A population model of integrative cardiovascular physiology

• Published in: PloS one Vol. 8; no. 9; p. e74329
• Main Authors: Pruett, William A, Husband, Leland D, Husband, Graham, Dakhlalla, Muhammad, Bellamy, Kyle, Coleman, Thomas G, Hester, Robert L
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 13.09.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Baroreflex - physiology; Biophysics; Blood; Blood Pressure; Blood Volume; Calibration; Carbon Monoxide - metabolism; Cardiovascular Physiological Phenomena; Classification; Collapse; Compensators; Correlation analysis; Heart Failure - physiopathology; Hemorrhage; Hemorrhage - physiopathology; Homeostasis; Humans; Mathematical models; Medical research; Models, Cardiovascular; Parameter sensitivity; Physiological aspects; Physiology; Population; Pressoreceptors - metabolism; Probabilistic methods; Rodents; Signal strength; Support Vector Machine; Support vector machines; Survival Analysis; Time Factors; Time series; Vascular Resistance; United States > US; Mississippi
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0074329

We present a small integrative model of human cardiovascular physiology. The model is population-based; rather than using best fit parameter values, we used a variant of the Metropolis algorithm to produce distributions for the parameters most associated with model sensitivity. The population is built by sampling from these distributions to create the model coefficients. The resulting models were then subjected to a hemorrhage. The population was separated into those that lost less than 15 mmHg arterial pressure (compensators), and those that lost more (decompensators). The populations were parametrically analyzed to determine baseline conditions correlating with compensation and decompensation. Analysis included single variable correlation, graphical time series analysis, and support vector machine (SVM) classification. Most variables were seen to correlate with propensity for circulatory collapse, but not sufficiently to effect reasonable classification by any single variable. Time series analysis indicated a single significant measure, the stressed blood volume, as predicting collapse in situ, but measurement of this quantity is clinically impossible. SVM uncovered a collection of variables and parameters that, when taken together, provided useful rubrics for classification. Due to the probabilistic origins of the method, multiple classifications were attempted, resulting in an average of 3.5 variables necessary to construct classification. The most common variables used were systemic compliance, baseline baroreceptor signal strength and total peripheral resistance, providing predictive ability exceeding 90%. The methods presented are suitable for use in any deterministic mathematical model.