Mikael Hakman

Prediction of myocardial infarct size from early serum myoglobin observations

Different possibilities to predict infarct size were analysed. The basic method was the fitting of a mathematical model to serial serum myoglobin concentration values from the very early phase of infarction. Correlation was performed with infarct size estimated from the complete serum curves of 53 patients. An observation period up to and including the serum peak value (on the average 6.8 h after onset) was required in order to give a well-determined value of infarct size. A correlation coefficient of r = 0.85 (n = 38) was then obtained. The serum peak concentration value of myoglobin correlated even better (r = 0.89). The initial slope of the serum curve (obtained on the average 4.3 h after onset of symptoms) also correlated well to infarct size (r = 0.80; n = 53). In conclusion, estimation of infarct size appears to be as good with the serum peak value of myoglobin as with model-based parameters. The most useful measure for early prediction of infarct size could be the initial slope of the serum curve.

4.5. Diagnosis, size estimation and prediction of acute myocardial infarction from S-myoglobin observations. A system analysis to assess the influence of various sources of variability

Three different ways of using S-myoglobin observations for early diagnosis of acute myocardial infarction have been investigated by computer simulation techniques, viz. classification based on (i) single determination in relation to a decision limit, (ii) the peak serum concentration value, (iii) estimated or predicted value of infarct size from serial serum concentration determinations. The results of the in numero experiments indicate that it is possible to define optimal conditions with regard to time, period and frequency of observation, as well as to assess requirements on pre-analytical/analytical variation. The optimal time for a single observation should be about 10-12 h after onset of symptoms. The influence of pre-analytical/analytical variation is not very critical in this connection and the quality requirements are achievable in the clinical chemical laboratory today (total CV about 0.10). The peak serum value has good diagnostic power, but does not provide a good index of infarct size, no matter how good the analytical quality may be. It should be possible to predict infarct size from early serial S-myoglobin observations. A coefficient of pre-analytical/analytical variation below 0.05 is then required in addition to frequent blood specimen collection from admission up to peak time of the serum concentration curve.

Object-oriented biomedical system modeling--the rationale.

A short tutorial and a rationale for Object-Oriented Biomedical (Continuous) System Modelling (OOBSM) are given. The paper investigates and defines what is needed in order to make the work with complex bio-medical and pathophysiological models easier, less error prone and conceptually clearer than is possible by using the existing modelling techniques. It also contains a specification of what is required in order to make such models and corresponding knowledge communicable among different research groups and in order to use such models as components in even more complex models. The work shows that hitherto available continuous system modelling languages and tools are less suitable for the construction of complex, interdisciplinary, multilevel, hierarchical models and model components and that those modelling languages do not allow for easy exchange and communication of the model knowledge between different research groups and sites. It concludes that object-oriented and distributed objects methodologies are both feasible and suitable for such modelling.

Object-oriented biomedical system modelling — the language

The paper describes a new object-oriented biomedical continuous system modelling language (OOBSML). It is fully object-oriented and supports model inheritance, encapsulation, and model component instantiation and behaviour polymorphism. Besides the traditional differential and algebraic equation expressions the language includes also formal expressions for documenting models and defining model quantity types and quantity units. It supports explicit definition of model input-, output- and state quantities, model components and component connections. The OOBSML model compiler produces self-contained, independent, executable model components that can be instantiated and used within other OOBSML models and/or stored within model and model component libraries. In this way complex models can be structured as multilevel, multi-component model hierarchies. Technically the model components produced by the OOBSML compiler are executable computer code objects based on distributed object and object request broker technology. This paper includes both the language tutorial and the formal language syntax and semantic description.

A PC-Workstation Supporting Interpretation of Clinical Chemistry Laboratory Data

In a previous paper1 the potential application of data base management systems (DBMS) and knowledge base systems (KBS) in clinical laboratory medicine was discussed. One application which has raised a lot of interest in many clinical chemistry laboratories is automatic interpretive reporting of test results. It has been pointed out that in order that the laboratory would be successful in this activity and have acceptance from the clinic, the interpretive advice has to be tailored to the real need of the physicians.

Connectivity Infrastructure and Components for POCT Environments — Overall Infrastructure

Connectivity infrastructure, hardware and software components for point-of-care testing (POCT) environments at medium-to-large sized hospitals with expected number of POCT instruments in the order of hundreds or thousands are described. The instruments include both network-ready and non-network-ready devices. The latter are connected to the network by means of a hardware-based Instrument Network Adapter. Instrument messages are converted to standardized form and, depending on message content, are routed and delivered to appropriate destinations by a software-based Message Routing System consisting of a Message Router and a Delivery Agent. Target information systems consist of departmental and central information systems.

Object-Oriented Biomedical System Modelling - A Short Presentation

Abstract The rationale and a short tutorial for object-oriented biomedical modelling are given. The paper investigates what is needed in order to make the work with complex biomedical models easier and conceptually clearer than is possible by using the existing modelling techniques, and in order to make these models communicable among different research groups and sites. The paper also describes shortly a novel Object-Oriented BiomedicalSystem Modelling Language, OOBSML. The language supports explicit definition of model input-, output- and state quantities, model components and component connections. The model compiler produces self-contained, independent, executable model components that can be used within other models.

A System for Interactive Knowledge-Based Simulation With Application in Management of the Critically Ill

The following paper describes an interactive knowledge-based modelling & simulation system (KBSIM) providing some new means for desig-ning and developing decision support for the management of the critically ill patient. It combines quantitative simulation with symbolic reasoning techniques, using a relational data base management system for data storage and inter process communication.