Bridging Cardiovascular Risk from Clinical Trials to Real Life Population
Anne Chain (1), Meindert Danhof (1), Oscar E Della-Pasqua (1, 3), Kevin M Krudys (3), Miriam CJM Sturkenboom (2)
(1)Division of Pharmacology, Leiden/Amsterdam Center for Drug Research, Leiden University, The Netherlands, (2) Department of Medical Informatics and Epidemiology & Biostatistics, (2) Erasmus University Medical Center, The Netherlands (3)(3) GlaxoSmithKline, Clinical Pharmacology/Modelling and Simulation, United Kingdom
Recently, there has been increasing concerns in the rising number of proarrhythmia cases due to drug-induced QT prolongation. This has become the second most common cause for market drug withdrawal. Many efforts have already been made towards harmonizing the technical requirements in the clinical evaluation of QT/QTc prolongation and pro-arrhythmic potential for non-antiarrhythmic drugs. Apart from conducting adequate clinical evaluation, much more effort should also be put into explaining discrepancies between clinical research and real life observational data from epidemiological studies. The first step to determine how to predict real life observations from clinical trials of patient population is to identify and resolve patient parameters that have not been taken into consideration in the clinical trial.
To resolve this missing link it will be assumed that overall change in QT in a real life population, QTc(RLP) = QTc (Drug Effect) + QTc(Other Factors). QTc(Drug effect) is obtained from the traditional thorough QT studies in clinical trials and is the focus of many recent recommendations. This study, however, will concentrate on quantifying and explaining discrepancies between QT effects in clinical trials and real life by determining other factors, QTc(other factors), that are also contributing towards the overall change in QT in real life.
To determine QTc(other factors), a prospective cohort study will be performed in patients who started the use of SotalolTM as indicated in the IPCI general practice research database and the Rotterdam cohort of elderly. The set of patients will be analysed as a whole as well as in subgroups; subgroup I will contain patients with the same inclusion and exclusion criteria as a Phase II/III clinical trial, the remaining patients will belong to subgroup II. The risks of QT prolongation and sudden death will be calculated in groups I, II and I+II. Within each group it will be determined which covariates have the highest impact on the risk of QT prolongation or sudden cardiac death, SCD.
By analysing the real life data we aim to identify clinical parameters that are important for predicting the effects of a drug in its ‘usage environment' on the risk of QT prolongation. It is envisioned that the results from this study can eventually be incorporated into a predictive QT/QTc PK/PD model.