control of drug administration during monitored anesthesia care

256 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 6, NO. 2, APRIL 2009

Short Papers

Control of Drug Administration During MonitoredAnesthesia Care

Antonello L. G. Caruso, Thomas W. Bouillon,Peter M. Schumacher, Eleonora Zanderigo, and Manfred Morari

Abstract—Monitored anesthesia care (MAC) is increasingly used toprovide patient comfort for diagnostic and minor surgical procedures.The drugs used in this setting can cause profound respiratory depressioneven in the therapeutic concentration range. Titration to effect suffersfrom the difficulty to predict adequate analgesia prior to application ofa stimulus, making titration to a continuously measurable side effect anattractive alternative. Exploiting the fact that respiratory depression andanalgesia occur at similar drug concentrations, we suggest to administeropioids and propofol during MAC using a feedback control system withtranscutaneously measured partial pressures of CO �PtcCO � as thecontrolled variable. To investigate this dosing paradigm, we developeda comprehensive model of human metabolism and cardiorespiratoryregulation, including a compartmental pharmacokinetic and a pharmaco-dynamic model for the fast acting opioid remifentanil. Model simulationsare in good agreement with ventilatory experimental data, both in presenceand absence of drug. Closed-loop simulations show that the controllermaintains a predefined CO target in the face of surgical stimulation andvariable patient sensitivity. It prevents dangerous hypoventilation anddelivers concentrations associated with analgosedation. The proposedcontrol system for MAC could improve clinical practice titrating drug ad-ministration to a surrogate endpoint and actively limiting the occurrenceof hypercapnia/hypoxia.

Note to Practitioners—We describe a system minimizing the risksassociated with the delivery of respiratory depressants to spontaneouslybreathing patients during medical procedures. In this setting, severalfactors can contribute to the occurrence of patient injuries: overdosingleading to profound respiratory depression, especially in the nonsteadystate (bolus administration); inadequate monitoring of physiologicalparameters; delayed or inadequate resuscitation. We propose to monitorthe respiratory gases (O CO � as effective indicators of the patient’sventilatory state and to automatically titrate drug delivery based on thisinformation. We tested the performance of the control system in a soft-ware environment with a comprehensive mathematical model of humanmetabolism and cardiorespiratory regulation. Using a PtcCO setpointof 50 mmHg, the system delivered drug concentrations in the acceptedtherapeutic range for analgesia and prevented the occurrence of severe(transient and steady-state) respiratory depression, coping with interindi-vidual variability. Since the drugs and the hardware necessary for theproposed system are commercially available, developing a medical devicefor the automatic delivery of sedatives/analgesics would be relatively inex-pensive. In our opinion, the safety of monitored anesthesia care, especiallywhen performed by non-anesthesiologists, would be profoundly enhanced.Future research will be directed towards the design of an algorithm forrobust detection of sensor malfunction and the clinical evaluation of thedevice.

Index Terms—Automatic drug dosing, blood gases, pharmacoki-netic-pharmacodynamic modeling, respiratory depression, transcutaneousmonitoring, ventilatory regulation modeling.

Manuscript received July 10, 2007; revised January 07, 2008. First publishedFebruary 27, 2009; current version published April 01, 2009. This paper wasrecommended for publication by Associate Editor M. Zhang and Editor D. Mel-drum upon evaluation of the reviewers’ comments.

A. L. G. Caruso, E. Zanderigo, and M. Morari are with the Automatic ControlLaboratory, ETH Zurich, CH-8092 Zurich, Switzerland (e-mail: [email protected]; [email protected]; [email protected]).

T. W. Bouillon and P. M. Schumacher are with the Department of Anes-thesiology, University Hospital Bern, 3010 Bern, Switzerland (e-mail: [email protected]; [email protected]).

Digital Object Identifier 10.1109/TASE.2008.2009088

I. INTRODUCTION

Sedation techniques are used to provide analgesia and reduceanxiety during diagnostic and minor surgical procedures such asendoscopy, bronchoscopy, extracorporeal shockwave lithotripsy, andwounds/burns debridement [1]. Monitored anesthesia care (MAC) isdefined as a medically controlled state of depressed consciousness thatallows protective reflexes (cardiorespiratory control) to be maintained.The moderate depression of consciousness is intended to facilitate theperformance of the medical procedure, while ensuring patient comfortand cooperation. The sedated patient retains the ability to breatheautonomously and to protect his airways; depending on the depth ofsedation, he can respond to verbal commands and tactile stimulationwith different degrees of purposefulness [2].

Drugs used for MAC are propofol, benzodiazepines or opioids, allof which are respiratory depressants. The magnitude of this effect de-pends on dosing history (rate of administration, cumulation, coadmin-istration of other drugs) and individual sensitivity to the drug(s). Drugcombinations, high doses and/or rapid administration rates can dan-gerously blunt the respiratory drive and lead to serious cardiorespira-tory depression in both adults and children [3]–[5]. Hypoxia, poten-tially progressing towards cardiocirculatory arrest is the most fearedconsequence. Oxygenation is usually supported via provision of sup-plemental oxygen. However, sufficient oxygenation does not imply ad-equate ventilation and, therefore, hypercarbia and respiratory acidosismay still develop.

Serious injuries and cardiorespiratory events associated with drugoverdosing during MAC have significant social and economic reper-cussions. A recent survey of surgical anesthesia malpractice claimsover the years 1990–2002 concluded that monitored anesthesia carehas the highest proportion of claims for death/permanent brain damage(MAC: 41%; general anesthesia: 37%; regional anesthesia: 21%) andthe highest median payment to the plaintiff (MAC: 159 kUSD; GA:140 kUSD; RA: 127 kUSD) [6]. The survey identified oversedationleading to respiratory depression as the main mechanism of patientinjuries during MAC and suggested improved monitoring throughcapnography and vigilance to reduce morbidity.

The pronounced interindividual variability of drug sensitivity and thechanging surgical stimulus mandate the continuous evaluation of thepharmacologic effects and the individualization of drug delivery. Thegoal is to optimize the desired effects (i.e., analgesia, sedation, and re-duction of anxiety and agitation), while minimizing the occurrence ofadverse effects (cardiorespiratory depression) [7]. Titration to effect iscompounded by the lack of a preemptive indicator of analgesia and se-dation. In fact, the therapeutic effects of MAC are difficult to quantify;analgesia, for instance, can only be assessed after the patient has beenexposed to a noxious stimulus. It has been suggested to use EEG-de-rived indicators such as the Bispectral Index (BIS, Aspect Medical Sys-tems, MA) to provide a continuous measurement of the desired effects,however, EEG-derived parameters display pronounced fluctuations inmoderately sedated patients and are insensitive to opioids in the thera-peutic concentration range [8]–[10]. Clinical sedation scales (e.g., theVAS scale, the OASS scale) are user-dependent classifications and arenot automated. End-expiratory capnography delivers false low readingsduring shallow breathing and partial airway obstruction and it is diffi-cult to assess without a tight fitting, low dead-space mask. A simple,objective, and robust measure of the therapeutic effects is therefore notavailable [11]. Titration to side effect as an alternative dosing paradigm

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 6, NO. 2, APRIL 2009 257

so far suffered from the lack of a fast and artifact resistant sensor for res-piratory depression. Apnea is not rapidly detected by MAC providerswithout continuous monitoring of �� [12]. Recognition of apnea orhypoventilation in patients who receive supplemental oxygen may alsobe delayed if oxygen saturation �� alone is monitored [13].

We advocate the combined use of transcutaneous �� tension�� and �� for respiratory monitoring during MAC. The re-liability of �� readings is independent of airway status and pulseoximetry provides information on the adequacy of peripheral oxygena-tion. A device combining transcutaneous �� and �� sensinghas been recently introduced into anesthetic practice. Its favorabledynamic properties �� enable it to detect the onset of apnea significantly faster than pulseoximetry alone [14]–[16]. Beyond serving as a monitor for patientsafety, such a device could also provide an objective, continuouslyaccessible and therapeutically meaningful surrogate endpoint for theautomatic titration of opioids and propofol during sedation. In fact,the desired and undesired effects are mediated by the same receptors,therefore drug induced respiratory depression always correlates withanalgosedation, and vice versa [17] (with the exception of opioid tol-erant subjects, for whom this remains to be established). Mu receptorsin the brainstem and the thalamus mediate both the analgesic and therespiratory depressant effect of highly potent opioids [18], [19] sothat the two effects share important pharmacodynamic characteristics[20] and exhibit similar plasma-effect site equilibration properties[21], [22]. Benzodiazepines and propofol exert their sedative effectat gamma-aminobutyric acid (GABA) receptors, that have also beenshown to induce respiratory inhibition [19]. Since the concentration-ef-fect relationships for the desired and undesired effect are similar, webelieve that deliberately targeting moderate levels of hypercapnia is aviable approach for MAC. The degree of hypercapnia can be selectedby the care provider within a safe range (45–60 mmHg) and serves asthe setpoint of a feedback infusion system. Such a system would bothsimplify the titration task and protect against lapses caused by lack ofvigilance in a highly dynamic environment.

Aim of this work, is to implement the proposed dosing paradigminto a feedback control system, i.e., to design a system for the auto-matic delivery of monitored anesthesia care based on transcutaneous�� and �� monitoring. The system should achieve and maintaindrug concentrations corresponding to adequate sedation and analgesia,avoiding marked respiratory depression. The present study focuses onproof of concept in a software environment, using remifentanil, a po-tent, fast-acting opioid. In particular, we will:

i) develop a comprehensive model of human respiratory controlthat can replicate experimental ventilatory responses to��,��

challenges;ii) integrate the ventilatory model with the pharmacokinetics (PK)

and pharmacodynamics (PD) of remifentanil, specifically toachieve a suitable quantitative description of the respiratorydepressant effect of the opioid (= virtual patient);

iii) design a control strategy for the automatic performance ofsedation that individualizes drug delivery, manages surgicalstimulation, and prevents the occurrence of severe respiratorydepression;

iv) analyze the performance of the system adopting the virtual pa-tient as a substrate for simulations.

The manuscript is structured as follows. First, the components of thephysiological model are presented: the metabolic gas-exchange system,the ventilatory control system, the remifentanil PKPD model, the�� model. Thereafter the control strategy for the automatic deliveryof monitored anesthesia care is described. Finally, simulation results ofthe physiological model and the feedback sedation scheme are presentedand discussed. Preliminary results were reported in [23] and [24].

Fig. 1. The metabolic model and its relationship to the ventilatory and cardio-vascular control systems. Model inputs are the metabolic rates of oxygen con-sumption/carbon dioxide production (not shown) and the partial pressures of therespiratory gases in inspired air �� . � , � � �, b, t, tc: arte-rial, cerebral, tissue, transcutaneous partial pressure of carbon dioxide (equiv-alent notation for oxygen). Shaded blocks depict the structure of the remifen-tanil PKPD model. �, � , and � are the infusion rate, plasma, and effect siteconcentrations of the opioid, respectively. E: pharmacologic depressant effecton ventilation; : baseline ventilation; : minute ventilation; : alveolarventilation; Q: cardiac output. The dead space indicates the fraction of minuteventilation that does not participate in gas exchange � � �� .

II. METHODS

In order to investigate the feasibility of the proposed drug deliveryparadigm, a comprehensive yet parsimonious respiratory model is de-veloped in the first part of this section. Several studies are availablein the literature describing isolated respiratory mechanisms [25]–[27],or the influence of anesthetics on those [18], [20]. Few attempts havebeen made at modeling the PD of propofol and opioid induced respi-ratory depression [17], [28], particularly in the non-steady state [29],[30]. No model is currently available, however, providing an integratedpicture of human metabolism, gas exchange and transport, cardiorespi-ratory regulation, drug disposition, and the respiratory effects of painfulstimuli and sedative drugs. In the following, we propose a consistentunified framework of regulatory physiology and respiratory drug ef-fects, that will later be used as a test bed for control of drug infusionduring MAC. The control algorithm for the automatic delivery of se-dation is described in the second part of this section.

A. The Gas Exchange System

To describe human metabolism and ventilatory gas exchange, weadopted a previously published model that characterizes the disposi-tion of oxygen and carbon dioxide in the tissues [31], [32]. The modeldescribes�� and�� storage, exchange and metabolism in three com-partments that represent different body regions: the lungs, the brain andthe other tissues (lumped into one compartment). Mass balance equa-tions for �� and �� are written for each compartment taking into ac-count �� and �� exchange with blood, �� consumption, and ��

production in tissues. The partial pressures of �� and �� in the in-spired air and the metabolic rates constitute the inputs to the model.Local blood flow to the tissues is modulated by a cardiovascular con-trol mechanism that acts to maintain arterial and tissular�� and��

close to the basal values despite changes in inspired air composition andmetabolism. We refer the interested reader to [32] for a detailed descrip-tion of gas exchange and cardiovascular regulation and for the modelequations and parameters. Fig. 1 depicts the structure of the metabolicmodel and its links to the ventilatory control system.


TABLE IPARAMETER VALUES IN THE PATIENT SIMULATOR EQUATIONS

Oxygen and carbon dioxide transport in blood is described with amathematical representation of �� and �� dissociation curves thataccounts for the Bohr and Haldane effects [33]. The agreement withexperimental data measured in whole blood (�� , �� at 37 ��) is adequate between0–120 mmHg �� and 0–80 mmHg �� . The dissociation curve ex-pressions disregard the amount of oxygen carried in dissolved form.This term becomes significant when breathing oxygen enriched air, acondition that is relevant to our study. Therefore, we modify the orig-inal equation describing �� molar concentration in blood as a functionof �� and �� ([33, eq. (4)]) as follows:

��

��

��

� � ��

��

� �� (1)

where the �� term represents the contribution of dissolvedoxygen (in accord with Henry’s law) to total blood content and ��is the oxygen solubility in blood. Linear least-squares fit values ofthe parameters in (1) are given in Table I. �� has a physiologicalmeaning since it corresponds to the molar oxygen concentrationachieved in blood with 100% saturated hemoglobin. Because the max-imum �� capacity of human Hb is �� andthe typical Hb content in blood is 150 g/l, �� is assigned a value of8.848 mmol/l. The modified dissociation curve equation matches wellthe experimental data over the whole �� range that is meaningful forthe present investigation (0–700 mmHg).

B. Ventilatory Regulation

Multiple studies are available in the literature investigating thehuman hypercarbic and hypoxic ventilatory drive [26], [34], [35],and the regulatory activity of chemoreceptors [25], [36]. However,most experimental paradigms are geared towards isolating physiologicphenomena in order to obtain reliable and interpretable data of therespiratory subsystems. Consequently, they are not suitable for simu-lating the global ventilatory response to oxygen and carbon dioxide.We propose to integrate the available knowledge into a parsimoniousinteraction model to achieve a unified picture of human respiratorycontrol under different clinically relevant conditions (hypercapnia andhypoxia being the most significant).

Respiratory control in man predominantly depends on chemicalsignals stimulating the peripheral and central chemoreceptors [25].Peripheral chemosensitive areas are located at the bifurcation of thecommon carotid arteries and respond to changes in arterial �� and�� . The central chemoreceptors lie on the anterolateral surface ofthe medulla and respond primarily to changes in cerebrospinal fluid

�� partial pressure [19]. According to experimental results, the twochemoreflex mechanisms exert an additive effect on ventilation [26],therefore the ventilatory response to �� and �� can be partitionedinto a peripheral and a central dynamic component [25], [27]. We usethe following equation:

!"� � !"��#� � �$ (2)

where !"�� is baseline minute ventilation, C and P are the central andperipheral fractional contributions to minute ventilation, respectively.Under basal conditions � � ��% and � � ��, in accordance withthe clinical results reported in [25], [27], and [36] indicating that theperipheral chemoreceptors contribute approximately 30% to the totalventilatory drive under normoxia.

We describe the output of the central chemoreflex loop, C, with thefollowing equation:

��

� � � � & #�� '�� ($ (3)

where�� , the arterial partial pressure of carbon dioxide, representsthe stimulus to the central chemoreceptors. The parameter & is the�� sensitivity, B the apneic threshold (or the extrapolated pressureof the steady-state�� -ventilation response curve at zero ventilation[27]). � and' are the mechanism time constant and central time delay(lung-receptor transit time), respectively.

We propose to describe the contribution of the peripheral chemore-flex mechanism to minute ventilation as follows:

� ��

� � � �&

!"� � !"�� !"� (4)

!"� �)�

#�� ' ��*$(5)

!"�� )�� #�� ' �� ($ (6)

!"� �)� !"� �� !"�� (7)

� is the output of the peripheral chemoreflex loop; it comprises anoxygen-dependent term � !"� �, a carbon dioxide-dependent term� !"�� , and an �� multiplicative interaction factor � !"��. Theparameters & and � in (4) are the �� sensitivity and the compo-nent time constant, respectively. The peripheral ventilatory response to�� under isocapnic conditions is described with the expression in (5),where �� ' � is the arterial stimulus delayed by the peripheraltransport time ' [34], [35]. The peripheral drive to carbon dioxide islinearly dependent on the difference between �� and the apneicthreshold B [(6)] [25], [27]. The �� interaction [(7)] accountsfor the ventilatory response under hypoxia being greater than the sumof the response to be expected from the rise in �� and the fall in�� if considered separately, as reported in [19] and [25].


Least-squares estimates of the ventilatory model parameters are ingood agreement with clinical experimental data regarding chemore-ceptive gains, thresholds, time constants, and time delays: �� , � � �� , �� and �� ,�� and �� [25], [37], [38]; � � �� [34].All values of the parameters in (2)–(7) are listed in Table I.

For the sake of mimicking intraoperative noxious stimulation, weincorporate into the physiological model a description of human venti-latory response to pain. The effect of painful stimulation on the humanbody is modeled as a 10% increase in �� metabolism and a 10%decrease of the apneic threshold. As a result, pain determines a 15%increase in minute ventilation compared to baseline. These effects re-flect the experimental observations on the ventilatory response to painin man reported in the literature [39]–[41].

C. Transcutaneous �� Sensing

The proposed dosing paradigm includes titration of drug accordingto �� . Therefore, the physiological model must incorporate an ex-plicit description of the transcutaneous �� signal. In a previous study[14], we evaluated the dynamic properties of the SenTec V-Sign Sensor(SenTec AG, Switzerland), a Severinghaus-type device for the transcu-taneous monitoring of the partial pressure of carbon dioxide �� .It was concluded that the �� response of the sensor can be relatedto end-expiratory �� by means of a two compartment model with�� ! � �� , and a 20 s timelag. This �� model is included into the respiratory simulator totest the feasibility of the proposed sedation system.

D. PKPD Modeling

In order to describe the respiratory depressant effect of remifentanil,a pharmacokinetic/pharmacodynamic model is added to the physiolog-ical model of gas exchange and cardiorespiratory control. RemifentanilPK is described by means of a customary three-compartment mammil-lary model; the PK model is augmented with a first-order transfer tothe effect site (link model) [42]. PK parameters are listed in Table I.

In a recent study [30], Magosso et al. simulated the ventilatory re-sponse to the opioid fentanyl, proposing a detailed description of drugeffects on the different mechanisms that regulate ventilation. However,we believe this level of detail is not sufficiently supported by the liter-ature. In fact, it would be nearly impossible to design an experimentalparadigm in man geared towards simultaneously determining �� and�� dynamics and drug PD at each regulatory subsystem. Therefore,we opt for a more parsimonious structure, expressing the effect as aconcentration-dependent depression of the total ventilatory drive. PDmodeling is performed using the fractional sigmoidal "� model forthe effect on minute ventilation. The choice of a fractional "� modelover other commonly used PD models (such as the power model) is jus-tified by its simplicity and accuracy in reproducing experimental results[29]. Effect quantitation is achieved through

" � "� ��

�� # ��(8)

where " is the minute ventilation as function of the drug concentration,"� the baseline value in absence of drug �� the drug concentrationcausing 50% depression of minute ventilation under isohypercapnia,� the sigmoidicity (Hill) factor. �� is set to a value of 1 ng/ml inagreement with the literature ([29]: 0.92 ng/ml, [22]: 1.12 ng/ml, [43]:1.17 ng/ml); � � �� [29]. Since " is normalized to baseline venti-lation (that is, "� � �), the dynamic effect of the drug is determinedwith a minimal number of parameters.

Fig. 2. Configuration for the closed-loop control of sedation delivery. I: druginfusion rate;� : sensor measurement of transcutaneous�� partial pres-sure; �� : estimated measurement; � : control setpoint. The overridesystem enforces the �� , � , � thresholds to maximize the safety ofopioid administration.

E. Control Strategy

Aim of the control algorithm, is to manage drug administration forinduction of sedation and maintenance of the �� setpoint definedby the anesthesiologist. Adequate management of intraoperativepainful stimuli, surgical disturbances, and interpatient variability isrequired. An endpoint of 50 mmHg is selected for paradigm validationbecause in volunteers it corresponds to mild respiratory depression andplasma concentrations well in the analgesic range [44]. The controlalgorithm must fulfil the following conservative constraints to ensurepatient safety: $ �� %; remifentanil ��, �� !. Incase the thresholds are exceeded (e.g., for exceptional drug sensitivity),a safety override stops the automatic infusion and reverts the responsi-bility of dosing to the care provider, still providing information aboutplasma and effect compartment concentrations.

In the region of interest for application to MAC, the�� responseof the model to stepwise changes in �� is approximately linear andof first order, so the requirements in terms of controller sophisticationare modest [45]. Therefore, a proportional-integral (PI) control strategyis selected. A two-degree-of-freedom control structure is employed toimprove tracking performance while ensuring rapid suppression of dis-turbance effects. Further considerations on the choice of this controlalgorithm over more sophisticated ones are included in the Discussionand Conclusions section.

F. Kalman Filtering

A state observer is included in the sedation system to perform fil-tering of the �� signal. As observer the Kalman filter is selectedbecause we do not rule out the presence of stochastic noise in the�� signal. The positioning of the Kalman filter within the controlloop and the configuration of the MAC delivery system are depicted inFig. 2.

The use of Kalman �� estimates is also intended to assistin sensor failure/disconnection detection [46]. For instance, duringthe medical procedure, it may happen that the sensing element isdisplaced from its measurement site on the patient. The �� mea-surements would then rapidly approach a value of zero because the�� &�� %. A significant mismatchbetween the sensed and the estimated �� would be observed. Inthe sedation system, excessive estimation residuals are interpreted asbeing indicative of a fault. We establish that the system detects a sensorfailure/disconnection if the sensor output undershoots the estimatedvalue by 7 mmHg or more. Following the detection of a fault, thesystem rejects the sensor output. The prediction at each time step isthen based on the previous estimate and the current control commandonly. In the clinical implementation a warning message would beissued at this stage, drawing the attention of the care provider. The


Fig. 3. Left diagrams—the ventilatory response to hypercapnia measured in healthy volunteers is plotted versus time (top: symbols are single minute ventilationmeasurements reported in [27]; middle and bottom: symbols are �� [25]). The hypercapnic stimulus is applied under hyperoxic, euoxic and hypoxicconditions (� � �, 100, 53 mmHg, respectively). Right diagrams—simulated minute ventilation versus time (thick line) in response to a square changein � partial pressure (thin line). The simulations are performed under � and � conditions identical to those of the clinical experiments. Baselineminute ventilation is 12 l/min under hyperoxia and 8.7 l/min under normoxia and hypoxia in accordance with the experimental observations.

system does not discard measurement information when the sensorreading is higher than the estimated value, since false high readingsrevert the virtual patient to a safe state decreasing the infusion rate.

III. RESULTS

Simulation results relative to the ventilatory response to �� chal-lenges, the ventilatory response to remifentanil administration, and thefeedback control of drug delivery are described in the following.

A. Ventilatory Response to Oxygen and Carbon Dioxide

The ventilatory response of the model to a square wave change ofend-tidal �� partial pressure for different oxygen concentrations isshown in Fig. 3 (hyperoxia: �� , top diagram; eu-oxia: �� , middle; hypoxia: �� ,bottom). Simulation results are in good agreement with the averageresponses measured in volunteers reported in [27] (top diagram) and

[25] (middle, bottom). In accordance with respiratory physiology, theresponse of the model to a hypercapnic challenge is stronger underhypoxia (approximately a threefold increase of baseline ventilation),while hyperoxia has a mild respiratory depressant effect [19]. Undernormocapnia and hypoxia an increase in resting ventilation is produceddue to the effect of low oxygen tension on ventilatory regulation [26],[27]; this effect is visible in the model output but it cannot be clearlydiscerned in the experimental results (middle, bottom diagrams).

The clinical data show a fast initial increase of minute ventilation inresponse to the �� change, followed by the contribution of a slowerrespiratory component. This biphasic behavior is typical of human res-piratory patterns and it is produced by the different time constant andtime delay (lung-receptor transit time) that characterize the response ofthe peripheral and the central chemoreceptors, respectively, the fast andthe slow ventilatory mechanism [19], [25], [27]. In the model, the twochemoreflex loops determine additively minute ventilation [(2)]; theircontributions generate the ventilatory patterns displayed in Fig. 3. Theon- and off-transients following the end-expiratory�� step challenge


Fig. 4. The open-loop arterial � response to stepwise changes ofremifentanil plasma concentration is displayed versus time for two differentsubjects. Filled circles: remifentanil plasma concentration �� measurements;empty circles: arterial carbon dioxide tension �� measurements. Theexperimental data are compared with simulation results. Solid lines: �input to the model that reproduces the experimental administration schedule;dashed lines: � resulting from model simulations with population PDparameters (� � � ��, � � ��, � �� ); dottedlines: � from simulations with individual fit of PD parameters (top:� � �� , � � ��, � � �� ; bottom: � � �� ,� � ��, � �� ).

present the same dynamics, in agreement with the clinical findings re-ported in [27] and [47].

B. Drug Induced Ventilatory Depression

Fig. 4 displays the ventilatory depressant effect of remifentanil ontwo healthy volunteers in terms of arterial �� changes. The experi-mental data reported here is from a clinical study aimed at identifyingthe inhibitory effect of remifentanil on ventilation [29]. Remifentanilwas administered in ascending steps via target-controlled infusion untilend-tidal �� exceeded 65 mmHg and/or apnea periods of more than60 s occurred. Thereafter the remifentanil concentration was allowed todecrease to 1 ng/ml. During the infusion, arterial blood samples weredrawn to determine remifentanil plasma concentrations and �� .The experimental conditions were reproduced in model simulations. Asexpected, open-loop simulation results based on population PD param-eters do not always adequately describe the data due to the pronouncedinterindividual variability (Fig. 4, top). Since individualization of theparameters leads to an adequate fit, the model structure is acceptable.

C. Control of Drug Infusion During MAC

In order to validate the proposed dosing paradigm for sedation,we simulated titration to and maintenance of a target �� usingthe physiological model described in the above as a virtual patient.

Closed-loop results for a �� target of 50 mmHg and ��

�� in three differentlysensitive virtual patients (highly sensitive, averagely sensitive, insen-sitive) are displayed in Fig. 5. To mimic the nuisance effects that oftenoccur in the operating theater during MAC delivery, the followingdisturbances were reproduced in the simulation studies:

i) a painful stimulus at � � �� ;ii) a generic surgical disturbance at � � �� that causes an

abrupt increase of 4 mmHg in arterial �� (to mimic for in-stance the effect of tourniquet release);

iii) the disconnection of the sensor at � � �� .The �� versus time diagram in Fig. 5 shows that the control algo-rithm adjusts drug dosing to track the reference signal, to reject thedisturbances and to individualize drug delivery depending on the spe-cific sensitivity of the virtual patient. Remifentanil plasma concentra-tions delivered by the controller (1–2.5 ng/ml) lie within the analgesicand therapeutic range for sedation [44]. When sensor disconnection oc-curs, the �� signal quickly drops to zero; thereafter at each timestep the Kalman filter outputs a �� estimate based on the previousestimate and the current control command only (no estimate updatewith measurement information since the sensor is detached). Minuteventilation is displayed as another indicator of drug induced respira-tory depression. It remains at safe levels, both transiently and at steadystate. Throughout the entire simulated procedure the hypoxic overrideremains inactive since blood oxygenation stays always above the 93%threshold.

IV. DISCUSSION AND CONCLUSION

The ability of opioids to provide adequate analgesia is limited bythe ventilatory depression associated with overdosing in spontaneouslybreathing patients. Therefore, quantitation of drug induced ventilatorydepression is a pharmacokinetic-pharmacodynamic problem relevantto the practice of anesthesia and in particular to the delivery of moni-tored anesthesia care.

Aim of this study, was to address the problem of drug delivery inthe spontaneously breathing patient and to provide a solution that en-hances safety in clinical practice. Dosing during MAC is critical be-cause there is no direct, objective measurement of the therapeutic ef-fect and the drug induced adverse effects are life threatening. We pre-sented an innovative dosing strategy that entails the measurement ofthe pharmacologic side effect rather than the evaluation of the thera-peutic effect to provide guidance for the automatic control of drug de-livery (surrogate endpoint based dosing paradigm). More specifically,we suggested titrating the administration of sedatives and analgesicsto the individual sensitivity based on transcutaneous measurements of�� . We demonstrated the dosing paradigm by means of computersimulations. The results in Fig. 5 show that the control algorithm indi-vidualizes drug infusion, titrates to effect and delivers concentrationswithin the analgesic range. Closed-loop control may be effective androbust to intraoperative disturbances, individual drug sensitivity, andsensor disconnection. Improved safety, reliable and adequate therapy,workload reduction for the care providers, cost saving in terms of drugs,and manpower would be the most prominent results of implementingthe proposed dosing paradigm into a therapeutic device for use duringMAC. The system has, therefore, the potential to be converted into acommercial healthcare device because it may fulfil the clinical demandfor improved sedation care and the technology of the single compo-nents (sensor, actuator, processing and control unit) is available andrelatively inexpensive compared to anesthesia workstations. However,a second, independent sensor (e.g., a nasal thermistor) is needed to de-tect ongoing breathing and identify possible single fault conditions.

It is relevant to mention that the dosing paradigm object of investi-gation was here exemplified selecting �� as the feedback signal


Fig. 5. Simulated closed-loop induction and maintenance of sedation �� . Model results in terms of estimated , measured , remifen-tanil plasma concentration, and normalized minute ventilation are displayed. The reference signal is changed from baseline to 50 mmHg at time �� , thereby activating the controller and initiating drug infusion. A painful stimulation and a generic surgical disturbance occur for 10 min at time � �� and � �� , respectively. At � �� signal loss occurs (reproducing, e.g., the detachment of the sensor). The sequence of events is simulatedfor three different levels of drug sensitivity: high sensitivity (� � �� , dotted line), average sensitivity (� � � ��, solid line), low sensitivity(� � �� , dash-dotted line).

and remifentanil as the anesthetic drug. However, in principle, the par-adigm can be applied to other ventilatory measurements (e.g., ��via end-expiratory capnography) and/or other sedative/analgesic drugswith respiratory depressant side effects (for instance, opioids such asfentanyl and alfentanil; propofol; combinations of drugs).

The physiological model presented herein combines several isolatedpharmacological and physiological aspects of respiratory regulationand drug induced respiratory depression under various conditions.Several studies in the literature address the effect of chemical stimuliand anesthetics on specific respiratory and metabolic subsystems. Ourwork integrates and thereby expands that knowledge providing, for thefirst time, a unified picture of respiratory regulation and drug effectson breathing under different clinically relevant conditions. The modelserves as a simulator/test bed for both drug tolerability and controlissues under non-steady-state conditions.

The patient simulator expresses drug induced respiratory depressionas a concentration-dependent effect on minute ventilation. It does notdescribe the pharmacologic effects on tidal volume and respiratory rateseparately, although it has been shown that their dynamics are different[48]. Modeling these two ventilatory components may improve the ca-pability to predict apneic events and is, therefore, going to be the sub-ject of further investigation.

The main objective of this work was to investigate the feasibility ofemploying a surrogate measurement of analgesia for automatic drugdosing during sedation. As it is often the case for physiological sys-tems, the most critical decision for the success of the control scheme isthe right choice of the controlled and actuated variable(s), and not thelevel of sophistication of the algorithm. The specific control strategywe adopted is undoubtedly simple, but the simulation results in Fig. 5show that a higher degree of complexity is not required to deliver con-centrations in the analgesic range even in the presence of pronouncedinterindividual physiological variability and the occurrence of variousdisturbances. We strongly suspect a more complex control algorithmwould not contribute substantially to the theoretical or clinical rele-vance of our results. In fact, more sophisticated control strategies suchas model predictive control (MPC) [49] and cascade control (being�� and �� the controlled variables) were tested in the sim-ulation environment, but did not deliver a significant improvement inthe performance of the system. Therefore, we concluded that the use ofmore sophisticated control algorithms is not justified. From our trackrecord in collaborations with anesthesiologists, we also believe thatsimpler control structures are considerably more likely to be acceptedand successfully introduced in the clinical practice.

This study modeled the effect of remifentanil on breathing and didnot consider coadministration of other drugs. Polypharmacy of seda-


tive/analgesic drugs can cause severe respiratory effects due to the phar-macologic interaction of the drugs. However, coadministration of an-other respiratory depressant would lead to more pronounced hyper-capnia and, therefore, reduce administration of the automatically ad-ministered drug, thereby protecting the patient. This hypothesis will betested in a prospective clinical study that aims at validating the pro-posed dosing paradigm for MAC in the clinical practice.

Future developments of the research also include the design of arobust algorithm for detection of single fault conditions of the sensorand/or the control system. A hardware implementation of the sedationsystem will be pursued for the performance of the clinical study di-rected at confirming the validity of our findings in patients. The majorchallenges there will be posed by the �� sensor and the pronouncedpatient variability, rather than by the implementation of the controller.

ACKNOWLEDGMENT

The authors would like to thank Dr. C. Jones (Automatic ControlLaboratory, ETH Zurich, Zurich, Switzerland) for his valuable collab-oration during the study.

REFERENCES

[1] M. M. Sa Rego, M. F. Watcha, and P. F. White, “The changing role ofmonitored anesthesia care in the ambulatory setting,” Anesth. Analg.,vol. 85, pp. 1020–1036, 1997.

[2] D. Ghisi, A. Fanelli, M. Tosi, M. Nuzzi, and G. Fanelli, “Monitoredanesthesia care,” Minerva Anestesiol., vol. 71, no. 9, pp. 533–538,2005.

[3] J. B. Arrowsmith, B. B. Gerstman, D. E. Fleischer, and S. B. Ben-jamin, “Results from the American Society for Gastrointestinal En-doscopy/U.S. Food and Drug Administration collaborative study oncomplication rates and drug use during gastrointestinal endoscopy,”Gastrointest. Endosc., vol. 37, pp. 421–427, 1991.

[4] C. J. Cote, D. A. Notterman, H. W. Karl, J. A. Weinberg, and C. Mc-Closkey, “Adverse sedation events in pediatrics: A critical incidentanalysis of contributing factors,” Pediatrics, vol. 105, pp. 805–814,2000.

[5] J. B. Gross et al., “Practice guidelines for sedation and analgesia bynon-anesthesiologists,” Anesthesiology, vol. 84, no. 2, pp. 459–471,1996.

[6] S. M. Bhananker, K. L. Posner, F. W. Cheney, R. A. Caplan, L. A.Lee, and K. B. Domino, “Injury and liability associated with monitedanesthesia care,” Anesthesiology, vol. 104, no. 2, pp. 228–234, 2006.

[7] , J. E. Charlton, Ed., Core Curriculum for Professional Education inPain. Seattle, WA: IASP Press, 2005.

[8] L. M. Drake, S. C. Chen, and D. K. Rex, “Efficacy of bispectralmonitoring as an adjunct to nurse-administered propofol sedation forcolonoscopy: A randomized controlled trial,” Amer. J. Gastroenterol.,vol. 101, no. 9, pp. 2003–2007, 2006.

[9] J. W. Johansen, “Update on bispectral index monitoring,” Best Pract.Res. Clin. Anaesthesiol., vol. 20, no. 1, pp. 81–99, 2006.

[10] J. M. LeBlanc, J. F. Dasta, and S. L. Kane-Gill, “Role of the bispectralindex in sedation monitoring in the ICU,” Ann. Pharmacother., vol. 40,no. 3, pp. 490–500, 2006.

[11] P. H. Tonner, A. Paris, and J. Scholz, “Monitoring consciousness inintensive care medicine,” Best Pract. Res. Clin. Anaesthesiol., vol. 20,no. 1, pp. 191–200, 2006.

[12] R. G. Soto, E. S. Fu, H. Vila, Jr., and R. V. Miguel, “Capnography accu-rately detects apnea during monitored anesthesia care,” Anesth. Analg.,vol. 99, pp. 379–382, 2004.

[13] J. B. Downs, “Has oxygen administration delayed appropriate respira-tory care? Fallacies regarding oxygen therapy,” Respir. Care, vol. 48,pp. 611–620, 2003.

[14] P. Schumacher, A. Caruso, V. Hartwich, M. Luginbuehl, and T.Bouillon, “Dynamic properties of a new transcutaneous �� sensor:Fast enough for early detection of apnea?,” Anesthesiology, vol. 105,p. A477, 2006.

[15] S. Kocher, R. Rohling, and A. Tschupp, “Performance of a digital� �� ear sensor,” J. Clin. Monit. Comput., vol. 18, pp. 75–79,2004.

[16] A. Mizushima, A. Nakamura, S. Katashima, Y. Kawauchi, and Y.Kamiyama, “Clinical evaluation of a new combined transcutaneouscarbon dioxide tension and pulse oximetry sensor in adult patientsundergoing laparoscopic surgery,” Anesthesiology, vol. 101, p. A563,2004.

[17] L. H. Mildh, H. Scheinin, and O. A. Kirvelä, “The concentration-effectrelationship of the respiratory depressant effects of alfentanil and fen-tanyl,” Anesth. Analg., vol. 93, pp. 939–946, 2001.

[18] M. A. Czapla and J. E. Zadina, “Reduced suppression of�� -inducedventilatory stimulation by endomorphins relative to morphine,” BrainRes., vol. 1059, no. 2, pp. 159–166, 2005.

[19] J. F. Nunn, Nunn’s Applied Respiratory Physiology. Stoneham, MA:Butterworth-Heinemann, 1993.

[20] A. Dahan, R. Romberg, L. Teppema, E. Sarton, H. Bijl, and E. Olofsen,“Simultaneous measurement and integrated analysis of analgesia andrespiration after an intravenous morphine infusion,” Anesthesiology,vol. 101, pp. 1201–1209, 2004.

[21] P. S. Glass, D. Hardman, Y. Kamiyama, T. J. Quill, G. Marton, K. H.Donn, C. M. Grosse, and D. Hermann, “Preliminary pharmacokineticsand pharmacodynamics of an ultra-short-acting opioid: Remifentanil,”Anesth. Analg., vol. 77, no. 5, pp. 1031–1040, 1993.

[22] H. D. Babenco, P. F. Conard, and J. B. Gross, “The pharmacodynamiceffect of a remifentanil bolus on ventilatory control,” Anesthesiology,vol. 92, no. 2, pp. 393–398, 2000.

[23] A. Caruso, T. W. Bouillon, E. Zanderigo, P. M. Schumacher, and M.Luginbuehl, “Closed loop administration of remifentanil for monitoredanesthesia care using � as endpoint,” Anesthesiology, vol. 105, p.A1198, 2006.

[24] E. Zanderigo, A. Caruso, T. Bouillon, M. Luginbuehl, and M. Morari,“Pharmacodynamic modelling of drug-induced ventilatory depressionand automatic drug dosing in conscious sedation,” in Proc. IEEE EMBSAnn. Int. Conf. (EMBC06), 2006, pp. 5029–5032.

[25] J. W. Bellville, B. J. Whipp, R. D. Kaufman, G. D. Swanson, K. A.Aqleh, and D. M. Wiberg, “Central and peripheral chemoreflex loopgain in normal and carotid body-resected subjects,” J. Appl. Physiol.,vol. 46, pp. 843–853, 1979.

[26] D. J. C. Cunningham, P. A. Robbins, and C. B. Wolff, “Integration ofrespiratory responses to changes in alveolar partial pressures of ��and� and in arterial pH,” in Handbook of Physiology, A. P. Fishman,Ed. et al. Bethesda, MD: Amer. Phys. Soc., 1986, pp. 475–528.

[27] A. Dahan, J. Degoede, A. Berkenbosch, and I. C. W. Olievier, “Theinfluence of oxygen on the ventilatory response to carbon dioxide inman,” J. Physiol., vol. 428, pp. 485–499, 1990.

[28] A. Berkenbosch, L. J. Teppema, C. N. Olievier, and A. Dahan, “Influ-ences of morphine on the ventilatory response to isocapnic hypoxia,”Anesthesiology, vol. 86, pp. 1342–1349, 1997.

[29] T. Bouillon, J. Bruhn, L. Radu-Radulescu, C. Andresen, C. Cohane,and S. Shafer, “A model of the ventilatory depressant potency ofremifentanil in the non-steady state,” Anesthesiology, vol. 99, pp.779–787, 2003.

[30] E. Magosso, M. Ursino, and J. H. V. Oostrom, “Opioid-induced res-piratory depression: A mathematical model for fentanyl,” IEEE Trans.Biom. Eng., vol. 51, pp. 1115–1128, 2004.

[31] L. Chiari, G. Avanzolini, and M. Ursino, “A comprehensive simulatorof the human respiratory system: Validation with experimental and sim-ulated data,” Ann. Biomed. Eng., vol. 25, pp. 985–999, 1997.

[32] M. Ursino, E. Magosso, and G. Avanzolini, “An integrated model ofthe human ventilatory control system: The response to hypercapnia,”Clin. Physiol., vol. 21, no. 4, pp. 447–464, 2001.

[33] J. L. Spencer, E. Firouztale, and R. B. Mellins, “Computational ex-pressions for blood oxygen and carbon dioxide concentration,” Ann.Biomed. Eng., vol. 90, no. 5, pp. 1329–1338, 1979.

[34] R. J. van Klaveren and M. Demedts, “A mathematical and physio-logical evaluation of the different hypoxic response models in normalman,” Respir. Physiol., vol. 113, pp. 123–133, 1998.

[35] B. B. Lloyd, M. G. M. Jukes, and D. J. C. Cunningham, “The rela-tion between alveolar oxygen pressure and the respiratory response tocarbon dioxide in man,” Q. J. Exp. Physiol., vol. 43, no. 2, pp. 214–227,1958.

[36] R. Lugliani, B. J. Whipp, C. Seard, and K. Wassermann, “Effect ofbilateral carotid body resection on ventilatory control at rest and duringexercise in man,” N. Engl. J. Med., vol. 285, pp. 1105–1111, 1971.

[37] A. Dahan, D. Nieuwenhuijs, E. Olofsen, E. Sarton, R. Romberg, andL. Tepperna, “Response surface modeling of alfentanil-sevoflurane in-teraction on cardiorespiratory control and bispectral index,” Anesthesi-ology, vol. 94, pp. 982–991, 2001.


[38] D. S. Ward and J. W. Bellville, “Effect of intravenous dopamine onhypercapnic ventilatory response in humans,” J. Appl. Physiol., vol.55, pp. 1418–1425, 1983.

[39] E. I. Eger, W. M. Dolan, W. C. Stevens, R. D. Miller, and W. L. Way,“Surgical stimulation antagonizes the respiratory depression producedby forane,” Anesthesiology, vol. 36, no. 6, pp. 544–549, 1972.

[40] C. J. Glynn, J. W. Lloyd, and S. Folkhard, “Ventilatory response tointractable pain,” Pain, vol. 11, pp. 201–211, 1981.

[41] E. Sarton, A. Dahan, L. Teppema, A. Berkenbosch, M. V. D. Elsen, andJ. V. Kleef, “Influence of acute pain induced by activation of cutaneousnociceptors on ventilatory control,” Anesthesiology, vol. 87, no. 2, pp.289–296, 1997.

[42] C. F. Minto et al., “Influence of age and gender on the pharmacoki-netics and pharmacodynamics of remifentanil: I. Model development,”Anesthesiology, vol. 86, no. 1, pp. 10–23, 1997.

[43] P. S. A. Glass, I. A. Iselin-Chaves, D. Goodman, E. Delong, and D.J. Hermann, “Determination of the potency of remifentanil comparedwith alfentanil using ventilatory depression as the measure of opioideffect,” Anesthesiology, vol. 90, no. 6, pp. 1556–1563, 1999.

[44] M. Luginbuehl, A. Gerber, T. W. Schnider, S. Petersen-Felix, L.Arendt-Nielsen, and M. Curatolo, “Modulation of remifentanil-in-duced analgesia, hyperalgesia, and tolerance by small-dose ketaminein humans,” Anesth. Analg., vol. 96, pp. 726–732, 2003.

[45] K. J. Åström and T. Hägglund, PID Controllers: Theory, Design, andTuning. Research Triangle Park, NC: ISA, 1995.

[46] P. M. Frank, “Fault diagnosis in dynamic systems using analytical andknowledge-based redundancy: A survey and some new results,” Auto-matica, vol. 26, pp. 459–474, 1990.

[47] W. N. Gardner, “The pattern of breathing following step changes ofalveolar partial pressures of carbon dioxide and oxygen in man,” J.Physiol., vol. 300, pp. 55–73, 1980.

[48] J. M. Ansermino, P. Brooks, D. Rosen, C. A. Vandebeek, and C. Re-ichert, “Spontaneous ventilation with remifentanil in children,” Pae-diatr. Anaesth., vol. 15, no. 2, pp. 115–121, 2005.

[49] M. Morari and J. H. Lee, “Model predictive control: Past, present andfuture,” Comp. Chem. Eng., vol. 23, pp. 667–682, 1999.

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