Event detection using population-based health care databases in randomized clinical trials: a novel research tool in interventional cardiology

AIM To describe a new research tool, designed to reflect routine clinical practice and relying on population-based health care databases to detect clinical events in randomized clinical trials. BACKGROUND Randomized clinical trials often focus on short-term efficacy and safety in a controlled environment. Trial follow-up may be linked with study-related investigations and differ from routine clinical practice. Because treatment and control in randomized trials differ from daily practice, trial results may have reduced general applicability and may be of limited value in clinical decision-making. Further, it is economically very costly to conduct randomized clinical trials. METHODS AND RESULTS Population-based health care databases collect data continuously and prospectively, and make it possible to monitor lifelong outcomes of cardiac interventions in large numbers of patients. This strengthens external validity by eliminating the effects of study-related monitoring or diagnostic tests. Further, follow-up data can be obtained at low expense. Importantly, data sources encompassing a complete population are likely to reflect clinical practice. Because population-based health care databases collect data for quality-control and administrative purposes unrelated to scientific investigations, certain biases, such as nonresponse bias, recall bias, and bias from losses to follow-up, can be avoided. CONCLUSION Event detection using population-based health care databases is a new research tool in interventional cardiology that may allow large, low-cost, randomized clinical trials to reflect daily clinical practice, covering a broad range of patients and end points with complete lifelong follow-up.