Abstract
Computational cardiovascular models hold promise for simulations in education and bedside clinical decision support. To enhance patient-specific modeling, individual anthropometrics are imperative, as physiology varies with body size due to fundamental energetic relations expressed in allometric scaling laws. We hypothesize that computational cardiovascular models can be advanced towards individualization by implementing scaling laws based on patient age, weight, height, and sex. A scaling methodology was developed for the lumped-parameter cardiovascular model Aplysia Cardiovascular Lab. Male and female subjects were based on Swedish growth charts from birth to adult size and simulated to test model realism. Realistic physiology was generated for underweight, overweight, and average male and female patients from birth to 80 years. Model output included comprehensive measures of hemodynamics, cardiac function, respiratory function, gas exchange, ventilatory mechanics, and energy expenditure. In comparison to published data, aggregate Z scores for infant, pediatric, and geriatric simulations were 1.16, 0.69, and 0.10, respectively. Allometric scaling laws can be used to generate parameter sets of males and females of disparate sizes and ages in line with published data. This sets the stage for modeling diverse patient populations and novel approaches toward individualized clinical applications.