Comprehensive Summary
This study by Neleman et al. investigates the clinical applicability of an algorithm that quantifies coronary calcifications on intravascular ultrasound (IVUS) to determine patient cardiovascular outcomes. 408 patients who underwent coronary angiography and pre-procedural IVUS imaging between January 2008 and January 2018 were included in the study. A machine learning algorithm was used to calculate the VUS-calcium score (ICS) and then tested for associations with patient-oriented composite endpoints (POCE), consisting of all-cause mortality, stroke, myocardial infarction, and revascularization. From this cohort, the median ICS was 85, where patients with an ICS of at least 85 tended to be older and had a significantly higher incidence of POCE at 6-year follow-up. Furthermore, a 100-unit increase in ICS raised the risk of POCE and target vessel revascularization. ICS can be a good predictor of cardiovascular outcomes and may enhance risk assessment for patients with coronary artery disease.
Outcomes and Implications
Accurate prediction of cardiovascular events is important for improving patient outcomes and tailoring treatment strategies, especially for impaired cardiovascular and cerebrovascular outcomes. The ICS could provide clinicians with an automated tool for identifying patients at higher risk of adverse events during coronary angiography. However, limitations of the study include a limited sample size due to strict inclusion criteria and retrospective screening. In addition, the use of ICS is restricted to patients referred for coronary angiography and native coronary artery disease for now. Further studies have to be conducted before widespread clinical implementation becomes feasible.