Real-Time Personalised Pharmacokinetic-Pharmacodynamic Modelling in Propofol Anesthesia through Bayesian Inference.

Pharmacological models describe a patient's response to the administration of a medicinal drug based on parameters derived from population studies. However, considerable inter-patient variability exists, such that population models may underperform when used to predict the actual response of a specific individual. In applications which demand predictive accuracy-such as target-controlled infusion of anesthetic agents-modeling uncertainty may reduce system dependability and introduce clinical risk. Our work investigates the use of Bayesian inference, implemented through a particle filter algorithm, to refine a prior model of propofol pharmacokinetics-pharmacodynamics and estimate patient-specific parameters in real-time. We report here on an observational clinical study conducted on 40 adults undergoing general anesthesia, where we evaluated the performance of Bayesian inference-personalized models in forecasting forward trends of depth of anesthesia (Bispectral Index) measurements and compared it with that of a traditional population-based pharmacological model. Our results show a significant reduction in prediction error metrics for the patient-specific models. Our study demonstrates the viability and practical implementability of Bayesian inference as a tool for real-time intra-operative estimation of personalized pharmacological models in anesthesia applications.

A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation.

Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry is manual which adds cognitive burden to the persons living with T1D. In this study, we proposed a control system based on Proximal Policy Optimisation (PPO) that controls both basal and bolus insulin infusion and only requires meal announcement, thus eliminating the need for carbohydrate estimation. We evaluated the system on a challenging meal scenario, using an open-source simulator based on the UVA/Padova 2008 model and achieved a mean Time in Range value of 65% for the adult subject cohort, while maintaining a moderate hypoglycemic and hyperglycemic risk profile. The approach shows promise and welcomes further research towards the translation to a real-life artificial pancreas. Clinical relevance- This was an in-silico analysis towards the development of an autonomous artificial pancreas system for glucose control. The proposed system show promise in eliminating the need for estimating the carbohydrate content in meals.

Using UV-Responsive Nanoparticles to Provide In Situ Control of Growth Factor Delivery and a More Constant Release Profile from a Hydrogel Environment.

Nanoparticles are popular delivery vehicles, but their diffusional release results in inconstant drug delivery. Here, we flatten the delivery profile into a more constant, zero-order profile. Brain-derived neurotrophic factor (BDNF) is attached to photoactive titanium dioxide nanoparticles and loaded into a nanofibrous self-assembling peptide (SAP) hydrogel. Different UV exposure conditions show three distinct profiles, including a counterintuitive decrease in release after UV exposure. We propose that the adsorption of the freed growth factor onto the hydrogel nanofibers affects release. Nanoparticles diffuse from the hydrogel readily, carrying the bound growth factor, but the freed growth factor (released from the nanoparticles by UV) instead interacts with─and is released less readily from─the hydrogel. UV shifts growth factor from nanoparticles to the hydrogel, therefore changing the diffusional release. Through midpoint UV exposure, we achieve a flattened delivery profile─unusual for diffusion─by changing in situ the amount of growth factor bound to the diffusing nanoparticles. With nanoparticle diffusion alone, we observed an increasing release profile with 36% of release in the first 6 h and 64% in the second 6 h. With midway UV exposure, this was controlled to 49 and 51%, respectively. The release of an unbound (soluble) control growth factor, glial cell-line derived neurotrophic factor (GDNF), was not affected by UV treatment, demonstrating the potential for independent control of temporal delivery profiles in a multiagent material.

An approach to controlled drug infusion via tracking of the time-varying dose-response.

Automatic administration of medicinal drugs has the potential of delivering benefits over manual practices in terms of reduced costs and improved patient outcomes. Safe and successful substitution of a human operator with a computer algorithm relies, however, on the robustness of the control methodology, the design of which depends, in turn, on available knowledge about the underlying dose-response model. Real-time estimation of a patient's actual response would ensure that the most suitable control algorithm is adopted, but the potentially time-varying nature of model parameters and the limited number of observation signals may cause the estimation problem to be ill-posed, posing a challenge to adaptive control methods. We propose the use of Bayesian inference through a particle filtering approach as a way to overcome these limitations and improve the robustness of automatic drug administration methods. We report on the results of a simulation study modeling the infusion of vasodepressor drug sodium nitroprusside for the control of mean arterial pressure in acute hypertensive patients. The proposed control architecture was able to meet the required performance objectives under challenging operating conditions.

Computational finite element model of cardiac torsion.

PURPOSE: A novel finite-element model of ventricular torsion for the analysis of the twisting behavior of the left human ventricle was developed, in order to investigate the influence of various biomechanical parameters on cardiac kinematics. METHODS: The ventricle was simulated as a thick-walled ellipsoid composed of nine concentric layers. Arrays of reinforcement bars were embedded in each layer to mimic physiological myocardial anisotropy. The reinforcement bars were activated through an artificial combination of thermal and mechanical effects in order to obtain a contractile behavior which is similar to that of myocardial fibers. The presence of an incompressible fluid inside the ventricular cavity was also simulated and the ventricle was combined with simple lumped-parameter hydraulic circuits reproducing preload and afterload. Changes to a number of cardiac parameters, such as preload, afterload and fiber angle orientation were introduced, in order to study the effects of these changes on cardiac torsion. RESULTS: The model is able to reproduce a similar torsional behavior to that of a physiological heart. The results of the simulations showed that there was sound correspondence between the model outcomes and available data from the literature. Results confirmed the importance of symmetric transmural patterns for fiber orientation. CONCLUSIONS: This model represents an important step on the path towards unveiling the complexity of cardiac torsion. It proves to be a practical and versatile tool which could assist clinicians and researchers by providing them with easily-accessible, detailed data on cardiac kinematics for future diagnostic and surgical purposes.

Noninvasive average flow estimation for an implantable rotary blood pump: a new algorithm incorporating the role of blood viscosity.

The effect of blood hematocrit (HCT) on a noninvasive flow estimation algorithm was examined in a centrifugal implantable rotary blood pump (iRBP) used for ventricular assistance. An average flow estimator, based on three parameters, input electrical power, pump speed, and HCT, was developed. Data were collected in a mock loop under steady flow conditions for a variety of pump operating points and for various HCT levels. Analysis was performed using three-dimensional polynomial surfaces to fit the collected data for each different HCT level. The polynomial coefficients of the surfaces were then analyzed as a function of HCT. Linear correlations between estimated and measured pump flow over a flow range from 1.0 to 7.5 L/min resulted in a slope of 1.024 L/min (R2=0.9805). Early patient data tested against the estimator have shown promising consistency, suggesting that consideration of HCT can improve the accuracy of existing flow estimation algorithms.