Background and aims: Pulse pressure has been commonly used to assess atherosclerosis and cardiovascular outcomes with the defects of fluctuation. However, the pulse pressure index (PPI), a feasible alternative with the advantage of lower fluctuations, has not been sufficiently researched.
Methods: This study included 10,796 participants over 65 years from the National Health and Nutrition Examination Survey 1999-2018. Cox proportional hazards models, restricted cubic splines and subgroup analysis were used to investigate the association between PPI in the elderly and all-cause and cardiovascular mortality. Subsequently, a prediction model for identifying coronary heart disease (CHD) in elderly individuals was developed using three machine learning algorithms. The impact of each feature on CHD was visualized in the optimal model after comparing the performances of the models.
Results: This study discovered that the highest levels of PPI were associated with a 28% increased all-cause [HR (95%CI) 1.28 (1.16, 1.42), P < 0.001] and a 36% increased risk of cardiovascular mortality [HR (95%CI) 1.36 (1.08, 1.72), P = 0.008] compared with the first quantile of PPI. The nonlinear relationships between PPI and mortality (all-cause: P for Nonlinear = 0.038; cardiovascular: P for Nonlinear = 0.005) were determined using restricted cubic spline curves. Among the three machine-learning models, random forest model showed the best performance (AUC 0.667 (0.638, 0.696)). In descending order of feature importance, PPI came in second place, with a positive relationship with CHD.
Conclusions: This study indicated a positive correlation between PPI and long-term adverse cardiovascular outcomes among the elderly. Notably, PPI has considerable predictive power for recognizing CHD.
Keywords: All-cause mortality; Cardiovascular mortality; Coronary heart disease; Machine learning; NHANES; Pulse pressure index.
© 2025. The Author(s).