Anthony Matthews

The association between weight, and cardiovascular disease and mortality is well established, however, the causal effect of weight-loss in midlife on these outcomes is less clear. Bariatric surgery results in substantial weight-loss and is an ideal candidate to study the causal effects of weight-loss. We propose a project that willcausal inference and machine learning methods to answer two important questions: 2) Is bariatric surgery effective for reducing cardiovascular disease and mortality, and if so, for who? 3) Which type of bariatric surgery (gastric bypass or sleeve gastrectomy) is most effective, and for who?We will use data from various Swedish registers to identify individuals with obesity who are eligible for bariatric surgery. We will then compare cardiovascular and mortality outcomes among those undergoing different types of bariatric surgery with those receiving non-surgical obesity management using causal inference methods. We will use causal forests and expert knowledge to estimate indiviual treatment effects, and identify the groups of patients who benefit the most from these surgeries.This 3-year project will be undertaken by the CAUSALab, at the Unit of Epidemiology, Karolinska Institutet. The team of co-applicants, have extensive experience in using observational data to obtain causal inferences, particularly in the field of cardiovascular disease. A postdoctoral researcher will be hired to work full-time on this project.

Decisions about adoption of medical treatments by the healthcare system require precise quantification of their effectiveness. However, the findings from randomized trials may not be applicable to the general population of individuals who may benefit from the treatments. A treatment that works well on average in the selected participants in randomized trials may have a lower effectiveness in the typical patients, with a different distribution of socioeconomic and clinical factors, who will use the treatment in the real world. This reduction in effectiveness needs to be known and quantified so that it can guide policy decisions. Yet the task of extending randomized trial results to the general population of patients is often handled via informal subjective assessments.In contrast, we propose to develop, refine, and implement methodology to extend causal inferences from randomized trials to the general population of patients who may benefit from the treatments. This project will use real world data from the SWEDEHEART registry and linked nationwide registers, along with data from two randomized trials: TASTE and VALIDATE.  We will develop two use cases in cardiovascular research and build the tools that can later be applied to any other medical intervention. Specifically, we estimate the effect of routine thrombus aspiration before percutaneous coronary intervention in the whole trial-eligible population of Sweden, estimate the effect of two anticoagulant medications during percutaneous coronary intervention in the whole trial-eligible population of Sweden, and create tutorials, teaching materials, and open-source code to facilitate the uptake of this methodology by Swedish researchers. Our research will improve public health decisions in Sweden and will serve as a model for other countries interested in the extension of findings from randomized trials to their general population of patients who can benefit from the treatments under study.