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
