Aims: The CLIMA study, on the relationship between coronary plaque morphology of the left anterior descending artery and twelve months clinical outcome, was designed to explore the predictive value of multiple high-risk plaque features in the same coronary lesion [minimum lumen area (MLA), fibrous cap thickness (FCT), lipid arc circumferential extension, and presence of optical coherence tomography (OCT)-defined macrophages] as detected by OCT. Composite of cardiac death and target segment myocardial infarction was the primary clinical endpoint. Methods and results: From January 2013 to December 2016, 1003 patients undergoing OCT evaluation of the untreated proximal left anterior descending coronary artery in the context of clinically indicated coronary angiogram were prospectively enrolled at 11 independent centres (clinicaltrial.gov identifier NCT02883088). At 1-year, the primary clinical endpoint was observed in 37 patients (3.7%). In a total of 1776 lipid plaques, presence of MLA <3.5 mm2 [hazard ratio (HR) 2.1, 95% confidence interval (CI) 1.1–4.0], FCT 180° (HR 2.4, 95% CI 1.2–4.8), and OCT-defined macrophages (HR 2.7, 95% CI 1.2–6.1) were all associated with increased risk of the primary endpoint. The pre-specified combination of plaque features (simultaneous presence of the four OCT criteria in the same plaque) was observed in 18.9% of patients experiencing the primary endpoint and was an independent predictor of events (HR 7.54, 95% CI 3.1–18.6). Conclusion: The simultaneous presence of four high-risk OCT plaque features was found to be associated with a higher risk of major coronary events.

Clinicians often face difficult decisions despite the lack of evidence from randomized trials. Thus, clinical evidence is often shaped by non-randomized studies exploiting multivariable approaches to limit the extent of confounding. Since their introduction, propensity scores have been used more and more frequently to estimate relevant clinical effects adjusting for established confounders, especially in small datasets. However, debate persists on their real usefulness in comparison to standard multivariable approaches such as logistic regression and Cox proportional hazard analysis. This holds even truer in light of key quantitative developments such as bootstrap and Bayesian methods. This qualitative review aims to provide a concise and practical guide to choose between propensity scores and standard multivariable analysis, emphasizing strengths and weaknesses of both approaches.