Delineating the Relationship Between Cardiovascular Physiology and Subject Characteristics Using Computational Modeling and Systemic Review

Lumped Parameter Networks (LPN) are computational models that can capture the systemic responses of the cardiovascular system. An LPN describes the systemic behavior by providing outputs such as the blood pressure, flowrate, and heart chamber volume at the points defined in the network. To use the LPN for medical applications, it needs to provide output values that represent the physiology of realistic subjects. The cardiovascular system exhibits short-term and long-term adaptations to factors such as aging, weight gain, exercise, etc. Therefore, subject characteristics such as age, height, weight, etc. affect the output values of the LPN. Current literature reports the mean reference values of the outputs for the population and the trends of the outputs for each characteristic separately. However, the combined effects of multiple characteristics and the corresponding numerical output values are generally not reported. This study first remodels an existing LPN to represent an average healthy adult. Next, a systematic review was conducted to compile blood pressure, cardiac output, and ventricular volume data for groups of healthy subjects. Regression algorithms used this aggregate data to formulate predictive models for the outputs – systolic and diastolic blood pressure, ventricular volumes, cardiac output, and heart rate – against the characteristics – age, height, weight, and exercise intensity. A simulation-based procedure generated data of virtual subjects to confirm whether these regression models built based on aggregate data can perform well for individual-level predictions. The blood pressure and heart rate models were also validated using real individual data. Although the multivariable regression models use aggregate data, the expected error for individual predictions is provided. The direction of trends between model outputs and the input subject characteristics reported in this study agree with the results in current literature. The high prediction errors (20% - 50%) can be attributed to the best models being linear. Although other studies observe exponential predictor-output relations, the non-linear algorithms do not fit the data well. Future studies involving the LPN can use the output values predicted by these regression models as targets to model the cardiovascular system of a population cohort or an individual described using age, height, weight, and exercise intensity

Systematic Review and Regression Modeling of the Effects of Age, Body Size, and Exercise on Cardiovascular Parameters in Healthy Adults

Purpose Blood pressure, cardiac output, and ventricular volumes correlate to various subject features such as age, body size, and exercise intensity. The purpose of this study is to quantify this correlation through regression modeling. Methods We conducted a systematic review to compile reference data of healthy subjects for several cardiovascular parameters and subject features. Regression algorithms used these aggregate data to formulate predictive models for the outputs—systolic and diastolic blood pressure, ventricular volumes, cardiac output, and heart rate—against the features—age, height, weight, and exercise intensity. A simulation-based procedure generated data of virtual subjects to test whether these regression models built using aggregate data can perform well for subject-level predictions and to provide an estimate for the expected error. The blood pressure and heart rate models were also validated using real-world subject-level data. Results The direction of trends between model outputs and the input subject features in our study agree with those in current literature. Conclusion Although other studies observe exponential predictor-output relations, the linear regression algorithms performed the best for the data in this study. The use of subject-level data and more predictors may provide regression models with higher fidelity. Significance Models developed in this study can be useful to clinicians for personalized patient assessment and to researchers for tuning computational models