Search

AimTo estimate the COVID-19 infection-to-fatality ratio (IFR), infection-to-case ratio (ICR), and infection-to-ICU admission ratio (IIAR) in Sweden; to suggest methods for time series reconstruction and prediction.MethodsWe optimize a set of simple finite impulse response (FIR) models comprising of a scaling factor and time-delay between officially reported cases, ICU admissions and deaths time series using the least squares method. Combined with randomized PCR study results, we utilize this simple model to estimate the total number of infections in Sweden, and the corresponding IFR.ResultsThe model class provides a good fit between ICU admissions and deaths throughout 2020. Cases fit consistently from July 2020, by when PCR tests had become broadly available. We observe a diminished IFR in late summer as well as a strong decline during 2021, following the launch of a nation-wide vaccination program. The total number of infections during 2020 is estimated to 1.3 million.ConclusionsA FIR model with a delta filter function describes the evolution of epidemiological data in Sweden well. The fact that we found IFR, ICR and IIAR constant over large parts of 2020 is in contrast with claims of healthcare adaptation or mutated virus variants importantly affecting these ratios. The model allows us to retrospectively estimate the COVID-19 epidemiological trajectory, and conclude that Sweden was far from herd immunity by the end of 2020.

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.

Purpose Ischemic myocardial contracture (IMC) or “stone heart” is a condition with rapid onset following circulatory death. It inhibits transplantability of hearts donated upon circulatory 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 ($$n=6$$ n = 6 ) receiving no therapy upon WLST, and a test group ($$n=6$$ 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 $${1} \text { h}$$ 1 h , whereas all control group individuals did (4/6 within $${30}{\text { min}}$$ 30 min ). Conclusion Automatic dosing of hemodynamic drugs in the controlled DCD context has the potential to prevent onset of IMC up to $${1}{\text { h}}$$ 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.

The role of non-pharmaceutical interventions (NPIs) on the spread of SARS-CoV-2 has drawn significant attention, both scientific and political. Particularly, an article by the Imperial College COVID-19 Response Team (ICCRT), published online in Nature on June 8, 2020, evaluates the efficiency of 5 NPIs. Based on mortality data up to early May, it concludes that only one of the interventions, lockdown, has been efficient in 10 out of 11 studied European countries.We show, via simulations using the ICCRT model code, that conclusions regarding the effectiveness of individual NPIs are not justified. Our analysis focuses on the 11th country, Sweden, an outlier in that no lockdown was effectuated. The new simulations show that estimated NPI efficiencies across all 11 countries change drastically unless the model is adapted to give the Swedish data special treatment. While stated otherwise in the Nature article, such adaptation has been done in the model code reproducing its results: An ungrounded country-specific parameter said to have been introduced in all 11 countries, is in the code only activated for Sweden. This parameter de facto provides a new NPI category, only present in Sweden, and with an impact comparable to that of a lockdown.While the considered NPIs have unarguably contributed to reduce virus spread, our analysis reveals that their individual efficiency cannot be reliably quantified by the ICCRT model, provided mortality data up to early May.

IntroductionA series of modelling reports that quantify the effect of non-pharmaceutical interventions (NPIs) on the spread of the SARS-CoV-2 virus have been made available prior to external scientific peer-review. The aim of this study was to investigate the method used by the Imperial College COVID-19 Research Team (ICCRT) for estimation of NPI effects from the system theoretical viewpoint of model identifiability.MethodsAn input-sensitivity analysis was performed by running the original software code of the systems model that was devised to estimate the impact of NPIs on the reproduction number of the SARS-CoV-2 infection and presented online by ICCRT in Report 13 on March 30 2020. An empirical investigation was complemented by an analysis of practical parameter identifiability, using an estimation theoretical framework.ResultsDespite being simplistic with few free parameters, the system model was found to suffer from severe input sensitivities. Our analysis indicated that the model lacks practical parameter identifiability from data. The analysis also showed that this limitation is fundamental, and not something readily resolved should the model be driven with data of higher reliability.DiscussionReports based on system models have been instrumental to policymaking during the SARS-CoV-2 pandemic. With much at stake during all phases of a pandemic, we conclude that it is crucial to thoroughly scrutinise any SARS-CoV-2 effect analysis or prediction model prior to considering its use as decision support in policymaking. The enclosed example illustrates what such a review might reveal.

Modeling is fundamental to both feed-forward and feedback control. Within automated anesthesia the two paradigms are usually referred to as target-controlled infusion (TCI) and closed-loop drug delivery, respectively. In both cases, the objective is to control a system with anesthetic drug infusion rate as input, and (measured) clinical effect as output. The input is related to the output through the pharmacokinetics (PK) and pharmacodynamics (PD) of the patient. This chapter gives an introduction to PKPD modeling in automated anesthesia management, intended to be accessible to both anesthesiology and (control) engineering researchers. The following topics are discussed: the role of modeling; the classic PKPD structure used in clinical pharmacology; anesthesia modeling and identification for closed-loop control; inter-patient variability and model uncertainty; disturbance, noise and equipment models. The chapter emphasizes electroencephalogram-guided control of propofol. (Less)

First responders to cardiac arrest depend on cardiopulmonary resuscitation to keep patients alive. A new ventilation method, phase-controlled intermittent insufflation of oxygen, was previously shown to improve heart perfusion during cardiopulmonary resuscitation in a large-animal study, outperforming the best currently known ventilation method. This paper investigates whether the advantage of the new method can be explained using standard linear lumped-parameter models of respiratory mechanics. The simple models were able to qualitatively capture the improvement.

Relay autotuning has proven very successful for single-input single-output systems. This paper proposes an identification method for relay autotuning of systems with two inputs and two outputs (TITO systems). The combination of asymmetric relay feedback and output error identification admits short tuning time, without the need for limit cycle convergence. The method is successfully demonstrated on relevant system models, including the Wood-Berry distillation column.

A method for optimization of PID controller parameters and measurement filter time constant is presented. The method differs from the traditional approach in that the controller and filter parameters are simultaneously optimized, as opposed to standard, sequential, design. Control performance is maximized through minimization of the integrated absolute error (IAE) caused by a unit step load disturbance. Robustness is achieved through Hinf constraints on sensitivity and complementary sensitivity. At the same time, noise attenuation is enforced by limiting either the H2 or Hinf norm of the transfer function from measurement noise to control signal. The use of exact gradients makes the synthesis method faster and more numerically robust than previously proposed alternatives. (Less)

Purpose: It has previously been shown that continuous intratracheal insufflation of oxygen (CIO) is superior to intermittent positive pressure ventilation (IPPV) regarding gas exchange and haemodynamics. The purpose of this study was to investigate gas exchanged and haemodynamics with a new technique of phase-controlled intermittent insufflation of oxygen (PIIO) compared to CIO. Method: Twenty (20) pigs were used, stratified into two groups (CIO, PIIO), with 10 animals each. Upon induction of ventricular fibrillation, standard ventilator support was replaced by either of CIO or PIIO ventilation. Chest compressions were delivered by the LUCAS I mCPR device. Following 20 min of CPR in normothermia, defibrillation was attempted. Results: Return of spontaneous circulation (ROSC) occurrence was not significantly higher (P

A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise. (Less)

In this paper we propose an autotuning method that combines a setup for decentralized relay autotuning of two-input two-output systems with an identification method that uses short experiments to estimate up to second-order time-delayed systems. A small modification of the experiment gives better low-frequency excitation and improved models. The method is successfully demonstrated in simulations and on a quadruple tank process.

Objective: The purpose of this paper is to demonstrate feasibility of a novel closed-loop controlled therapy for prevention of hypertension in the heartbeating brain-dead porcine model. Methods: Dynamic modeling and system identification were based on in vivo data. A robust controller design was obtained for the identified models. Disturbance attenuation properties and reliability of operation of the resulting control system were evaluated in vivo . Results: The control system responded both predictably and consistently to external disturbances. It was possible to prevent mean arterial pressure to fall below a user-specified reference throughout 24 h of completely autonomous operation. Conclusion: Parameter variability in the identified models confirmed the benefit of closed-loop controlled administration of the proposed therapy. The evaluated robust controller was able to mitigate both process uncertainty and external disturbances. Significance: Prevention of hypertension is critical to the care of heartbeating brain-dead organ donors. Its automation would likely increase the fraction of organs suitable for transplantation from this patient group.

Purpose: The purpose of this concept study was to investigate the possibility of automatic mean arterial pressure (MAP) regulation in a porcine heart-beating brain death (BD) model. Hemodynamic stability of BD donors is necessary for maintaining acceptable quality of donated organs for transplantation. Manual stabilization is challenging, due to the lack of vasomotor function in BD donors. Closed-loop stabilization therefore has the potential of increasing availability of acceptable donor organs, and serves to indicate feasibility within less demanding patient groups.Method: A dynamic model of nitroglycerine pharmacology, suitable for controller synthesis, was identified from an experiment involving an anesthetized pig, using a gradient-based output error method. The model was used to synthesize a robust PID controller for hypertension prevention, evaluated in a second experiment, on a second, brain dead, pig. Hypotension was simultaneously prevented using closed-loop controlled infusion of noradrenaline, by means of a previously published controller.Results: A linear model of low order, with variable (uncertain) gain, was sufficient to describe the dynamics to be controlled. The robustly tuned PID controller utilized in the second experiment kept the MAP within a user-defined range. The system was able to prevent hypertension, exceeding a reference of 100 mmHg by more than 10 %, during 98 % of a 12 h experiment.Conclusion: This early work demonstrates feasibility of the investigated modelling and control synthesis approach, for the purpose of maintaining normotension in a porcine BD model. There remains a need to characterize individual variability, in order to ensure robust performance over the expected population.

This paper demonstrates how second-order time-delayed models adequate for PID controller synthesis can be identified from significantly shorter relay experiments, than used in previous publications to obtain first-order time-delayed models. Apart from having good noise robustness properties, the proposed method explicitly addresses non-stationary initial states of the dynamics to be identified, and handles constant load disturbances.

Closed-loop control of anesthesia is expected to decrease drug dosage and wake up time while increasing patient safety and decreasing the work load of the anesthesiologist. The potential of closed-loop control in anesthesia has been demon- strated in several clinical studies. One of the challenges in the development of a closed-loop system that can be widely accepted by clinicians and regulatory authorities is the effect of inter- patient variability in drug sensitivity. This system uncertainty may lead to unacceptable performance, or even instability of the closed-loop system for some individuals. The development of reliable models of the effect of anesthetic drugs and charac- terization of the uncertainty is therefore an important step in the development of a closed-loop system. Model identification from clinical data is challenging due to limited excitation and the lack of validation data. In this paper, approximate models are therefore validated for controller design by evaluating the predictive accuracy of the closed-loop behavior. A set of 47 validated models that describe the inter-patient variability in the response to propofol in children is presented. This model set can be used for robust linear controller design provided that the experimental conditions are similar to the conditions during data collection.

A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust controller synthesis.

This paper presents a combined experiment and identification procedure, well suited to obtain low-order dynamic models of a patients’ response to continuous drug administration. The experiment requires no a priori information and is of very short duration. The identification method provides both a parametric low-order model, and an estimate of the parameter error covariance. It has been demonstrated to work well with very noisy measurements, as typically encountered in drug dosing applications.

An augmented version of the traditional relay feedback experiment is proposed. It aims at producing an input with energy concentrated to a frequency band, corresponding to a certain phase sector of the Nyquist curve of the process to be identified. A non-convex problem is formulated. Sub-optimal, but efficient, algorithms are developed.

This paper describes the design of a robust proportional-integral derivative (PID) controller for propofol infusion in children and presents the results of clinical evaluation of this closed-loop system during endoscopic investigations in children age 6-17. The controller design is based on a set of models that describes the interpatient variability in the response to propofol infusion in the study population. The PID controller is tuned to achieve sufficient robustness margins for the identified uncertainty. 108 children were enrolled in this study, and anesthesia was closed-loop controlled in 102 of these cases. Clinical evaluation of the system shows that closed-loop control of both induction and maintenance of anesthesia in children based on the WAVCNS index as a measure of clinical effect is feasible. A robustly tuned PID controller can accommodate the interpatient variability in children and spontaneous breathing can be maintained in most subjects.

Background During closed-loop control, a drug infusion is continually adjusted according to a measure of clinical effect (e.g., an electroencephalographic depth of hypnosis (DoH) index). Inconsistency in population-derived pediatric pharmacokinetic/pharmacodynamic models and the large interpatient variability observed in children suggest a role for closed-loop control in optimizing the administration of intravenous anesthesia. Objective To clinically evaluate a robustly tuned system for closed-loop control of the induction and maintenance of propofol anesthesia in children undergoing gastrointestinal endoscopy. Methods One hundred and eight children, aged 6-17, ASA I-II, were enrolled. Prior to induction of anesthesia, NeuroSENSE™ sensors were applied to obtain the WAVCNS DoH index. An intravenous cannula was inserted and lidocaine (0.5 mg·kg(-1) ) administered. Remifentanil was administered as a bolus (0.5 μg·kg(-1) ), followed by continuous infusion (0.03 μg·kg(-1) ·min(-1) ). The propofol infusion was closed-loop controlled throughout induction and maintenance of anesthesia, using WAVCNS as feedback. Results Anesthesia was closed-loop controlled in 102 cases. The system achieved and maintained an adequate DoH without manual adjustment in 87/102 (85%) cases. Induction of anesthesia (to WAVCNS ≤ 60) was completed in median 3.8 min (interquartile range (IQR) 3.1-5.0), culminating in a propofol effect-site concentration (Ce ) of median 3.5 μg·ml(-1) (IQR 2.7-4.5). During maintenance of anesthesia, WAVCNS was measured within 10 units of the target for median 89% (IQR 79-96) of the time. Spontaneous breathing required no manual intervention in 91/102 (89%) cases. Conclusions A robust closed-loop system can provide effective propofol administration during induction and maintenance of anesthesia in children. Wide variation in the calculated Ce highlights the limitation of open-loop regimes based on pharmacokinetic/pharmacodynamic models.

This paper proposes an individualized approach to closed-loop control of depth of hypnosis during propofol anesthesia. The novelty of the paper lies in the individualization of the controller at the end of the induction phase of anesthesia, based on a patient model identified from the dose-response relationship during induction of anesthesia. The proposed approach is shown to be superior to administration of propofol based on population-based infusion schemes tailored to individual patients. This approach has the potential to outperform fully adaptive approaches in regards to controller robustness against measurement variability due to surgical stimulation. To streamline controller synthesis, two output filters were introduced (inverting the Hill dose-response model and the linear time-invariant sensor model), which yield a close-to-linear representation of the system dynamics when used with a compartmental patient model. These filters are especially useful during the induction phase of anesthesia in which a nonlinear dose-response relationship complicates the design of an appropriate controller. The proposed approach was evaluated in simulation on pharmacokinetic and pharmacodynamic models of 44 patients identified from real clinical data. A model of the NeuroSense, a hypnotic depth monitor based on wavelet analysis of EEG, was also included. This monitor is similar to the well-known BIS, but has linear time-invariant dynamics and does not introduce a delay. The proposed scheme was compared with a population-based controller, i.e. a controller only utilizing models based on demographic covariates for its tuning. On average, the proposed approach offered 25 % improvement in disturbance attenuation, measured as the integrated absolute error following a step disturbance. The corresponding standard deviation from the reference was also decreased by 25 %. Results are discussed and possible directions of future work are proposed.

Recently, several control systems for closed-loop anesthesia have been demonstrated both in simulation and clinical studies. A set of performance measures, proposed by Varvel et al., have constituted the standard means of comparing such systems. This paper debates the adequacy of the Varvel measures, as applied to closed-loop anesthesia, and proposes an alternative set of measures. Key features of the proposed measures are: wide acceptance within the control community; reflection of clinical feasibility; separate measures for induction and maintenance of anesthesia; separation of outlier detection and performance evaluation. The proposed measures are descriptive, few, and easy to compute.

The first study with a PID controller based automatic drug delivery system for propofol anesthesia in children is presented. It is shown that a robustly tuned PID controller is capable of delivering safe and adequate anesthesia. The design process of the control system is reviewed. Results are discussed and compared to those of two previous studies in adults.

This paper suggests an extension of an existing, clinically evaluated, closed-loop drug delivery system for hypnotic depth control using propofol. The extension introduces closed-loop administration of the analgesic drug remifentanil, thus forming a multiple input-single output (MISO) control system. Remifentanil acts and is metabolized at a significantly faster time scale than propofol. Direct control of analgesia is hindered by the current absence of a reliable real-time nociception monitor. However, several hypnotic depth monitors respond to nociception. Sudden changes in the measured hypnotic depth are frequently caused by changes in noxious stimulation. The novelty of this work lies in increasing the disturbance rejection bandwidth of the control system for hypnotic depth by directing the high frequency content of its control error to a remifentanil controller. Such a mid-ranging control system was implemented and tuned based on 23 patient models obtained from a previous clinical study and its performance is demonstrated through a simulation study.

This paper proposes a closed-loop propofol admission strategy for depth of hypnosis control in anesthesia. A population-based, robustly tuned controller brings the patient to a desired level of hypnosis. The novelty lies in individualizing the controller once a stable level of hypnosis is reached. This is based on the identified patient parameters and enhances suppression of output disturbances, representing surgical stimuli. The system was evaluated in simulation on models of 44 patients obtained from clinical trials. A large amount of improvement (20 -- 30%) in load suppression performance is obtained by the proposed individualized control.

This paper proposes an intuitive nonlinear lateral control strategy for trajectory tracking in autonomous nonholonomic vehicles. The controller has been implemented and verified in Alice, Team Caltech's contribution to the 2007 DARPA Urban Challenge competition for autonomous motorcars. A kinematic model is derived. The control law is described and analyzed. Results from simulations and field tests are given and evaluated. Finally, the key features of the proposed controller are reviewed, followed by a discussion of some limitations of the proposed strategy.