technological approach to patient physiological image - IEEE Xplore

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 12, DECEMBER 2002

Virtual Center for Renal Support: Technological Approach to Patient Physiological Image Manuel Prado*, Student Member, IEEE, Laura Roa, Senior Member, IEEE, Javier Reina-Tosina, Student Member, IEEE, Alfonso Palma, and José Antonio Milán

Abstract—The patient physiological image (PPI) is a novel concept which manages the knowledge of the virtual center for renal support (VCRS), currently being developed by the Biomedical Engineering Group of the University of Seville. PPI is a virtual “replica” of the patient, built by means of a mathematical model, which represents several physiological subsystems of a renal patient. From a technical point of view, PPI is a component-oriented software module based on cutting-edge modeling and simulation technology. This paper provides a methodological and technological approach to the PPI. Computational architecture of PPI-based VCRS is also described. This is a multi-tier and multi-protocol system. Data are managed by several ORDBMS instances. Communications design is based on the virtual private network (VPN) concept. Renal patients have a minimum reliable access to the VCRS through a public switch telephone network—X.25 gateway. Design complies with the universal access requirement, allowing an efficient and inexpensive connection even in rural environments and reducing computational requirements in the patient’s remote access unit. VCRS provides support for renal patients’ healthcare, increasing the quality and quantity of monitored biomedical signals, predicting events as hypotension or low dialysis dose, assisting further to avoid them by an online therapy modification and easing diagnostic tasks. An online therapy adjustment experiment simulation is presented. Finally, the presented system serves as a computational aid for research in renal physiology. This is achieved by an open and reusable modeling and simulation architecture which allows the interaction among models and data from different scales and computer platforms, and a faster transference of investigation models toward clinical applications. Index Terms—Clinical decision support, dialysis, distributed simulation, dynamic knowledge-based assistance, healthcare, interoperability, modeling and simulation, renal disease, reusability, telemedicine.

I. INTRODUCTION

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ND-STAGE renal disease (ESRD) population is growing with a rising slope near 7% average per year in developed countries [1]. The public healthcare system costs of this population are increasing in a more dynamic way, due to the imManuscript received January 3, 2002; revised January 22, 2002. Asterisk indicates corresponding author *M. Prado is with the Grupo de Ingeniería Biomédica, Universidad de Sevilla, 41092 Sevilla, Spain (e-mail: [email protected]). L. Roa is with the Grupo de Ingeniería Biomédica and Departamento Ingeniería de Sistemas y Automática, Universidad de Sevilla, 41092 Sevilla, Spain. J. Reina-Tosina is with the Grupo de Ingeniería Biomédica and Area de T. de la Señal y Comunicaciones, Departamento Ingeniería Electrónica, Universidad de Sevilla, 41092 Sevilla, Spain. A. Palma and J. A. Milán are with the Grupo de Ingeniería Biomédica and Serv. Nefrología del Hospital Universidad Virgen Macarena, 41092 Sevilla, Spain. Digital Object Identifier 10.1109/TBME.2002.805454

provement in the quality of life and in the percentage of elderly people within the ESRD population. The raising of public economical costs is not readily solvable by current health program strategies, because of the low natality and high life expectation of industrialized countries. The renal replacement therapies outcomes are still low. Therefore, although more investigation about uremic syndrome, secondary pathologies related to renal disfunction and membranes behavior are needed, the improvement in renal replacement therapy is currently an achievable goal. Agodoa et al. [2] showed that a higher monitoring frequency and a best prediction of complications are needed to improve the clinical outcomes. The reduction of moves to a dialysis center by means of home dialysis improves patient quality of life, as several studies have shown. A recent example from Canada is presented in [3]. Moreover, the successful improvements in quality of life which have recently been obtained with daily hemodialysis (HD) [4] could be adequately implemented by home HD therapy. However, home dialysis has not received a widespread use because of the lack of physician support together with the constraints and inertia of medical politics. The remote healthcare (telehealthcare) paradigm has emerged as a new approach to healthcare services from the present-day explosion of novel ideas and applications of information and communications technologies. This novel area is characterized by the reduction in physician–patient gap by means of multimedia services carried on communications networks. Telehealthcare is starting to be used in many areas such as diabetes, cardiac pathology or elderly population. Currently telehealthcare investigation is mainly focused on the elderly population. This assertion may be justified by different ways. First, the high percentage of papers addressed to this subject in recent international forums. As a sample, more than 60% of papers in the special session Information Systems in Home Health Care of track 04, and 50% of papers in the session titled Home Health Care Delivery and Monitoring of the same track were addressed to the elderly in a recent world congress [5]. Second, a high percentage of public health care expenditure is consumed by a population at more than 65 years of age and several published forecasts indicate an increase of this economic burden [1], [6]. These predictions support the cost-effectiveness of telehealthcare systems for the elderly [7]. Consequently, the governments are promoting the investigation and development on this issue, which is considered a key action: systems and services for the citizen, with the targeted field of research: responses to the specific needs of groups such as the elderly and the disabled, into the fifth framework program of the European Union.

0018-9294/02$17.00 © 2002 IEEE

PRADO et al.: VCRS: TECHNOLOGICAL APPROACH TO PATIENT PHYSIOLOGICAL IMAGE

The main objective of the virtual center for renal support (VCRS) is the overcoming of several current limitations of the ESRD health assistance. The VCRS provides a dynamic knowledge-based assistance to renal patients, which is achieved by means of the patient physiological image (PPI). This is a computer component which represents several physiological subsystems of the patient. This paper develops the methodology and technology on which the PPI is based, emphasizing the knowledge representation and modeling implementation. The PPI is not restricted to a particular physiological subsystem nor a minimum degree of approximation of the patient physiology. Despite this fact, a kinetic and hemodynamics model-based PPI is used in a simulation experiment in Section V. This PPI has been developed into the VCRS pilot model which is being built at GIB. Although the computational architecture of the VCRS is a secondary goal of this paper, we present its major technical issues, which are related to the communications, protocols and the fundamental subsystems and elements. This description clarifies the previous technological approach to the PPI and supports the later description of the VCRS functions. The definition of the PPI-based VCRS is presented in the next two Sections. Section II develops the PPI concept in three stages. The first of them characterizes the knowledge representation methodology used for the description of the physiological dynamics into the PPI. This is a key subject, because it distinguishes the VCRS with respect to other model-based computer aided healthcare systems, as UTilities for OPtimizing Insulin Adjustment (UTOPIA) [8]. The second stage presents the formal definition of the PPI as an abstract computer component, neither dependent on the computer platform nor the language. The third stage addresses the technical issues of the PPI. The technical approach achieves the definition of the second stage, together with the interoperability and reusability requirements. The modeling and simulation methodology is an important task within this last stage. We benefit from the latest trends in this field and clarify several recent topics and standards which are considered in the PPIs design. Section III shows a pilot VCRS computational architecture. The paper emphasizes the communication protocols needed to achieve cost-effectiveness and universal access for renal patients. The architecture uses a multitier scheme, with an open and modular design which allows its scalability by means of clusters of computers and distributed processes. The functionality of the VCRS is presented on Section IV. We mainly analyze the novel functions that characterize this telehealthcare system. Finally, an application of PPI as a kinetic and hemodynamics representation of patient is used on the simulation experiment presented on Section V. II. PPI A. Knowledge Representation PPI is a computational component which describes several aspects of the human physiology by means of a systemic dynamics mathematical model which emphasizes the system feedback relations. The mathematical description is built on differential-algebraic equations (DAEs) or partial differential equations (PDEs), depending on the lumped or distributed

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nature of the modeled physiological topic. Considering modeling and simulation as a specialized form of information, in agreement with Miller et al. [9], the PPI may be considered as a computational module which provides knowledge about a patient. Knowledge representation in PPI is essentially different to the data-driven representations, as regression linear models, neuronal networks or rule-based systems. As a consequence, PPI provides a higher inference capacity than data-driven knowledge representations. This is because the structure and state equations of systemic models are supported on true physical relations and conservation laws. Despite this fact, data-driven strategy has advantages which are not neglected in this paper, being considered into the clinical decision support (CDS) subsystem of the VCRS. The degree of approximation of the PPIs model must be stated by physicians and researchers according to previous clinical validation studies. Therefore, physiologic, anatomic, biochemistry or biophysics assumptions are not limited by the PPIs definition. Indeed, compartmental kinetic models are considered as a kind of simplified physiological models, in agreement with Doménech et al. [10]. A first PPI based on a multi-pool urea kinetic model, extended to consider water flows between pools together with hemodynamical issues, is presented into a simulation experiment in Section V. B. PPI Formal Definition PPI is a systemic dynamics mathematical model-based observer of the patient physiology together with machines which apply therapy, e.g., dialyzer. The objective of the PPI is the discovery of new knowledge about a patient. The monitoring signals are used as mathematical inputs to the model and to adapt the observer to changes in the patient physiology and machines. As a software component, the PPI exhibits distribution, modularity and independence of platform or language capabilities. Therefore, it is placed at the [1,1,1] position into the component space, following the terminology of Stuart Thomason in Brereton and Budgen work [11]. C. PPI Technology PPI is formed by a mathematical model together with execution control and plug-in interface modules (see the “PPI architecture” block in Fig. 1). These elements are implemented at different levels, giving the PPI a desired functionality. The Mathematical model has previously been described. It is the major part of the PPI, defining the dynamics behavior of the modeling system. It is based on three elements: model equations, tunable parameters and initial conditions (snapshot). The model equations describe physiology and machines in a hierarchical and modular way, i.e., defining the dynamics of individual subsystems and machines (modules) by their DAEs or PDEs systems and the connections among them. This way, a dialyzer is represented by a DAEs system connected during an hemodialysis to the DAEs or PDEs system which in turn represents the patient vascular compartment. This concept is shown in the “PPI architecture” block of Fig. 1. The boundary conditions are defined in the BC module. Tunable parameters allow the personalization and online adaptation/customization of the PPI to

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Fig. 1. PPI is constituted by the set of elements shown in the PPI architecture block. The mathematical model is the major element and is implemented at different levels. The first level is defined by a XML codification, which is converted to EL source at a second level. A PPI program is built from this latter codification together with the remaining elements which define the PPI architecture. A detailed explanation can be found in the text.

a patient. Finally, the snapshot is a set of variables defining the model state. Tunable parameters are assigned in the parameterization stage, when a new PPI is generated and “connected” to a patient. The snapshot is computed in a second stage, using the current patient physiological state. Once a PPI is activated, it will be continuously adapting itself to the patient evolution. The mathematical procedures involved in parameterization, tuning and computation of a snapshot exceed the scope of this paper. The execution control module provides autonomy to the PPI, managing simulations tasks such as real-time synchronization, control of communication steps, read–write on the central data-base and attending requests as stop simulation, load snapshot, come back to a previous snapshot, fast or slow simulation and many others. The execution control capabilities support several advanced functions of the VCRS architecture as online therapy and personalized therapy design, which are described in Section IV. The plug-in interfaces allow the acquisition of monitoring signals and requests toward the PPI and the output of simulated biomedical signals toward the VCRS data-base. Elements which encapsulates application-level protocols such as IIOP/CORBA and COM/DCOM are also considered in this module (see blocks 1 and 2 in Fig. 2).

Fig. 1 shows a technological approach to the PPIs design. The implementation is done at two levels. At the first one, the mathematical model is defined by a XML language [12]. XML metalanguage is a novel technology based on the Standard Generalized Markup Language or SGML (ISO 8879), which was previously based on GML, created by IBM in 1969, and therefore its basic concepts are widely accepted and proved. At the present day, the majority of modeling and simulation computational environments support XML. This standard metalanguage has also been selected for the open transfer and storage of data in the physiome project [13], devoted to ease and promote the transfer of biotechnological and physiological data by means of information technologies. The XML-based PPI possesses reusable and interoperable capabilities. The importance of these topics has increased with the current explosion of novel Web-technologies [14], [15], which are pushing new methodologies in modeling and simulation arena [16]. As indicated in Fig. 1, the XML-based mathematical model is stored in a data-base. This implemented mathematical model may be accessed by any research team and this way may be connected to other models or fully or partially reused. The PPI must be transformed in an autonomous program before it can be connected to a patient. Essentially, the XML-based


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Fig. 2. Block diagram of the VCRS pilot model. With the exception of block 10, all remaining blocks represent computers, which may be extended to clusters of computers in a later industrial implementation. 3-D blocks show the application protocols used by the different objects and components into the pilot model, as is explained in the text.

PPI is first transformed to a noncausal modeling language description (intermediate stage) and finally converted to a PPI program. Noncausal modeling languages are considered the spearhead of current research in modeling and simulation area. In that technological domain, the term noncausal claims the independence between the implemented model and the assumptions about which variables represent mathematical inputs or outputs. The Unified Object-Oriented Language for Physical Systems Modeling, Modelica [17], is a novel language which has been designed to fulfill interoperability and reusability requirements. With that goal, Modelica is based on a noncausal and object-oriented methodology. Although in our opinion, Modelica is the most suitable modeling language for the implementation of PPI at this intermediate stage, it is not yet widespread, extended, or implemented enough to be considered an effective solution yet. Therefore, we have chosen the Ecosim Language (EL) for the implementation of the PPIs mathematical model in this stage. EL is also based on a noncausal and object-oriented methodology and is integrated into EcosimPro simulation tool [18]. EcosimPro’s capabilities are similar to that proposed by Modelica and is currently used by many companies and research teams. The conversion between XML-based mathematical equations and EL is a natural transformation because EL keeps the equations in a nonalgorithmic mode. A set of EL components results

from this conversion. EL-based mathematical model is stored in the VCRS database allowing the reuse by the research team. This PPI codification is more efficient than XML codification when interoperation capability is not necessary. The several components which constitute the PPIs model equations are connected together to generate a C++ class which represents the mathematical model. This stage is not an easy task and several advanced mathematical procedures are required to solve this issue, rendering an algorithm-based model [18]. Numerical integration is mainly done by DASSL, a well-known DAEs-oriented solver [19], [20]. Finally, a PPI class is built as an aggregation of PPIs model class, execution control class and several data sources classes, as indicates Fig. 1, where the classes aggregation process uses specific information about configuration of execution control and plug-in interfaces modules from the PPI architecture block. III. VCRS ARCHITECTURE Fig. 2 shows a block diagram of the VCRS architecture currently being developed by GIB. Numbered blocks represent different computers, with the exception of block 10. The VCRS is constituted by three types of elements: RAUs, CIPAs, and RPVC, which are now described. The remote access unit (RAU) is a microcomputer which reads monitoring signals and sends

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them to the RPVC subsystem allowing the access of patient to the VCRS. They are represented by block 10 in the previous figure. A client interface for professional access (CIPA) is the computational application by which physicians access the VCRS (see block 9). A web-based interface is provided for standard professional access, while more specific event-driven applications are designed for other clients. Finally the resources provider of the virtual center (RPVC) is the set of machines and applications which manage and process monitoring signals, discover knowledge about patients and assist physicians and patients (see remaining blocks). Communications design is based on the virtual private network concept. Renal patients have a reliable link to the VCRS, connecting with a X.25 network through the public switch telephone network by means of a gateway. This design guarantees a universal, efficient and inexpensive connection to the VCRS, even from rural environments, and reduces the RAUs computational requirements related to communications support. Although it is not represented in Fig. 2, IP connections for RAUs are also permitted. The VCRS uses two local area networks (LANs) which may be extended toward different geographical locations by means of virtual private networks (VPN). LAN 1 connects RPVC to the outside world, i.e., CIPAs and RAUs, through the network service provided by Telefonica Data communications provider [21] (see network node DPN/PP1 in the previous figure). LAN 2 connects computers pertaining to the VCRS. The RPVC is constituted by seven subsystems: PPI-based simulator (blocks 1 and 2), supervisory control and data acquisition (SCADA) (block 3), processing server (block 3), clinical decision support (CDS) (block 6), object-relational database management systems (ORDBMS) (blocks 4 and 5), web server (block 8), and communications server (block 7). The specific software platforms and operating systems indicated in Fig. 2 have been selected for the development of the VCRS pilot model. Interaction among all subsystems is mainly done through databases, allowing their implementation within different languages and platforms under an interoperable scheme. Block 1 represents a set of computers which support the simulation of PPIs connected to renal patients. An autonomous running PPI is called active PPI to differentiate from the other PPI implementations described before. All model variables from block 1 are stored in the assistance data-base (block 4). Blocks 2 and 5 achieve similar functions but are addressed to support testing and research about new physiological models and therapy trials for “clones” or identical copies of the PPIs which run into the assistance database toward the trial database. The way PPI data are stored and managed was investigated to get efficiency and security in the transactions. Two issues were taken into account, the methodology used by the database management system (DBMS) and the biomedical specific application protocols or formats [22]. This latter issue expresses which type of standard must be used to provide interoperability with other environments. Extended relational database methodology (ERDB) is not able to manage complex 1DPN/PP represents different technical options provided by the communications provider.

objects like those required in biomedical engineering domain [23]. There exist two methodological approaches to account for this limitation: object-relational DBMS (ORDBMS) and object oriented DBMS (OODBMS). It is not a clear issue which methodology is the best for PPI implementation. However, ORDBMS is a natural evolution of relational methodology which has been successfully used by many companies and is currently supported by many commercial or “public” products, as Oracle 9 [24] or PostgreSQL 7.x [25], respectively. Although OODBMS has experimented a large advance in recent years [26] and despite the theoretical superiority of OODBMS to manage object-oriented concepts plus its seamless integration with host languages, it is not extended enough yet. As a consequence, the ORDBMS methodology has been selected for the VCRS. PostgreSQL has been selected for the VCRS model pilot because this free software provides similar features and programming utilities than the more powerful ORDBMS such as Oracle 9. In fact, PostgreSQL must not be discarded for an industrial implantation of VCRS, because it is being constantly improved [23]. The RPVC has been designed as a multi-tier architecture to avoid bottle-necks on computers running the ORDBMS. This solution agrees with methodology proposed for the remote access of biomedical signal data by Lovell et al. [27]. Indeed multi-tier systems, which are also known as second generation client/server, have been successfully used in many engineering areas during last years. The processing server (block 3) attends and processes requests from specific CIPAs and retrieves or sends data to the database server. Database business rules and other computational tasks related to data request and storage, are implemented and executed by means of stored procedures (PL/pgSQL on PostgreSQL), running on the data sever (blocks 4 and 5), and SQL queries embedded on C++ code, running on the processing server (block 3). Standard professional access from CIPA is done through a web-based interface, connecting by the HTTP protocol with the web server, which in turn requests/sends data to the database server. The architecture presented in Fig. 2 has not considered any object broker server to provide available processing server addresses to CIPAs, because there is only one processing server in our VCRS pilot model. The SCADA (block 3) conditions and filters biomedical signals from RAUs before they can be stored in the database and read by PPIs. This subsystem has the classical functions of commercial SCADAS, especially alarm generation and management, and simple signal computing. Simulator-generated signals are also processed by the SCADA to generate alarms. The CDS is an online clinical decision support subsystem, sharing some key concepts with online monitoring performance and computer aided diagnostic, successfully used in energy production area [28], [29]. A health state analyzer (block 6) both periodically and manually computes several algorithms and mathematical procedures using monitored and simulated signals as inputs, with the goal of detecting sensor malfunctions, inferring causes for low performance of therapies, such as an excessive protein deposition or hollow fibers clotting in a dialyzer membrane, and discovering physiological dysfunctions as an exces-


sive vascular access recirculation during an HD session. A detailed explanation of this process exceeds the scope of this paper. Data-driven knowledge creator (block 6) is the CDS module which provides diagnostic assistance. Rule-based models are specially indicated for this target, nevertheless there exist other data-driven methodologies which have demonstrated their competence as support to diagnostic and therapy adjust, like regression models or neural networks. Application protocols have been selected to render an efficient and reliable communication between clients and servers and to support distributed computing. Common object request broker architecture (CORBA) specification is an architecture developed by the Object Management Group (OMG) which allows two software objects in different programs to interact directly with each other. This standard was adopted as an alternative to the remote method invocation (RMI) into Java, which allows interaction between Java components. Adopting CORBA remote access in Java is made by Internet Inter-Operability Protocol (IIOP). CORBA has been the specification selected for open communications in VCRS. As a consequence object request broker (ORB)-IIOP appears in all blocks of Fig. 2. This specification is supported by all major operative systems (OS). Nevertheless, although it is not supported by many OS, the wellknown Microsoft COM/DCOM architecture has also been considered for distributed computing in PPI-based simulator and CIPA into VCRS pilot model. Many commercial development suites, like Borland Delphi or Microsoft Visual Studio include both CORBA and DCOM capabilities within their resources. Attending to practical reasons, SQL/Prop (proprietary) protocol for SQL requests and finally Stub-Skeleton(Skel)/RMI protocol for distributed Java are also considered in the VCRS pilot model. IV. VCRS FUNCTIONS Classical telehealthcare systems provide communication networks and services together with biomedical signal monitors to improve patient supervision and increase interaction between physicians and remote patients. There are some recent noteworthy telehealthcare systems for the ESRD population [30], [31]. The VCRS is a telehealthcare system which shares these initial goals, adding new capabilities for detecting dysfunctions in machines, therapies and patients and for diagnosis assistance. All these capabilities are mainly supported by the PPI-based simulator. This Section stresses the major PPI-based functions of the VCRS. 1) Online Dysfunctions Detection: Following a similar philosophy to that used by the PMAX environment from Scientech [29], the health state analyzer cooperates with the PPI-based simulator for detection of low performance of therapies, physiological dysfunctions or sensor malfunctions. A detailed explanation of how this function is implemented exceeds the scope of the paper. 2) Predictability and Online Therapy: New instances of every PPIs mathematical model are created and executed either in a nonsynchronized manner as response to events or periodically. These instances compute the physiological , being patient evolution, since current time , until

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Fig. 3. Illustrative representation of the online therapy function. Continuous lines show the (upper figure) patient health state temporal evolution and (lower figure) real-time PPI state evolution. Dotted lines in the lower figure are predicted evolutions (trials) computed in a non real-time way by a PPI instance and started at different instants (marked by filled circles). Action related to the last trial is used to adjust online the therapy which is being applied to the patient. The desired target point is reached.

a configurable time period. A health state analyzer from the CDS processes this information and generates output variables, processed later by the SCADA, generating alarms or warnings if a plausible future incidence is detected. Biomedical variables simulated in this mode have less accuracy than those computed by the PPIs observer, because the latter uses real time monitored variables. Online therapy is another VCRS function based on the VCRS prediction capability. This technique has been graphically represented in Fig. 3. The upper graphic shows the temporal evolution of the patient state with respect to real time during a therapy session. The target point is the desired value for the patient state. PPIs state evolution, in lower graphic, shows several patient state evolution estimations. The fine continuous line shows the predicted patient evolution before therapy will be modified. Three therapy modifications (simulations trials) are tested before the definitive action will actually be applied. As indicated in Fig. 3 the starting point is different for successive trials, because real time is progressing. Third trial is accepted and applied, starting at mark “2” on the upper graphic. Finally, the thick continuous line in the lower graphic shows real time patient state evolution computed by the PPI. Section V presents a simulation case study about online therapy. Davis et al. presented a distributed simulation methodology for the military area which is based in similar concepts [32]. Although recent biomedical works are advancing toward closed-loop therapy control [33], the VCRS uses the physician as the effector element in our pilot model. 3) Online Computer Aided Diagnostic and Therapy Recommendations: The data-driven knowledge creator module from the CDS provides online diagnostic assistance to the physician. This process is executed as a reaction to a physician request from a CIPA point. Rule-based systems are mainly preferred for this target, although there exist other data-driven methodologies which are being considered. 4) Personalized Therapy Design: Early design of personalized therapies is currently a challenge for healthcare systems. The VCRS facilitates trials of new and personalized therapies by means of the same procedure used for the adjustment of

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therapies. In addition, several computational algorithms implemented in an interoperable language as Java [34], and devoted to therapy prescription may be discharged from RPVC and executed on a CIPA element. A new hemodialysis prescription procedure has been designed for that goal [35]. 5) Physiological System Simulation Environment: The design and development of the VCRS is a chance to give new technological and scientific solutions to human body simulation environments. This futuristic domain is mainly represented by the Physiome project [13], which has agglutinated physiology and biotechnology research in different health areas and particularly in the renal system [36]. The Physiome project is devoted to the building of computational knowledge networks about human physiology. It emphasizes the connection of human physiological models developed in a modular way to achieve integral approaches to the human body behavior. PPI-based VCRS architecture has been designed with the aim of contributing to Physiome’s objective, sharing concepts with the physiological system simulation environment recently proposed by Bei Gu and Asada [37]. Each PPI implementation level provides a different interoperability and reusability grade. This design strategy is intended to provide an easier transfer and application of the PPIs mathematical models.

V. CASE STUDY: ON-LINE THERAPY SIMULATION A simulation experiment about an online modification of the hemodialysis therapy is presented. The experiment has been carried out with a PPI, whose mathematical model represents the urea kinetics and water flows between major human pools, together with a dialyzer membrane. It is a lumped parameter model which was presented in a previous study [38]. This PPI was adjusted to follow the evolution of a true ESRD patient, submitted to regular hemodialysis therapy. The PPI initial condition was set according the predialysis initial condition of the patient in a past HD session. That HD session was chosen due to the high difference between the desired HD dose, defined in [39] ( ), and the delivterms of the equilibrated . ered The objective of this simulation experiment is to show the . With the goal of PPIs capability to achieve a desired simplification, we have selected the blood flow ( ) only as a therapy control parameter. This choice was supported on the delivered in the true session, which let us small value of range for an adequate therapy modification. Simulation experiment assumes that patient has no vascular access impairment. A. Method and Materials A PPI has been adjusted to an ESRD diabetic female patient, having 79 Kg postdialysis weight, 65 years old, and 155 cm height. She was submitted to three HD sessions per week, having stable clinical conditions. Table I shows blood urea nitrogen (BUN), hematocrit (HTO) and plasma proteins measurements, drawn at the indicated time instants, from a selected HD session. Postdialysis measurement was collected keeping a 50-ml/min arterial line flow during one minute. A Gambro Cuprofan filter, type GFE 18, was used. Excessive access recir-

TABLE I BLOOD SAMPLES DRAWN IN A SELECTED HD SESSION

Values (Val. are drawn previously to the beginning, 1 min and 30 min after the end. BUN is the blood urea nitrogen concentration, HTO is the hematocrit, and PP is the plasmatic protein concentration.

culation was discarded by means of the two-needle urea-based method [40]. 210 ml/min blood True dialysis was delivered with a 500 ml/min dialysate flow during 4 h, flow and a 900 ml. Correcting blood being the ultrafiltrated volume flow to take into account excessive pressure drop when 200 ml/min, considering average percentage of water into blood ) provided by and reducing the product permeability-area ( manufacturer a 7%, in agreement with considerations stated 171.43 ml/min expected dialyzer in [41], a value of urea clearance was obtained. The effectiveness or extraction 0.916. The coefficient related to this urea clearance is was approximately 1.05, in agreement with desired the value recommended by the NKF [42]. A computational related to a procedure for obtaining the dialyzer clearance was presented in [35]. However, the delivered desired 0.874, which has been computed from HD dose was the single-pool urea kinetic model of Sargent [43], using the postdialysis urea distribution volume as an input. The adjusted PPI was used as a base for the development of the simulation experiment, whose objective was the achieve1.05. The PPIs ment of an adequate HD dose, i.e., a mathematical model was implemented in EL [18] and the simulation experiment was developed on the Ecosim simulation monitor. Predialysis BUN was known by the PPI before starting the HD session. HTO and clearance were PPIs real-time input variables. These signals can be measured by commercial monitors, such as Hemoscan and Diascan from Hospal [44]. Blood flow, dialysate flow, and ultrafiltrate flow or transmembrane pressure are also known by the PPI. The particular dialyzer filter is defined when the PPI is parameterized. A reference value for the total body water (TBW) is computed from Chertow et al. formula [45] by the mathematical model. Plasmatic protein concentration (PP) was measured at the three instants indicated in Table I. These values were sent to the PPI after the end of HD session in the simulation experiment, therefore they are nonreal time values. These last measurements are used to adjust the PPIs observer and to correct the previously computed signal evolution. Three types of signal evolution computations are distinguished. First, a predictive computation, in which real-time variables are not available. Second, a real-time computation, in which the PPIs mathematical model works as a patient’s observer. Finally, a corrective computation in which several nonreal time variables collected after their occurrence are available for PPI. PP values have been selected for this latter mission in the simulation experiment.


c

c

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c

Fig. 4. Urea concentrations into vascular ( ), interstitial ( ), and cellular ( ) compartments computed by the PPI during simulation experiment. (a) Urea 15 min., when urea clearance provided by simulated dialysance module is reliable enough. (b) Adjustment therapy trial, done in a evolution prediction done at non real-time mode, increasing from 210 ml/min toward 252 ml/min. (c) Real-time evolution computed by PPI when previous adjustment on is accepted. (d) Experimentation environment (EcosimPro) used to compute a final correction to the urea concentration evolution as is explained in the text.

t= Q

Real-time evolution of the HTO has been simulated from true HTO samples presented in Table I, by means of a nonlinear interpolation. A more detailed description of the PPI model and interpolation functions exceeds the objective of this case study. has been simulated by a The dialyzer urea clearance signal constant value corresponding to the average urea clearance delivered in the true session. That value has been obtained from the same PPIs mathematical model, using BUN samples from 144.67 ml/min. Table I. The resulting value was Signal evolution computed by the PPI could be analyzed by the physician with the aid of computer analysis tools. These tools are based on algorithms similar to those implemented in the health state analyzer module from the CDS, but are manually started. Their execution can be done on server (RPVC) or client (CIPA) side. In this latter case, the algorithm code is downloaded as a Java applet to the CIPA. B. Simulation Results A predictive computation of the patient evolution is requested 15 min after the starting of the HD session, with a temporal horizon stated 30 min after its end. The dialyzer switch-off event may be considered as a programmed event. Fig. 4(a) shows the urea concentration evolution computed by the PPI, into cellular, 0 has been considered interstitial and vascular pools. Time as the start of the HD session. Postdialysis BUN was 63.91 mg/dl, while the BUN 30 min after the end of dialysis was 71.79 mg/dl. The hemodialysis dose predicted by the second generation formula of Daugirdas [46] was

Q

0.895. This value is close to the truly delivered on this session, which was previously presented. Discarding an excessive vascular access recirculation, the deand the target viation between the predicted may be corrected acting on . Although the target can be solved from a procedure derived from that presented in [35] can be we have preferred a less accurate but easier method. solved from the following equation, which is explained in [41]

ml/min

(1)

171.43 ml/min is the desired dialyzer urea clearwhere 3.75 ml/min is the ultrafiltration flow and ance, 0.770 is the dialyzer efficiency. This latter variable is solved from the simulated urea clearance signal previously presented. is the adjusted blood flow. Blood flow to be requested , is given by the following equation, which is obtained from [41]: ml/min

(2)

252 mil/min was tested on a new A therapy trial for instance of the current PPI, starting 15 min after the beginning of HD. Fig. 4(b) shows the predictive evolution of urea concentration into major patient pools. There is a slight slope 15 min. It was obchange of concentrations evolution at 54.34 mg/dl and 62.47 mg/dl. tained 1.048. This dose is Predicted hemodialysis dose was

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closed to the desired one, therefore, therapy modification was accepted. Fig. 4(c) shows the urea concentration observed by the PPI is modified at 15 min. during the HD session, when Differences between this urea concentration evolution and that computed in the therapy trial are due to the higher information available, i.e., real-time signals are now considered by the PPI. In this simulation experiment, the HTO provided by an simulated monitor is the only real-time variable which causes 54.39 mg/dl and that difference. It was obtained 60.72 mg/dl. Computed delivered hemodialysis dose 1.080. was Finally, Fig. 4(d) shows the interface of the EcosimPro monitor with the urea concentration evolution obtained when PP measurements are considered by the PPI. After this correction, 54.55 mg/dl, the values were obtained 64.07 mg/dl and 1.02. This simulated delivered dose is very close to the desired one.

VI. DISCUSSION Previous studies have shown that monitoring requirements of renal patients are not being achieved nowadays [2]. Telehealthcare systems provide a fast link between patient and physician to transmit the monitoring data. This is especially important during a dialysis session, therefore several recent telehealthcare systems oriented to renal patients are promoting home dialysis by means of a communications link between the patient’s home and the physician. We should highlight the HOMER-D project [47], [48] which fills the supervision gap between a patient submitted to home dialysis and the physician by means of an ISDN connection. Although those classical approaches are demonstrating economical, technical and clinical reliability, they keep classical schemes to acquire knowledge about patients, to make diagnosis and prognosis and to adjust the therapy for a particular patient. The particular design of the VCRS pursues a better benefit from the monitoring signals obtained from the patient. This is achieved putting together the disciplines modeling and simulation and information technologies. In essence, VCRS works at two levels. The first of which is supported by the PPI element. This is basically a systemic mathematical model which acts as an observer of the internal biological variables of the patient, emphasizing the physiology’s dynamics due to its richness of information [49]. In the second level, PPI outputs are analyzed and processed in different ways. Data-driven models are the base of data-driven knowledge creator from the CDS, which is placed in this second level. This partitioning is found on properties of systemic models in opposition to data-driven models. Systemic models have higher predictive and inference capacity than data-driven models [50]. Thus, they have been chosen to support the VCRS knowledge. On the contrary, data-driven models have demonstrated a higher robustness on the determination of signal patterns, which are the base of expert systems. Data-driven models are being successfully applied in diabetes care [51], [52]. UTOPIA, a consultation system for visit-by-visit diabetes management [8], is a representative example of a data-

driven model. It provides advices generation for insulin adjustments by solving a linear system equation. This linear system is learned by extraction of relationships between insulin adjustments and temporal patterns of blood glucose trends. UTOPIA has recently been extended toward a telemedicine configuration [48], which emphasizes its model-based design. On the other hand, systemic models are basically based on conservation laws and feedback relations. These are being successfully used in modern online performance monitoring of engineering systems. A particular domain where this kind of model has demonstrated its benefits is the energy production area. The predictive capacity of these models is applied to predict plant behavior from scenarios created by the user. This latter function is classically provided by What-if? modules [28], [29]. The What-if? concept underlies the online therapy function of VCRS. The reliability of predictions and observations done by the PPIs mathematical model is critical on the VCRS architecture. Nevertheless, mathematical models have fully demonstrated their suitability in the renal area. For example, urea kinetic models have proven their capability for the improvement of clinical outcomes obtained by renal replacement therapies in ESRD [53]. Hemodialysis-induced hypovolemia is a frequent intradialytic complication which can be successfully simulated and predicted [54]. Indeed, clinical application of renal physiology models is limited nowadays due to the lack of computational resources and tools easily manageable by physicians in their working environments. The VCRS design has pursued an easier transfer of research models toward the telehealthcare area. Signal monitoring is also a key aspect in VCRS as in other telehealthcare systems. A detailed analysis of this issue exceeds the scope of this paper. Nevertheless, high advances on minimally invasive biosensors, online hematocrit monitoring and online dialysis monitors are being produced in these times. Distributed Modeling and Simulation is a research area which is directly related to the VCRS project. This area has been strongly pushed due to the Web revolution [15]. However, distributed simulation technology is not mature enough to support this critical service [32]. In spite of this situation, current network architectures for distributed computing can initially be deployed within the VCRS investigation side (trial side), serving as added value to investigators and moreover allowing the future implantation in the assistance side. With that objective, we are researching in the more adequate computing protocols to support distributed simulation. High Level Architecture (HLA), which has recently been adopted as the IEEE standard 1516 is now the cutting edge in distributed simulation environments as the many conferences and papers related to this issue demonstrate. HLA development has been led by the U.S. Defense Modeling and Simulation Office (DMSO) and was adopted by the Object Management Group (OMG) as a distributed simulation architecture in 1998. However, HLA was thought to reuse and allow interoperability among old simulators by employing an Object Model Template (OMT) to wrap a given model into a single virtual object called a federate and allowing the communication among federates by means of a bus scheme, i.e., each federate places its current state information


while the other federates listen to the communication bus in order to obtain data which affect their internal operation [32]. This architecture has serious limitations, above all, real-time inefficiency and inability to make use of system of systems (hierarchic) approach. For these reasons, the VCRS project has adopted CORBA protocol for the distributed simulation. Another nonsolved issue related to the distributed simulation is relative to the physical coupling between modeling systems. In discrete systems the major problem is the simultaneity event [55], while in continuous time systems not only the accuracy but also the stability may be affected. The current trend is to make distributed simulation within superstructures, with a weak coupling between them [9].

VII. SUMMARY In this work we have presented a novel telehealthcare architecture for renal assistance (VCRS) which is not limited to patient telemonitoring, but yields a more deep knowledge about patients and therapy equipments by means of modern modeling and simulation technologies. This paper has been focused on the PPIs technology, which is the base of the VCRS, although we have presented also a detailed perspective of the VCRS architecture. A pilot model of the VCRS has currently been developed by the Biomedical Engineering Group (GIB) of the University of Seville. The first objective of this system is to provide a more adequate dialysis therapy to ESRD patients, overcoming current limitations on the application of kinetic and hemodynamics mathematical models to achieve better clinical outcomes. With that objective we are making advances on urea kinetic modeling and on computational procedures for HD therapy prescription. These mathematical models are the base of the first PPI components which we are developing. The simulation experiment presented in this paper has also been focused on HD adequacy improvement. Another research line related to the VCRS project is the development of dialyzer membrane mathematical models with the ability to explain, predict and make inferences about the membrane performance when different in-vivo conditions occur. Clinically available dialyzer mathematical models are too simplistic nowadays. For example, they do not consider variation on permeability-area product (KA) when dialyzer flow changes. More detailed membrane mathematical models are being researched by the GIB with the goal to be implemented into the PPI components. As a conclusion, we think this telemedicine system promotes a change in the manner in which emerging telehealthcare technologies are used by physicians and patients.

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Manuel Prado (S’00) was born in Huelva, Spain, in 1965. He received the electrical industrial engineering degree from the University of Seville, Seville, Spain, in 1990, and is currently working toward the Ph.D. degree at the same university. From 1990 to 1994, he was engaged in real-time modeling and simulation of power plant physics processes at the Power Plant Department of Sainco-Abengoa, Seville. In 1995, he worked as a Manager for SHS Corporation for four years, where he was responsible for research on remote measurement and telecontrol in low-voltage electric distribution networks. Since 1995, he has been with the Biomedical Engineering Group, University of Seville. His research interests include modeling and simulation of physiological systems and information technologies applied to telemedicine.

Laura Roa (M’93–SM’96), photograph and biography not available at the time of publication.

Javier Reina-Tosina (S’99) was born in Seville, Spain, in 1973. He received the telecommunication engineering degree from the University of Seville, in 1996, and is currently working toward the Ph.D. degree at the same university. Since 1997, he has been with the Electronics Engineering Department, University of Seville. His current research interests include health information systems, communication networks for telemedicine and microwave technology.

Alfonso Palma, photograph and biography not available at the time of publication.

José Antonio Milán, photograph and biography not available at the time of publication.