A Dynamic Fitting Strategy for Physiological Models: A Case Study of a Cardiorespiratory Model for the Simulation of Incremental Aerobic Exercise.

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. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques.

BACKGROUND: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment. RESEARCH OBJECTIVE: To assist the prosthetist during the assessment of prosthetic alignment using a new computational protocol based on machine learning. METHODS: Sixteen transfemoral amputees were recruited for training and validation of the alignment protocol. Four misalignments and one nominal alignment were performed. Eleven prosthetic limb ground reaction force parameters were recorded. A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained to predict the alignment condition, as well as the magnitude and angle of required to align the prosthesis correctly. The alignment protocol was validated by one junior and one senior prosthetist during the prosthetic alignment of two transfemoral amputees. RESULTS: The support vector machine-based model detected the nominal alignment 92.6 % of the time. The neural network recovered 94.11 % of the angles needed to correct the prosthetic misalignment with a fitting error of 0.51°. During the validation of the alignment protocol, the computational models and the prosthetists agreed on the alignment assessment. The gait quality evaluated by the prosthetists reached a satisfaction level of 8/10 for the first amputee and 9.6/10 for the second amputee. IMPORTANCE: The new computational prosthetic alignment protocol is a tool that helps the prosthetist during the prosthetic alignment procedure thereby decreasing the likelihood of gait deviations and musculoskeletal diseases associated with misalignments and consequently improving the amputees-prosthesis adherence.

A Novel Strategy to Fit and Validate Physiological Models: A Case Study of a Cardiorespiratory Model for Simulation of Incremental Aerobic Exercise.

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy's usefulness.

The effect of prosthetic alignment on the stump temperature and ground reaction forces during gait in transfemoral amputees.

BACKGROUND: Lower limb prosthetic alignment is a procedure mostly subjective. A prosthetic misaligned induces gait deviations and long-term joint diseases. The alignment effects for each lower limb and the stump stays uncertain. RESEARCH OBJECTIVE: To identify the effect of the transfemoral alignment prosthesis on ground reaction forces and thermal images of the residual limb. METHODS: The effect of misalignment and nominal alignment was evaluated in sixteen transfemoral amputees. The nominal alignment was considered as the optimal alignment for each subject. Misalignment included random variations in the anterior-posterior and medial-lateral translation of the prosthetic foot and the angle of flexion-extension, abduction-adduction, and internal-external rotation of the socket and prosthetic foot. The control group consisted of fifteen non-amputee individuals. The ground reaction force parameters and stump temperature were analyzed for each alignment condition. The statistical analysis included the one-way ANOVA, Kruskal-Wallis, and multiple comparison tests. RESULTS: The prosthesis did not produce statistically significant changes in the average temperature of residual limbs. However, the temperature distribution on the stump skin was different (P < 0.05). The transfemoral prosthesis misalignment produced an irregular heat diffusion on the anterior, posterior, and lateral sides of the stump contour compared to the nominal alignment (P < 0.05). The sound limb did not show differences between nominal alignments and misalignments for most ground reaction force parameters. For almost all GRF parameters, significant differences were observed for the prosthetic limb between misalignment and nominal alignment (P < 0.001). The symmetry indices of ground reaction force parameters of transfemoral amputees did not show any kind of significant improvements after aligning the prosthesis nominally. SIGNIFICANCE: The stump's temperature distribution and the ground reaction force findings for the prosthetic limb provide a better understanding of the alignment procedure of the transfemoral prosthesis and improve the amputees' compliance to the prosthesis.

Population Pharmacokinetic Models of Antituberculosis Drugs in Patients: A Systematic Critical Review.

BACKGROUND: Tuberculosis (TB) remains one of the most important infectious diseases. Population pharmacokinetic (pop-PK) models are widely used to individualize dosing regimens of several antibiotics, but their application in anti-TB drug studies is scant. The aim of this study was to provide an insight regarding the status of pop-PK for these drugs and to compare results obtained through both parametric and nonparametric approaches to design precise dosage regimens. METHODS: First, a systematic approach was implemented, searching in PubMed and Google Scholar. Articles that did not include human patients, that lacked an explicit structural model, that analyzed drugs inactive against M. tuberculosis, or were without full-text access, were excluded. Second, the PK parameters were summarized and categorized as parametric versus nonparametric results. Third, a Monte Carlo simulation was performed in Pmetrics using the results of both groups, and an error term was built to describe the imprecision of each PK modeling approach. RESULTS: Thirty-three articles reporting at least 1 pop-PK model of 19 anti-TB drug were found; 46 different models including PK parameter estimates and their relevant covariates were also reported. Only 9 models were based on nonparametric approaches. Rifampin was the drug most studied, but only using parametric approaches. The simulations showed that nonparametric approaches improve the error term compared with parametric approaches. CONCLUSIONS: More and better models, ideally using nonparametric approaches linked with clear pharmacodynamic goals, are required to optimize anti-TB drug dosing, as recommended in the WHO End TB strategy.

An Improved Dynamic Model for the Respiratory Response to Exercise.

Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model). Secondly, to propose changes in the models' structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency.

Development of a wearable vital signs monitor for healthcare.

In development countries the vital signs data measurement normally is performed at hospitals or laboratories where patients remain under observation with many electrodes attached on the body. The integration of biomedical data acquisition systems and information technologies (IT) enables continuous real time monitoring of physiological data in daily life, which improves patient's medical care and medical research possibilities. To achieve this goal, the research and development of some wearable intelligent sensors, sensors miniaturization, signal processing, wireless transmission, and databases development for these vital data have been done. Our goal is to implement a wearable system that can be used in places located outside of hospitals and medical institutions coverage area. In this paper, we present the current stage of the project where some intelligent modules have been implemented and other are under construction. Preliminary results concerning Non-Invasive Blood Pressure (NIBP), ECG and wireless connection are also presented.

Computational tool for modeling and simulation of mechanically ventilated patients.

The mechanical ventilator settings in patients with respiratory diseases like chronic obstructive pulmonary disease (COPD) during episodes of acute respiratory failure (ARF) is not a simple task that in most cases is successful based on the experience of physicians. This paper describes an interactive tool based in mathematical models, developed to make easier the study of the interaction between a mechanical ventilator and a patient. It describes all stages of system development, including simulated ventilatory modes, the pathologies of interest and interaction between the user and the system through a graphical interface developed in Matlab and Simulink. The developed computational tool allows the study of most widely used ventilatory modes and its advantages in the treatment of different kind of patients. The graphical interface displays all variables and parameters in the common way of last generation mechanical ventilators do and it is totally interactive, making possible its use by clinical personal, hiding the complexity of implemented mathematical models to the user. The evaluation in different clinical simulated scenes adjusts properly with recent findings in mechanical ventilation scientific literature.