Back

Mediation Analysis To Inform Policy On Coronary Revascularization By Expected Time To Treatment: Analytical Framework

Sobolev, B.; Kuramoto, L.

2022-01-04 health systems and quality improvement
10.1101/2021.12.30.21268556 medRxiv
Show abstract

ObjectivesClinical guidelines favour coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) for patients with stable complex coronary disease. Yet the benefit of CABG as established in trials may not be generalizable to populations in which treatment method determines time to treatment, typically being longer for CABG. For cases in which the cardiac anatomy is suitable for either treatment, it is unclear whether it is appropriate to recommend CABG, which is likely to be delayed, if PCI can be performed sooner. This paper outlines an analytical framework for a policy analysis of the timing of coronary revascularization. MethodsWe constructed a thought experiment to examine whether time to treatment will influence the advantage of CABG. We substantiated the use of mediation analysis to estimate the extent to which differences in outcomes between CABG and PCI would change if times to CABG were the same as times to PCI. ResultsWe designed a study that uses data from a population-based patient registry to obtain effect measures of mediation analysis: the total effect, the natural indirect effect, and the natural direct effect. The partitioning of the total effect will allow us to estimate the proportional reduction in the risk of an outcome if the time to CABG was similar to that of PCI. InterpretationTreatment recommendation, resource allocation and scheduling benchmarks will be guided by understanding the extent to which the time to treatment mediates the relation between revascularization method and outcome.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.