Introduction: Wave condition number (WCN) is a non-dimensional number that can predict the optimum arterial wave reflection for left ventricle pulsatile workload minimization across mammalian cardiovascular systems. We previously showed that the optimum value of WCN is ~ 0.1 among all mammals. Our hypothesis is that changes in WCN can be used to predict coronary heart diseases (CHD).
Methods: Baseline population consisted of 5220 participants from Framingham Heart Study, who were free of cardiovascular diseases (CVD) and hypertension. During a 10 year monitoring period, 120 subjects had a first major CHD event. WCNs were computed from tonometry measured carotid waveforms. Standardized hazard ratios were computed for five models: Model 1, targeted WCN only. Model 2 adjusted for gender, age and body mass index (BMI). Model 3 further adjusted for Framingham risk scores. Kaplan-Meier method was applied and subjects were segregated into two groups: WCN<0.06 and WCN>=0.06 (threshold based on lower quartile of WCN). Cox regression models were additionally produced for the group with WCN<0.06 (n=1314) with WCN only (model 4) and adjusted for age, gender and BMI (model 5).
Results: WCN was significantly associated with increased risk of CHD events in all models: model 1: Hazard Ratio (HR)= 1.37, p=0.0003; model 2: HR=1.19, p=0.0337; model 3: HR=1.22, p=0.02; model 4: HR=2, p=0.0002; and model 5: HR=1.61, p=0.0222. Figure shows that subjects with WCN<0.06 were at significantly higher risk of developing a CHD event (p=0.0028). Hazard ratios underscore an increased risk for CHD event in the population with low WCN.
Conclusions: These results show that WCN is associated with risk for onset of CHD events. The population with low WCN displayed a higher HR. The results suggest that WCN may be a useful addition to standard risk assessment for CHD. These observations have important clinical implications for preventive medicine since WCN can be computed noninvasively and inexpensively.