Assessing gaps in cholesterol treatment guidelines for primary prevention of cardiovascular disease based on available randomised clinical trial evidence: The Rotterdam Study

Background The purpose of this study was to determine how American College of Cardiology/American Heart Association (ACC/AHA) 2013 and European Society of Cardiology 2016 guidelines for the primary prevention of atherosclerotic cardiovascular disease (CVD) compare in reflecting the totality of accrued randomised clinical trial evidence for statin treatment at population level. Methods From 1997–2008, 7279 participants aged 45–75 years, free of atherosclerotic cardiovascular disease, from the population-based Rotterdam Study were included. For each participant, we compared eligibility for each one of 11 randomised clinical trials on statin use in primary prevention of CVD, with recommendations on lipid-lowering therapy from the ACC/AHA and European Society of Cardiology (ESC) guidelines. Atherosclerotic cardiovascular disease incidence and cardiovascular disease mortality rates were calculated. Results The proportion of participants eligible for each trial ranged from 0.4% for ALLHAT-LLT to 30.8% for MEGA. The likelihood of being recommended for lipid-lowering treatment was lowest for those eligible for low-to-intermediate risk RCTs (HOPE-3, MEGA, and JUPITER), and highest for high-risk individuals with diabetes (MRC/BHF HPS, CARDS, and ASPEN) or elderly PROSPER. Eligibility for an increasing number of randomised clinical trials correlated with a greater likelihood of being recommended lipid-lowering treatment by either guideline ( p < 0.001 for both guidelines). Conclusion Compared to RCTs done in high risk populations, randomised clinical trials targeting low-to-intermediate risk populations are less well-reflected in the ACC/AHA, and even less so in the ESC guideline recommendations. Importantly, the low-to-intermediate risk population targeted by HOPE-3, the most recent randomised clinical trial in this field, is not well-captured by the current European prevention guidelines and should be specifically considered in future iterations of the guidelines.

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Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

10.21203/rs.3.rs-35669/v3 ◽

2020 ◽

Author(s):

Ravinder Claire ◽

Christian Gluud ◽

Ivan Berlin ◽

Tim Coleman ◽

Jo Leonardi-Bee

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Sample Size ◽

Sequential Analysis ◽

Randomised Clinical Trial ◽

Randomised Clinical Trials ◽

Trial Sequential Analysis ◽

Analysis Software ◽

Cessation Intervention ◽

Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.MethodsWe describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects.ResultsWe provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. ConclusionsTrial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

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Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

BMC Medical Research Methodology ◽

10.1186/s12874-020-01169-7 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Ravinder Claire ◽

Christian Gluud ◽

Ivan Berlin ◽

Tim Coleman ◽

Jo Leonardi-Bee

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Sample Size ◽

Sequential Analysis ◽

Randomised Clinical Trial ◽

Randomised Clinical Trials ◽

Trial Sequential Analysis ◽

Analysis Software ◽

Cessation Intervention ◽

Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

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Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

10.21203/rs.3.rs-35669/v2 ◽

2020 ◽

Author(s):

Ravinder Claire ◽

Christian Gluud ◽

Ivan Berlin ◽

Tim Coleman ◽

Jo Leonardi-Bee

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Sample Size ◽

Sequential Analysis ◽

Randomised Clinical Trial ◽

Randomised Clinical Trials ◽

Trial Sequential Analysis ◽

Analysis Software ◽

Cessation Intervention ◽

Pilot Trials

Abstract Background: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms.Methods: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention's effects. Results: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. Conclusions: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

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3098Assessing gaps in cholesterol treatment guidelines for primary prevention of cardiovascular disease using clinical trial evidence: results from the prospective population-based Rotterdam Study

European Heart Journal ◽

10.1093/eurheartj/ehx504.3098 ◽

2017 ◽

Vol 38 (suppl_1) ◽

Author(s):

M.J.G. Leening ◽

J. Pavlovic ◽

J.W. Deckers ◽

M. Kavousi ◽

A. Hofman ◽

...

Keyword(s):

Cardiovascular Disease ◽

Clinical Trial ◽

Primary Prevention ◽

Treatment Guidelines ◽

Population Based ◽

Rotterdam Study ◽

Cholesterol Treatment ◽

Clinical Trial Evidence ◽

Trial Evidence ◽

Prevention Of Cardiovascular Disease

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Text messages for primary prevention of cardiovascular disease: The TextMe2 Randomised Clinical Trial

American Heart Journal ◽

10.1016/j.ahj.2021.08.009 ◽

2021 ◽

Author(s):

Harry Klimis ◽

Aravinda Thiagalingam ◽

Daniel McIntyre ◽

Simone Marschner ◽

Amy Von Huben ◽

...

Keyword(s):

Cardiovascular Disease ◽

Clinical Trial ◽

Primary Prevention ◽

Randomised Clinical Trial ◽

Text Messages ◽

Prevention Of Cardiovascular Disease

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Computable Phenotype Implementation for a National, Multicenter Pragmatic Clinical Trial

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/circoutcomes.119.006292 ◽

2020 ◽

Vol 13 (6) ◽

Cited By ~ 1

Author(s):

Faraz S. Ahmad ◽

Iben M. Ricket ◽

Bradley G. Hammill ◽

Lisa Eskenazi ◽

Holly R. Robertson ◽

...

Keyword(s):

Cardiovascular Disease ◽

Clinical Trial ◽

Clinical Trials ◽

Large Scale ◽

Research Network ◽

Administrative Claims ◽

Atherosclerotic Cardiovascular Disease ◽

Research Networks ◽

Pragmatic Clinical Trial ◽

Pragmatic Clinical Trials

Background: Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)—a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events. Methods and Results: A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network—a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation. Conclusions: The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02697916.

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Therapie der Hypercholesterinämie in der Primärprävention

DMW - Deutsche Medizinische Wochenschrift ◽

10.1055/a-0657-1668 ◽

2019 ◽

Vol 144 (05) ◽

pp. 322-328 ◽

Cited By ~ 1

Author(s):

Veronika Sanin ◽

Wolfgang Koenig

Keyword(s):

Cardiovascular Disease ◽

Primary Prevention ◽

Density Lipoprotein ◽

Reasonable Doubt ◽

Lipid Lowering ◽

Current Evidence ◽

Individual Risk ◽

Atherosclerotic Cardiovascular Disease ◽

Mortality And Morbidity ◽

Target Values

AbstractAtherosclerotic cardiovascular disease is the leading cause of premature mortality and morbidity worldwide. Dyslipidemia is a commonly encountered clinical condition and is an important determinant of cardiovascular disease. The causality of plasma low-density lipoprotein-cholesterol (LDL-C) in the pathophysiology of cardiovascular disease has been established beyond any reasonable doubt. In this context, individual risk estimation, the determination of target values and lipid-lowering strategies represent an essential part and a challenge in the daily clinical practice to prevent cardiovascular events. Statins are recommended as first-line therapy for patients with hypercholesterolemia in secondary prevention. Controversies remain in the context of primary prevention, however, as to which kind of subjects to treat, the magnitude of the benefit, and potential harm. This article gives a brief overview of the current evidence, guideline recommendations and strategies for lowering of LDL-C in the primary prevention of cardiovascular disease.

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Power estimations for non-primary outcomes in randomised clinical trials

BMJ Open ◽

10.1136/bmjopen-2018-027092 ◽

2019 ◽

Vol 9 (6) ◽

pp. e027092 ◽

Cited By ~ 3

Author(s):

Janus Christian Jakobsen ◽

Christian Ovesen ◽

Per Winkel ◽

Jørgen Hilden ◽

Christian Gluud ◽

...

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Present Article ◽

Clinical Relevance ◽

Randomised Clinical Trial ◽

Randomised Clinical Trials ◽

Type I ◽

Type Ii ◽

Limit Problems ◽

Type Ii Errors

Objective and methods: It is rare that trialists report power estimations of non-primary outcomes. In the present article, we will describe how to define a valid hierarchy of outcomes in a randomised clinical trial, to limit problems with Type I and Type II errors, using considerations on the clinical relevance of the outcomes and power estimations. Conclusion: Power estimations of non-primary outcomes may guide trialists in classifying non-primary outcomes as secondary or exploratory. The power estimations are simple and if they are used systematically, more appropriate outcome hierarchies can be defined, and trial results will become more interpretable.

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Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

10.21203/rs.3.rs-35669/v1 ◽

2020 ◽

Author(s):

Ravinder Claire ◽

Christian Gluud ◽

Ivan Berlin ◽

Tim Coleman ◽

Jo Leonardi-Bee

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Smoking Cessation ◽

Sample Size ◽

Sequential Analysis ◽

Randomised Clinical Trial ◽

Randomised Clinical Trials ◽

Trial Sequential Analysis ◽

Cessation Intervention ◽

Pilot Trials

Abstract Background Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. Methods We describe using feasibility and pilot trial data in the Trial Sequential Analysis program to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using Trial Sequential Analysis to assess the intervention’s effects. Results We provide a worked example to illustrate how we successfully used Trial Sequential Analysis methods to argue for the research funds needed to undertake a randomised clinical trial. Conclusions Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.

Download Full-text