Age-Stratified Prevalence and Relative Prognostic Significance of Traditional Atherosclerotic Risk Factors: A Report from the Nationwide Registry of Percutaneous Coronary Interventions in Japan.

Background The prevalence of traditional atherosclerotic risk factors (TARFs) and their association with clinical profiles or mortality in percutaneous coronary intervention remain unclear. Methods and Results The study analyzed 559 452 patients who underwent initial percutaneous coronary intervention between 2012 and 2019 in Japan. TARFs were defined as male sex, hypertension, dyslipidemia, diabetes, smoking, and chronic kidney disease. We calculated the relative importance according to and machine learning models to assess the impact of TARFs on clinical profile and in-hospital mortality. The relative contribution (RC) of each TARF was defined as the average percentage of the relative importance calculated from these models. The age-specific prevalence of TARFs, except for chronic kidney disease, formed an inverted U-shape with significantly different peaks and percentages. In the logistic regression model and relative risk model, smoking was most strongly associated with acute myocardial infarction (adjusted odds ratio [OR], 1.62 [95% CI, 1.60-1.64]; RC, 47.1%) and premature coronary artery disease (adjusted unstandardized beta coefficient, 2.68 [95% CI, 2.65-2.71], RC, 42.2%). Diabetes was most strongly associated with multivessel disease (adjusted unstandardized beta coefficient, 0.068 [95% CI, 0.066-0.070], RC, 59.4%). The absence of dyslipidemia was most strongly associated with presentation of cardiogenic shock (adjusted OR, 0.62 [95% CI, 0.61-0.64], RC, 34.2%) and in-hospital mortality (adjusted OR, 0.44 [95% CI, 0.41-0.46], RC, 39.8%). These specific associations were consistently observed regardless of adjustment or stratification by age. Conclusions Our analysis showed a significant variation in the age-specific prevalence of TARFs. Further, their contribution to clinical profiles and mortality also varied widely.