Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system’s temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system’s parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation.Sarmiento, Carlos & Hernandez, Alher Mauricio & Mananas, Miguel Angel & Serna, Leidy. (2024). A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise. Journal of Personalized Medicine. 14. 406. 10.3390/jpm14040406.
Most of the parameters optimized for DB1 show slight differences regarding the predicted ones and those that were not modified (*) (variations of less than 10%). Therefore, similar simulation results could be expected. In this case, all the predictions were in accordance with the defined physiological justification criteria, which suggests good fits for a restricted number of samples and predictions with consistent variations considering the prediction time and the input data.
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From the results related to DB2, it was observed that the number of non-modeled parameters (*) decreased with the number of samples, which is related to the greater coverage of the prediction of physiological characteristics related to cardiorespiratory variations. Even so, the number of predictions that did not satisfy the physiological justification criteria increased for the last two records (**), related to lower goodness-of-fit measures and more significant variations, mainly for record six, considering the longer
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Figure 4. Steady-state simulation results against experimental data from the third DB1 record in function of experimental levels of exercise (VCO2). Simulations were based on the third DB1 record following three approaches: time-specific fit, single-fit, and proposed dynamic fit. They aimed to evaluate the physiological model performance from a population perspective. It was shown that the simulations of the dynamic fit approach were the most similar to those of the time-specific fit approach for this record, mainly for the variables VE, VT, TI , PACO2 , and HR , both in terms of magnitude and behavior. Although PM and PD show some of the most significant differences in magnitude, their behaviors concerning the stimulus are similar, mainly for PD, compared to the results of the traditional single-time fit approach. Some variables that present the most significant differences for the dy-namic fit approach are related to the parameter values with the highest variations regard-ing those o
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and Fitted values for record 3 in Table 5, respec-tively). Thus, the results of PM and PD can be attributed mainly to the values of I0,met and PaCO2,n and BF to the values of 2 and Pmax. For PAO2, the differences in the parameter values are not so significant, so the results may mainly be related to the effect of the sys-temic arterial pressures on the blood flows that enter the gas exchange system (Equations of Gas Exchange and Mixing and Gas transport in the Supplementary Materials). Figure 5 shows the PE values obtained for the physiological model predictions re-garding the third DB1 record. The data are presented as the median and interquartile range of the PE results for each approach. The average PE values for each subject are pre-sented as individual points. The overall PE values and statistically significant differences between the results for each approach are also presented.
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