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Methodology for the development of a functional computer monitoring system

In the period from 1988 to 1992, 207 patients with severe mechanical trauma who were admitted to the clinic by ambulance were examined at the clinic of field surgery.

As a result of a thorough verification of the data, the observations with obvious technical errors were deleted, as well as the data of three victims who died shortly after the injury (during the first day). The cause of death was massive irreversible blood loss. Thus, 511 blocks of observations were selected for further analysis.

All of them contained a basic block, which included 14 direct indicators: systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm) - heart rate, respiratory rate per minute - BH. heart stroke volume (l) - UO, partial pressure of oxygen in arterial and venous blood (mm Hg) - Pao2 and P2O2 - respectively, partial pressure of carbon dioxide in arterial and venous blood (mm Hg) - Pao2 and P2O2, respectively, the acidity of arterial and venous blood - pH and pHv, respectively, the level of hemoglobin (Hb) in the blood (g / l), the saturation of hemoglobin with oxygen in arterial and venous blood (in percent) - НbО2а and Hb02v, respectively.

The use of these indicators allows us to evaluate almost all parts of the oxygen budget and, which is especially important, to study the relationship between oxygen delivery to tissues and its consumption, which changes significantly with the development of endotoxemia [5. 8. 20]. After calculations using the appropriate formulas to create a functional computer monitoring system, the following direct and calculated indicators were selected: stroke index (SI), cardiac index (SI), heart rate (HR), systolic index of the left ventricle of the heart (SRI_I), index minute work of the left ventricle of the heart (MRL_I), the index of total peripheral resistance (OPS_I), mean arterial pressure (ADsr), oxygen gradient between arterial and venous blood (A-VO ^), oxygen consumption a (PO2), indicators of the acid-base state of arterial and venous blood - pH and pHv. partial pressure indicators of oxygen and carbon dioxide in the arterial and venous blood of Pao2, Pvo2, Raso2, and Pvco2.

From the above indicators, it was necessary, using a special mathematical apparatus, to select the optimal number of features that reliably describe this array. To this end, the data array with the above indicators was investigated using the method of “principal components”. As a result of the analysis, eight indicators were selected that have the maximum specific gravity in the selected “main components”.
They are given in table. 4.1.

After identifying the indicators, the data array was processed using one of the varieties of cluster analysis - cluster analysis of observations.

Table 4.1

The proportion of indicators selected by the method of principal components



The essence of the method consists in isolating by an initial method a predetermined number of clusters based on certain attributes by identifying their most stable combinations [21]. The n-dimensional cluster space formed in accordance with this (in accordance with the specified number of clusters) is examined by checking the Fisher criterion whether the selection is reliable or not. During the cluster analysis with the variables selected by us as a result of the preliminary analysis by the method of “principal components”, the following results were obtained (Table 4.2.).

The table shows the formed stable clusters identified during the analysis. Data on the reliability of the separation of these signs into these clusters are given in table. 4.3. The data obtained show a reliable (p = 0.000) allocation of these cluster formations in this array, which is confirmed by the one shown in Fig. 4.1 graph of the reliability of the differences obtained during the cluster analysis in the output of the program.

Table 4.2

The results of cluster analysis on the selected eight variables in an array of 511 observations



Table 4.3

Reliability of differences of indicators divided into clusters



CLUSTER PROFILES - VARIABLES ARE ORDERED BY F-RATIO SIZE

EACH COLUMN DESCRIBES A CLUSTER.

THE CLUSTER NUMBER IS PRINTED AT THE MEAN OF EACH VARIABLE DASHES INDICATE ONE STANDARD DEVIATION ABOVE AND BELOW

Fig. 4. l.

Graphical reflection of the significance of differences by grouped characteristics in the formed clusters

.



The presented data show a high reliability of differences in the features that form the cluster image. To verify the significance of differences, the results were processed in pairs by a multiple t-test. Their study fully indicates a highly reliable separation of characters and data blocks included in the array (corresponding to individual clinical observations) into typical pathophysiological clusters.
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Methodology for the development of a functional computer monitoring system

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    Proposed by JHSiegel et al. | 33. 35], the system of functional computer monitoring is based on the assumption that the application of the mathematical methodology of cluster analysis allows us to distinguish typical clinical images (as the author himself calls them, patterns [31. 32 |). The pathogenetic basis for determining such clinical images in certain critical conditions (trauma, sepsis)
  2. Functional computer monitoring system in the diagnosis of sepsis
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