Actively controlled cardiac afterload

Ex vivo (outside of the body) working heart models enable the evaluation of isolated hearts. They are envisioned to play an important role in increasing the currently low utilization rate of donor hearts for transplantation. For the heart to work in isolation, an afterload (flow impedance) is needed. To date, afterload devices have been constructed by combining multiple constituent elements such as pumps, flow resistances, and flow capacitances (compliances), typically to replicate the structure of so-called Windkessel models. This limits active control to that achievable by varying these elements, making it slow and subject to the problem of dynamic coupling between parameters. Here we present a novel concept to achieve Windkessel dynamics through a very simple variable flow impedance. The impedance is actively controlled using feedback from a pressure measurement. Through simulations we demonstrate the ability to perfectly emulate Windkessel dynamics, while imposing tight pressure limits needed for safe operation—something not achievable with the verbatim implementation using constituent elements

Identifiability of pharmacological models for online individualization

There is a large variability between individuals in the response to anesthetic drugs, that seriously limits the achievable performance of closed-loop controlled drug dosing. Full individualization of patient models based on early clinical response data has been suggested as a means to improve performance with maintained robustness (safety). We use estimation theoretic analysis and realization theory to characterize practical identifiability of the standard pharmacological model structure from anesthetic induction phase data and conclude that such approaches are not practically feasible

Pharmacometric covariate modeling using symbolic regression networks

A central challenge within pharmacometrics is to establish a relation between pharmacological model parameters, such as compartment volumes and diffusion rate constants, and known population covariates, such as age and body mass. There is rich literature dedicated to the learning of functional mappings from the covariates to the model parameters, once a search class of functions has been determined. However, the state-of-the-art selection of the search class itself is ad hoc. We demonstrate how neural network-based symbolic regression can be used to simultaneously find the function form and its parameters. The method is put in relation to the literature on symbolic regression and equation learning. A conceptual demonstration is provided through examples, as is a road map to full-scale employment to pharmacological data sets, relevant to closed-loop anesthesia

Prevention of ischemic myocardial contracture through hemodynamically controlled DCD

Purpose—Ischemic myocardial contracture (IMC) or ‘‘stoneheart’’ is a condition with rapid onset following circulatory death. It inhibits transplantability of hearts donated uponcirculatory death (DCD). We investigate the effectiveness of hemodynamic normalization upon withdrawal of life-sustaining therapy (WLST) in a large-animal controlled DCD model, with the hypothesis that reduction in cardiac work delays the onset of IMC. Methods—A large-animal study was conducted comprising of a control group (n = 6) receiving no therapy upon WLST, and a test group (n = 6) subjected to a protocol for fully automated computer-controlled hemodynamic drug administration. Onset of IMC within 1 h following circulatory death defined the primary end-point. Cardiac work estimates based on pressure-volume loop concepts were developed and used to provide insight into the effectiveness of the proposed computer-controlled therapy. Results—No test group individual developed IMC within 1 h, whereas all control group individuals did (4/6 within30 min). Conclusion—Automatic dosing of hemodynamic drugs in the controlled DCD context has the potential to prevent onset of IMC up to 1 h, enabling ethical and medically safe organ procurement. This has the potential to increase the use of DCD heart transplantation, which has been widely recognized as a means of meeting the growing demand for donor hearts

Individualized closed-loop anesthesia through patient model partitioning

This master thesis project proposes methods for individualizing closed-loop controlled anesthesia. One of the largest challenges with closed-loop anesthesia is the variation between patients in the sensitivity to the anesthetic drug, here propofol. Due to limited excitation in the process dynamics together with a high measurement noise level is it not possible to determine a full reliable model describing a patient’s dynamics online. The method used here for minimizing the effects of inter-patient variability was through patient model partitioning of children and adult models. Partitioning was based on similarity measures between patients, for example age, weight and applied to a dynamic model describing each patient. For each subset resulting from partitioning, an optimal PID controller has been synthesized. This thesis has shown that the effects of inter-patient variability can be reduced using partitioning into two subsets. More subsets did not result in a significant reduction. Partitioning based on n-gap between patient models resulted in the best attenuation of surgical stimulation disturbances. Partitioning based on age for children and weight for adults reduces the impact from surgical stimulation were proposed for clinical practices. These methods are easy to implement because the demographics are known beforehand and does not depend on actual measurements during the anesthesia. The results are substantiated by simulations and calculations of achieved attenuation with acceptable performance and preserved robustness

Learning pharmacometric covariate model structures with symbolic regression networks

Efficiently finding covariate model structures that minimize the need for random effects to describe pharmacological data is challenging. The standard approach focuses on identification of relevant covariates, and present methodology lacks tools for automatic identification of covariate model structures. Although neural networks could potentially be used to approximate covariate-parameter relationships, such approximations are not human-readable and come at the risk of poor generalizability due to high model complexity. In the present study, a novel methodology for simultaneous selection of covariate model structure and optimization of its parameters is proposed. It is based on symbolic regression, posed as an optimization problem with smooth loss function. This enables training the model through back-propagation using efficient gradient computations. Feasibility and effectiveness is demonstrated by application to a clinical pharmacokinetic data set for propofol, containing infusion and blood sample time series from 1,031 individuals. The resulting model is compared to a published state-of-the-art model for the same data set. Our methodology finds a covariate model structure and corresponding parameter values with a slightly better fit, while relying on notably fewer covariates than the state-of the-art model. Unlike contemporary practice, finding the covariate model structure is achieved without an iterative procedure involving manual interactions

Individualized closed-loop anesthesia through patient model partitioning

Closed-loop controlled drug dosing has the potential of revolutionizing clinical anesthesia. However, interpatient variability in drug sensitivity poses a central challenge to the synthesis of safe controllers. Identifying a full individual pharmacokinetic–pharmacodynamic (PKPD) model for this synthesis is clinically infeasible due to limited excitation of PKPD dynamics and presence of unmodeled disturbances. This work presents a novel method to mitigate inter-patient variability. It is based on: 1) partitioning an a priori known model set into subsets; 2) synthesizing an optimal robust controller for each subset; 3) classifying patients into one of the subsets online based on demographic or induction phase data; 4) applying the associated closed-loop controller. The method is investigated in a simulation study, utilizing a set of 47 clinically obtained patient models. Results are presented and discussed.Clinical relevance—The proposed method is easy to implement in clinical practice, and has potential to reduce the impact from surgical stimulation disturbances, and to result in safer closed-loop anesthesia with less risk of under- and over dosing