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The principles of the functional computer monitoring system

The analysis of the obtained four clusters not only showed a mathematically significant difference between them, but also revealed the clinical, biochemical and pathophysiological characteristics of each of the analyzed patterns that are fundamentally different from each other. In this study, we intentionally did not compare the studied clinical and pathophysiological aspects of the course of traumatic disease with the type and nature of the damage. only the severity of damage (according to 1SS criterion) and the severity of the condition (according to ARACNE II criterion) were taken into account. This approach to data analysis makes it possible to conclude that in the indicated range of damage severity (1SS from 10 to 75) and state severity (ARACNE II from 4 to 29), the main types of pathological reactions appear, reflecting to some extent the main links of the oxygen budget in the body. The validity of this approach to the allocation of nodal pathogenetic links in the post-traumatic period is also indicated by a rather clearly defined clinical picture. corresponding to each individual cluster.

Based on the proposed JHSiegel et al. [34] classification and in accordance with the above pathophysiological and clinical characteristics. the clusters we have identified can be identified as:



Cluster A - “stress response pattern”,

Cluster B - “metabolic imbalance pattern”,

Cluster C - “pulmonary heart failure pattern”;

Cluster D is a “hypovolemic disorder pattern”.



In our study, we also identified four clusters, like JHSiegel. but in essence, in terms of quality, only the bottom of the four were similar - clusters of stress response and metabolic disturbance. For convenience and comparative analysis with a functional computer monitoring system. developed in buffalo. USA. we used the terminology proposed by these authors | 34).

The obtained four pathological clusters allow us to describe the whole spectrum of diverse combinations of the analyzed features using specific numerical values. For this, the simplest and most effective method is the method of determining the Euclidean distance from the center of one set of features to another that is widely used in mathematics (11. 16).

For this purpose, it is necessary first of all to bring all measured values ​​to any one form that is convenient for all the indicators used. The corresponding Z-score of each of the indicative control groups can most preferably be such an expression. Using it as a criterion, any of the indicators used in the structure of the obtained pathological clusters can be expressed as a multiple of it. Mathematically, this can be formulated as follows.

Let Ri be the Z-score of the i-th indicator of the control group. Then the Z-score of the i-th indicator of any cluster will be calculated by the formula:

Z (K) i = Ki / Ri,

where K - A, B, C, D, and Ki, is the actual value of the i-th indicator of the corresponding cluster).

Thus, an approach is mathematically defined that can be used to measure and compare with each other any of the analyzed indicators of clusters, despite the different units of expression.

In order to determine which of the clusters we identified refers to the pathophysiological image of the patient being examined at a particular time, it is necessary to summarize the Z-scores with respect to all four pathological clusters and control values ​​and then find the minimum score. It represents the desired value. Mathematically, this can be represented as:

where Dist is the desired minimum distance to the cluster, calculated as the minimum value of the sums of all ix Z-estimates of the clusters (only those values ​​that were greater than 1.9 were considered significant).

To use this algorithm in practice, the “Rescard” ver 1.1 program was created. written in the programming language Turbo Pascal ver 6.0 and implemented for IBM-compatible personal computers. In the process of creating this program, we were faced with the question of the form most suitable for the graphical interpretation of the resulting clusters. After a rather lengthy analysis, an eight-pointed star was chosen, the rays of which are the pathophysiological indicators we have chosen, and the circles intersecting it. Are the corresponding Z-scores. In fig. 4.13 presents a computer implementation of the specified algorithm in our chosen form. The circle is most intensely highlighted. corresponding to the control group. Each of the circles, located in the direction from the center of the circle, is removed by one standard deviation with a plus sign. but circles. located towards the center of the circle - with a minus sign. The Z-values ​​calculated at a particular moment in time are plotted along eight axes. For a visual representation of the nature of the average values ​​of the clusters formed by us, their graphical representations are in the corners of the screen, and when outputting results to a printing device (printer), in the corners of the sheet.



93.33

Fig. 4.13.

Graphic representation of an eight-dimensional image

.

AV_Diff - arteriovenous oxygen gradient;

SWLV (I) - an index of systolic work of the left ventricle;

MBP - mean arterial pressure;

HR is the heart rate;

CI is the cardiac index;

PHv is the acidity level of venous blood;

PvO2, is the partial pressure of oxygen in the venous blood;

PvNO2, is the partial pressure of carbon dioxide in the venous blood.

-

A graphic display of the pathophysiological profile of the body (at the time of the examination) in the form of an eight-pointed star, the rays of which record changes in the selected, the most representative. indicators, allow you to throw a logical "bridge" to the volumetric perception of the clinical image of the patient in four-dimensional space. The previous, third, chapter was precisely devoted mainly to the formation and justification of such a perception using the concepts and terminology of synergetics.

Let's go back to fig. 3.7, where in spatial form the spatial structure of two attractors is compared. The first scheme (a) corresponds to the state of effective stress, when the spatial trajectories of chronologically coupled functional algorithms converge at one point, and this ensures the implementation of a given behavioral response of the body to an emergency. As can be seen in the figure, the frontal plane slice (“computed tomography”) of the spatial structure of such an attractor displays a typical functional profile (pattern, cluster) of stress. The second scheme (b) conditionally expresses any of the clinical forms of the extreme state of the body. Here the situation is different: in a complex nonequilibrium system, disorder, imbalance occurs. Functional algorithms deviate from the programmed paths. Their final links cannot be reduced to a single point. A “strange” attractor is formed.
It belongs to the field of pathology, but at the same time retains the signs of an individual clinical image of the patient. A frontal plane section of the spatial structure of such an attractor. carried out after deviating from a given program the trajectories of several (in this case, eight) algorithms selected for analysis, we can fix an individual pathophysiological profile and recognize its similarity with one of the clusters focused on a specific prognosis of the clinical situation. It should only be emphasized once again the need for simultaneous fixation of all given indicators. since they are designed to characterize a single planar cut.

Thus, it is possible to visually and formally assess the patient’s condition at a particular point in time, as well as comparisons with average values ​​of typical pathological profiles - “hyperdynamic stress response”, “metabolic imbalance”, “pulmonary heart failure”, “hypovolemic disorder” and profile values ​​of the “control group”.

During the implementation of the algorithm for calculating the minimum distance and determining which cluster the victim belongs to at the corresponding moment in time, after determining the distances from the patient’s specific profile to the fixed values ​​of the clusters, the results are presented on a computer screen in the form of Fig. 4.14.

CLASSIFICATION OF STATE

Fig. 4.14.

Type of document after settlement

.

The calculation of the distances to each of the clusters allows you to determine the minimum, which is made as a conclusion about a specific condition.

It should be noted that the information content of the obtained conclusion is not fully manifested, since the analysis of the dynamics of the process requires orientation in four-dimensional space, which is not the usual category of clinical thinking. To facilitate the perception of four-dimensional space and to visually assess the dynamics of the process, a two-dimensional interpretation was used. For this purpose, the ratio of the distance to cluster C to the distance to cluster B was plotted along the abscissa axis, and the ratio of the distance to cluster D to the distance to cluster A is the second axis — the ordinate axis.

The choice of these particular relations is not accidental. In the course of the pathophysiological analysis of cluster C (a cluster of “pulmonary-cardiac disorders”), it was noted that the most likely ones are leading in the formation of a specific image of this cluster. violation of ventilation-perfusion relationships, which against the background of increasing hypoxia leads to the development of cardiac decompensation. At the same time, the severity of aerobic processes in the body of the victim is still quite high, and this is manifested in a significant increase in the arteriovenous oxygen gradient. low partial pressure of oxygen in the venous blood.

With an appropriate analysis of cluster B (“metabolic imbalance”), attention was drawn to a sharp decrease in oxygen consumption against the background of an increase in its partial pressure in the venous blood with a constant flow, as well as a narrowing of the arteriovenous oxygen gradient. All these changes are noted against the background of an increase in the lactate / pyruvate ratio. This allows us to believe with a high degree of certainty that the activation of anaerobic metabolism processes can be considered the main sign for this cluster.

The introduction into practice of the ratio of the distance to cluster C to the distance to cluster B will make it possible to tentatively judge the relationship between the aerobic and anaerobic metabolic pathways.

As follows from the analysis of cluster A (a cluster of a hyperdynamic reaction or a “stress response”), the basis of its pathophysiological manifestations is primarily a violation of the systemic regulation of vascular tone, which causes a sharp increase in cardiac output and. to compensate, an increase in the volume of the vascular bed. When studying the pathophysiological features of cluster D (“hypovolemic disorders”), the leading one is a decrease in the pumping function of the heart, the compensation of which is provided by a significant increase in vascular tone. At the same time, no increase in the level of anaerobic metabolism is determined in any of these clusters. On the other hand, this relationship is associated with a functional dependence with a developing metabolic imbalance, since its disproportionality, according to one hypothesis, is largely due to a defect in the utilization of aromatic amino acids and the synthesis of “fake neurotransmitters” [34]. In accordance with this, the ratio of the distance to the cluster D to the distance to cluster A can already be used in general terms to judge the state of vascular tone and pumping function of the heart.

Thus, the distances from a particular patient profile to all pathological and control profiles after their conversion are expressed in the form of three numbers: two ratios - D / A and C / B and the distance to the control group.

The resulting graphic display of the dynamics of these indicators in a patient in comparison with the available clinical data allows us to judge the growth of certain pathological processes, as well as to determine, to some extent, the adequacy of the response of the victim to the development of the pathological process. As an illustration, a graph of the dynamics of one of the patients included in our study is given (Fig. 4.15). The graph shows that on the abscissa axis the values ​​of the C / B ratio are plotted, and on the ordinate axis the corresponding D / A values. The dynamics of the patient's condition is defined as a trajectory - a broken line. connecting points at which the indicators of the functional computer monitoring system were determined and the corresponding values ​​of the C / B and D / A ratios were calculated. The sequential number of the sample is indicated near the sampling points, and in brackets is a typical pathophysiological profile, the distance to which was minimal at the time of this study.

Fig. 4.15.

Schematic representation of the dynamics of the condition state according to the system of functional computer monitoring in patient

.



Thus, this chapter presents a methodology for the development and pathophysiological substantiation of typical pathological profiles in patients with severe mechanical trauma, which can be considered as the main clinical images of the course of the post-shock period. Their use allows us to study mathematically the dynamics of the state of each individual victim in accordance with his indicators in the system of functional computer monitoring developed on this basis and to evaluate the quantitative and qualitative manifestation of pathological processes.

In the course of further study, typical clusters (patterns) A, B, C, D, identified by a block of studies relating to a certain period of the development of a process in a particular patient, are often designated as phases A, B, C or D. This designation seems correct to us. since the phase is understood as a specific stage in the development of a pathological process, distinguished on the basis of a paired analysis of clinical signs and pathophysiological mechanisms.
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The principles of the functional computer monitoring system

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