficulties performing individual randomization, unclear pretrial estimates of treatment effect leading to often unpowered studies, and difficulty assessing the generalizability of an intervention given the heterogeneity of both patients and trauma centers. In this review, we discuss alternative clinical trial designs that can address some of these difficulties. These include pragmatic trials, cluster randomization, cluster randomized stepped wedge designs, factorial trials, and adaptive designs. Additionally, we discuss how Bayesian methods of inference may provide more knowledge to trauma and acute care surgeons compared with traditional, frequentist methods.

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