=Paper=
{{Paper
|id=Vol-3628/paper17
|storemode=property
|title=Multiparametric neural network clustering in prediction the risk of surgical complications after revascularization on main arteries of the lower limbs
|pdfUrl=https://ceur-ws.org/Vol-3628/paper17.pdf
|volume=Vol-3628
|authors=Boryslav Selskyi,Andriy Sverstiuk,Petro Selskyi,Sviatoslav Kostiv,Ihor Venher
|dblpUrl=https://dblp.org/rec/conf/ittap/SelskyiSSKV23
}}
==Multiparametric neural network clustering in prediction the risk of surgical complications after revascularization on main arteries of the lower limbs==
https://ceur-ws.org/Vol-3628/paper17.pdf
Multiparametric neural network clustering in prediction the risk
of surgical complications after revascularization on great arteries
of the lower extremities
Boryslav Selskyia, Andriy Sverstiuka,b, Petro Selskyia, Sviatoslav Kostiva and Ihor Venhera
a
I. Horbachevsky Ternopil National Medical University, Maidan Voli, 1, Ternopil, 46002, Ukraine
b
Ternopil National Ivan Puluj Technical University, Rus’ka str. 56, Ternopil, 46001, Ukraine
Abstract
We propose a method for predicting complications of surgical intervention using
multiparameter neural network clustering, subsequently, a hierarchical scale was developed of
surgical risk complications. We analyzed the examination results of 397 patients with occlusive
atherosclerosis of the great arteries of the lower limbs. To optimize the prediction of the risk of
postoperative sequela, neural network clustering was performed on 119 patients applying the
software NeuroXL Classifier for a more detailed analysis of the changes in the results of each
group. The proposed measure of surgical risk stratification for postoperative treatment of the
great arteries of the lower extremities takes into account multifactorial clinical-anamnestic
factors and laboratory instrumentation. It’s characteristic combination of factor’s in condition
of an organ or system influences reconstructive surgery selection and methods based on neural
network clustering data, the level of potential postoperative sequela of surgery on the great
arteries of the lower extremity was perform, followed by four levels of risk factor expression:
very high risk 31-40, high risk 21-30, moderate 11-20, low risk 1-10.
Keywords
Risk scale, atherosclerosis, neural network clustering, limb revascularization, surgical
complications
1. Introduction
The use of nova neural methods and computer modeling in the modern context, especially in the
region of vascular surgery, can significantly improve the quality of the huge amount of information
parameter’s analysis required and provide’s a comprehensive approach to the best surgical choice [1,
2]. Numerous studies have been conducted with the aim of solving the prevention of complications by
taking into account patient parameters and developing relevant preventive risk measures [3]. However,
the problems of using them in practice and of comprehensively considering a multitude of risk factors
remain unresolved [4]. While, the development of a uniform scale to assess the possible risks of surgical
interventions has become very important. The prediction of huge part of complications in patients with
vascular damage and the applying of neural network techniques for their detection remain particularly
applicable [5, 6].
In [7], the approach to forecasting in endocrinology with the selection and justification of the most
important factors is considered. Prediction of the risk of developing hypothyroidism and diffuse non-
toxic goiter in patients with type 2 diabetes mellitus is considered in works [8, 9]. An example of the
use of multivariate regression analysis to assess the severity of Lyme borreliosis is given in works [10,
11].
ITTAP’2023: 3rd International Workshop on Information Technologies: Theoretical and Applied Problems, November 22–24, 2023,
Ternopil, Ukraine
EMAIL: selskyi_bp@tdmu.edu.ua l (A.1); sverstyuk@tdmu.edu.ua (A.2); selskyy@tdmu.edu.ua (A.3); kostivsj@tdmu.edu.ua (A.4);
vengerik@tdmu.edu.ua (A.5)
ORCID: 0000-0001-6787-4843 (A.1); 0000-0001-8644-0776 (A.2); 0000-0001-9778-2499 (A. 3); 0000-0002-7963-5425 (A.4); 0000-
0003-0170-1995 (A5)
©️ 2023 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Prediction and cluster analysis of informative features in cardiodiagnostic systems [12, 13], results
of measurements by biosensors [14] and immunosensors [15, 16] are interesting and promising studies.
The clustering technology of neural networks for the recognition of cartographic images is presented in
[17].
2. Materials and methods
The results of clinical observations, instrumental and laboratory studies have been processed by
variational mathematical statistics method. To process the data, we used Microsoft Excel (2016)
package. In example of normal distribution, statistical significance of the difference between the
arithmetic average was defined with the help of Student's test (T-Test), and in the case of non-normal
distribution – with the help of Mann-Whitney non-parametric test (U-Test) at p<0.05.
For more detailed analysis 119 patients of co-changes to optimize the group's measures in
observational studies and the predict the risk of developing postoperative complications, we have used
a neural network clustering using NeuroXL Classifier for the Microsoft Excel. NeuroXL Classifier
(developed by AnalyzerXL) implements a self-organizing neural network, which handles classification
by studying trends and interconnections within groups.
The main advantages of the k-means method are its simplicity and speed of execution. The k-means
method is more convenient for clustering a large number of observations than the hierarchical cluster
analysis method (in which dendograms become overloaded and lose visibility). The principle of the
algorithm consists in finding such cluster centers and sets elements of each cluster in the presence of
some function F(°), which expresses the quality of the current division of the set into k clusters, when
the total squared deviation of the elements of the clusters from the centers of these clusters will be the
smallest.
The following notation describes the linkages used by the various methods:
• Cluster 𝑟 is formed from clusters 𝑝 and 𝑞.
• 𝑛𝑟 is the number of objects in cluster 𝑟.
• 𝑥𝑟𝑖 is the ith object in cluster 𝑟.
• Single linkage, also called nearest neighbor, uses the smallest distance between objects in the
two clusters.
𝑑(r, s) = min(dist(xri , xsj )), i ∈ (i, . . . , n𝑟 ), j ∈ (1, . . . , n𝑠 )
• Complete linkage, also called farthest neighbor, uses the largest distance between objects in the
two clusters.
𝑑(r, s) = max (dist(xri , xsj )) , i ∈ (i, . . . , n𝑟 ), j ∈ (1, . . . , n𝑠 )
• Average linkage uses the average distance between all pairs of objects in any two clusters.
n𝑟 n𝑠
1
𝑑(r, s) = ∑ ∑ dist(xri , xsj )
n𝑟 n𝑠
𝑖=1 𝑗=1
• Centroid linkage uses the Euclidean distance between the centroids of the two clusters.
𝑑(r, s) = ‖x̅r − x̅s ‖2,
where
n𝑟
1
x̅r = ∑ xri
n𝑟
𝑖=1
• Median linkage uses the Euclidean distance between weighted centroids of the two clusters.
𝑑(r, s) = ‖x̃r − x̃s ‖2,
d(r,s)=‖‖˜xr−˜xs‖‖2,
where 𝑥̃𝑟 and 𝑥̃𝑠 are weighted centroids for the clusters r and s. If cluster r was created by combining
clusters 𝑝 and 𝑞, 𝑥̃𝑟 is defined recursively as
1
x̃r = (𝑥̃𝑝 + 𝑥̃𝑞 )
2
• Ward's linkage uses the incremental sum of squares, that is, the increase in the total within-
cluster sum of squares as a result of joining two clusters. The within-cluster sum of squares is defined
as the sum of the squares of the distances between all objects in the cluster and the centroid of the
cluster. The sum of squares metric is equivalent to the following distance metric 𝑑(𝑟, 𝑠), which is the
formula linkage uses.
2𝑛𝑟 𝑛𝑠
𝑑(𝑟, 𝑠) = √ ‖𝑥̅𝑟 − 𝑥̅𝑠 ‖2,
(𝑛𝑟 +𝑛𝑠 )
where
o ‖ ‖2 is the Euclidean distance.
o 𝑥̅𝑟 and 𝑥̅𝑠 are the centroids of clusters 𝑟 and 𝑠.
o 𝑛𝑟 and 𝑛𝑠 are the number of elements in clusters 𝑟 and 𝑠.
In some references, Ward's linkage does not use the factor of 2 multiplying nrns. The linkage function
uses this factor so that the distance between two singleton clusters is the same as the Euclidean distance.
• Weighted average linkage uses a recursive definition for the distance between two clusters. If
cluster r was created by combining clusters p and q, the distance between r and another cluster s is
defined as the average of the distance between p and s and the distance between q and s.
(𝑑(𝑝, 𝑞) + 𝑑(𝑞, 𝑠))
𝑑(𝑟, 𝑠) =
2
A linkage is the distance between two clusters.
The general advantages of NeuroXL Classifier are simplicity of usage; in-depth knowledge in the
region of neural networks is optional; integration with Microsoft Excel; providing justified neural
network technology for high accuracy classification; determination of interconnections and trends that
cannot be defined by traditional statistical methods [1, 2, 3, 4].
3. Main Part
To model a neural network clustering followed by the development of a surgical risk scale, we have
performed analysis of the indicators of 397 patients with atherosclerosis of the great arteries of lower
limbs. For a more in-depth analysis 119 patients of the combined changes in the performance of the
studied groups in order to optimize the prediction of the risk of complications in the postoperative
period, neural network clustering was performed by using the software NeuroXL Classifier.
To determine the nature and prevalence of atherosclerotic lesions of the arterial bed of great arteries
of lower extremities and to examine patiens, we used ultrasound SonoScape S8 Exp (China) and
tomographic computer study Siemens Brilliance CT64 (Germany) with contrasting of vascular segment.
With angiography of the main vessels of the lower extremities in the conditions of the endovascular
operating room with X-ray we used angiograph Siemens Axiom Artis (Germany).
4. Results and Discussions
Laboratory and instrumental studies, clinical observations results were analyzed, which we entered
in the neural network clustering system for сalculation.
4.1. Average values of indicators
Anesthesiological and laboratory indices of 72 patients were analyzed (group 1). Open surgical
interventions (subgroup 1a) have been used to treat 44 patients (61.1%), endovascular and hybrid
interventions (subgroup 1b) – to treat 28 patients (38.9%). The body mass index (BMI) in patients of
this examination group was 23.39±0.39, (50.0±5.89)% of patients led unhealthy lifestyle. The average
age of patients in the first group was (67.06±1.14) years old. Other indicators, were taken into account:
diabetes ((30.56±5.43) %), respiratory failure ((12.5±3.90 ) %), pathology of the gastrointestinal tract
((15.28±4.24) %), lesions of extracranial segment ((54.14±5.87) %), stroke in history ((5.56±2.70 ) %),
diseases of the cardiovascular system ((95.83±2.35) %), myocardial infarction in history ((23.61±5.01)
%), malignant process in history ((1.39±1.38) %), conduction anesthesia ((13.89±4.08 ) %), mechanical
ventilation ((1.39±1.38) %), epidural anesthesia ((55.56±5.86) %), presence of pulmonary hypertension
((9.72±3.49) %) and revascularization level ((5.56±2.70) %).
(25±5.10)% of patients (subgroup 1c) suffered from side-effects, such as thrombosis of the
reconstruction segment ((19.44±4.66) %), pseudoaneurysm ((2.78±1.94) %), myocardial infarction
((1.39±1.38) %) and suppuration conduit ((4.17±2.35) %). It should be memorize that the average age
((67.5±1.74) years old and BMI (22.64±0.89) of the patients in this subgroup were not significantly
different from the similar indicators of the patients of the first group of our study (р>0.05 ).
We studied biochemical, coagulation and general blood analyses picture indices for all subgroups of
patients. The results are shown in Table 1.
Table 1
Biochemical, coagulation and general blood analyses of patients with open, endovascular and hybrid
surgical interventions (М m)
Indicators 1 group 1а subgroup 1b subgroup 1c subgroup
(n – 72) (n – 44) (n – 28) (n – 18)
Erythrocytes, 4.38±0,06 4.32±0,05 4.48±0,08 4.50±0,28
*
1012/l
Hemoglobin, 128.80±2,41 127.30±2,92 131.18±4,18 125.72±4,97
g/dl
Color index 0.90±0,01 0.91±0,01 0.89±0,01 0.84±0,05
Leukocytes, *109 /l 7.85±0,39 8.16±0,54 7.35±0,54 7.23±0,64
Eosinophils, % 3.26±0,76 2.73±0,36 4.09±1,87 2.72±0,48
Rod-shaped 6.58±0,50 6.75±0,73 6.32±0,60 6.22±0,77
neutrophils, %
Segmented 64.58±1,11 65.66±1,39 62.89±1,82 65.67±1,85
neutrophils, %
Lymphocytes, 22.92±1,19 21.43±1,60 25.25±1,67 22.89±1,89
%
Monocytes, % 3.50±0,33 3.77±0,44 3.07±0,49 3.56±0,74
ESR, mm/hour 18.17±2,06 16.75±2,68 20.39±3,22 16.22±3,51
Glucose, mmol/l 6.23 ± 0,14 5.78 ± 0,10 6.94± 0,18** 5.59± 0,35*
Creatinine, μmol/l 75.46±2,33 70.44±2,64 83.34±3,93** 79.28±3,93
Urea, mmol/l 5.99±0,24 5.65±0,28 6.54±0,43 6.61±0,67
AST, u/l 20.34±1,85 21.29±2,83 18.86±1,73 25.18±6,95
ALT, u/l 20.14±1,34 21.15±1,85 18.56±1,83 24.59±4,30
Bilirubin, 9.81±0,59 9.13±0,70 10.89±1,04 11.31±1,71
μmol/l
К, mmol/l 5.79±1,10 6.54±1,80 4.61±0,14** 5.00±0,25
Na, mmol/l 138.14±0,41 138.91±0,55 136.93±0,53** 138.22±0,77
LDL, mmol/l 3.26±0,14 3.47±0,18 2.92±0,21 3.42±0,28
HDL, mmol/l 1.26±0,05 1.20±0,06 1.35±0,08 1.30±0,11
Cholesterol, 4.56±0,14 4.64±0,20 4.43±0,20 4.74±0,28
mmol/l
Fibrinogen, g/l 4.56±0,18 4.43±0,22 4.77±0,34 4.59±0,39
Prothrombin time, 11.65±0,15 11.81±0,22 11.40±0,18 11.43±0,22
sec.
Prothrombin 96.58±2,15 94.31±2,86 100.17±3,16 99.31±3,88
according to Kwik,
%
INR, index 0.99±0,02 0.99±0,03 0.99±0,02 0.96±0,02
Trombin time, sec. 11.11±0,14 11.28±0,19 10.83±0,21 10.86±0,37
Remark 1. * – p˂0.05 compared to the 1st group.
Remark 2. ** – p˂0.005 compared to the 2nd group.
Ultrasound indices were also analyzed in 47 patients (group2) who survive after open, endovascular
and hybrid operation. All patients were diagnosed with minor stenosis at the level of the aorto/iliac
segment. However, no significant stenosis/obstruction at the level of the aorto-iliac segment was found.
The passability of the femoral segment was identified in (44.68±7.25)% cases, the passability of the
deep femoral artery – in (89.36±4.50)% cases, passability of the superficial femoral artery – in
(34,04±6,91) and the passability of the popliteal segment – in (68.10±6.80)% cases. We have detected
passability of the anterior tibial artery in (68.09±6.80)%, posterior tibial artery in (53.19±7.29)% and
the peroneal artery in (80.85 ±5.74) % of patients. The ankle-brachial index (ABI) was (0.53±0.02) %.
The average sPO2 index before surgery was (83.40±0.81) %, and sPO2 after surgery – (92.21±1.10) %.
4.2. Cluster Analysis
To establish the most important parameter study composite changes to predict the risk of
afteroperative complications, we performed neural network clustering of the study's indicators. At the
same time, the rate of complications (C) in the postoperative period for each patient was defined: "1" in
case of absence of complications, and "2" – in case of presence of some complications. Neural network
clustering of the results of the anamnestic and clinical examination (Fig. 1) was carried out on the basis
of the following indicators: age (1), unhealthy habits (2), body mass index (3), injury of extracranial
segment (4), diabetes decompensated (5), diabetes uncompensated (6), stroke in anamnesis (7),
myocardial infarction in anamnesis (8), gastrointestinal pathology (9), respiratory failure (10),
cardiovascular diseases (11), oncology in anamnesis ( 12), pulmonary hypertension (13), reduced
ejection fraction (14), mid-range reduced ejection fraction (15), thrombosis of the reconstruction part
(16), myocardial infarction (17), pseudoaneurysm (18), suppuration of the prosthesis (19) and C –
indicator of complications in the afteroperative period (20).
Figure 1 shows the results of the indicators clustering program performance. The 1st cluster includes
20.83% of patients, 2nd – 36.11% patients, and 3rd – 43.06% patients.
Figure 1: Results after clustering of patients anamnestic indicators
According to the survey, patients in the first cluster have the highest number of postoperative
complications. With the help of cluster portraits, this cluster was found to have the highest age index.
(1.6%), injury of extracranial arteries (3.8%), diabetes in the de- and subcompensation stages (4.4%)
and respiratory failure (6.7%), as compared to other clusters. The rate of stroke in the anamnesis (1.1%)
exceeded the similar rate in the 3rd cluster, and the rate of diseases of the cardiovascular system (0.7%)
– exceeded the rate in the 2nd cluster.
A neural network clustering of the results of instrumental-laboratory research (Fig. 2) has been
performed based on a number of indicators: erythrocytes (1), content of hemoglobin (2), color index
(3), leukocytes (4), eosinophils (5), neutrophils rod-shaped ( 6), neutrophils segmented (7), lymphocytes
(8), monocytes (9), ESR (10), glucose (11), creatinine (12), urea (13), AST (14), ALT (15), bilirubin
(16), K (17), Na (18), LDL (19), HDL (20), cholesterol (21), time of prothrombin (22), Kwik
prothrombin (23), INR (24), time of thrombin ( 25), fibrinogen (26), fraction ejection (27), allo-graft
(28), fundoplasty of deep artery (29), autovenous graft (30), hybrid surgery (31), stenting (32), balloon
angioplasty (33), thrombosis of reconstruction segment (34) ), infarction of myocardium (35),
pseudoaneurysm (36), prosthesis suppuration (37), and C – is an indicator of complications in the
postoperative period (38).
Figure 2: Results of clustering of indicators program performance. 1st cluster includes 51.39% of
patients, 2nd – 20.83%, and 3rd cluster – 27.78%.
Figure 2 illustrates the results of clustering of indicators program performance. 1st cluster includes
51.39% of patients, 2nd – 20.83%, and 3rd cluster – 27.78%.
The biggest value of the complications parameter in the afteroperative period was found out in the
2nd cluster. With cluster portrait, we have come to result that the 2nd cluster includes the highest number
of monocytes (10.5%), erythrocytes (5.4%), AST (36.1%), ALT (34.7%), potassium (2.7%), creatinine
levels (5.4%), bilirubin (26.6%), low-density lipoproteins (6.6%), and cholesterol (5.34%). Indicators
of urea (2.2%) and prothrombin according to Kwik (3.1%) exceeded those in the 1st cluster.
Clustering of ultrasound results with neural networks was also performed in greater depth than in
previous studies (Fig. 3) based on the following indicators: aorto-iliac segment ultrasound (1), femoral-
popliteal segment ultrasound (2), stenosis at the level of the aorto-iliac segment hemodynamically
insignificant (3) , stenosis/occlusion at the level of the aorto-iliac segment hemodynamically significant
(4), femoral segment patency (5), deep femoral artery patency (6), superficial femoral artery patency
(7), popliteal segment patency (8), posterior tibial artery patency (9), anterior tibial artery patency (10),
peroneal artery patency (11), ankle-brachial index (12), before surgery sPO2 (13), after surgery sPO2
(14), revascularization level (15), thrombosis of the reconstruction segment (16), infarction of
myocardium (17), embolism (18), pseudoaneurysm (19), prosthesis suppuration (20) and C is an
indicator of complications in the afteroperative period (21).
As shown in Figure 3, the highest indicator value of complications in the afteroperative period was
found in the 3rd cluster. With the help of a cluster portrait, it can be determined that this cluster also had
the lowest values of patency in femoral segment (-17.1%), patency of superficial segment (-15,5%),
peroneal artery patency (-4,2%), as well as the ankle-brachial index (-1.5%). The patency of posterior
tibial artery value (-4,3%) and patency of anterior tibial artery value (-4,8%) in the 3rd cluster was lower
compared to the 1st cluster.
Clusters profiles Cluster 1 Cluster 2 Cluster 3
25,00%
20,00%
15,00%
10,00%
5,00%
0,00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
-5,00%
-10,00%
-15,00%
-20,00%
Figure 3: The biggest indicator value of complications in the afteroperative period was found in the 3rd
cluster. On this cluster portrait, it can be noted that this cluster also had the lowest values of patency in
femoral segment (-17.1%), patency of superficial segment (-15,5%), patency of peroneal artery (-4,2%),
as well as the ankle-brachial index (-1.5%).
4.3. Risk scale
Based on the results of clustering by neural networks, we identified the groups of anatomic,
laboratory, and ultrasound indicators that are most important for predict the risk of postoperative
complications. The resulting neural network clustering results were incorporated into the NeuroXL
Classifier program to create a scale to determine the risk of afteroperative complications. At the same
time, the limits of the indicator are determined based on the values defined by the patient clustering.
The coefficient value of the indicator was set as the ratio of the percent age of the indicator in a given
cluster to the minimum percent age of the indicator. The coefficient was set at1.0. As a result, the cluster
with the highest number of complications had the smallest percentage (0.70%) of indicators of
cardiovascular disease. This indicator isused as the unit of measure. Thus, the next highest percentage
of stroke (1.07%) exceeded the previous percentage by 1.5, resulting in a coefficient of 1.5. The
coefficients for the other anesthesia, laboratory, and ultrasound indices were also defined as the most
important predictors based on clustering and determined in a similar manner. Note that although the
prediction based on clustering did not assign to the most important groups, adding indicators that are
risk factors for the development of complications according to the results of other studies to the scale
resulted in a minimum coefficient of 1.0.
To unify the definition of risk levels, all coefficient values formed a scale of anamnestic (Table 2),
examination (Table 3), symptomatic ultrasound (Table 4), and contralateral ultrasound (Table 5) indices
of the patient's extremities, which were converted to a 10-point scale according to the direction of the
study. Each had a maximum score of 40 points. All clustering analysis indices, e.g., anesthesia,
examination, and ultrasonography indices for the symptomatic and contralateral limbs, are included in
the NeuroXL Classifier program to define their point values. The names of some indicators, including
ultrasound, have been changed to facilitate their use in vascular surgery.
Table 2 shows сoefficients and point values for anamnestic indicators of patients with open and
endovascular surgical interventions.
Table 2
Coefficients and scores values for anamnestic indicators of patients with open and
endovascular/hybrid surgical interventions
Indicator Coefficient Score
Age ≥ 65 years 2,3 0,7
Bad Habits 1,0 0,3
Body mass index ≥ 22,6 1,0 0,3
Carotid disease atherosclerotic genesis 5,4 1,6
Diabetes (in the stage of 1,0 0,3
compensation)
Diabetes (in the stage of sub- and 6,2 1,8
decompensation)
History of stroke 1,5 0,4
History of infarction of miocardium 1,0 0,3
Failure of respiratory tract 9,5 2,8
Pulmonary hypertension 1,0 0,3
Diseases of the cardiovascular system 1,0 0,3
(cardiosclerosis, heart failure І-ІІ stage,
coronary heart disease,)
Heart failure with reduced left 1,0 0,3
ventricular ejection fraction ≤ 49%
Gastrointestinal tract pathology 1,0 0,3
Oncological diseases history 1,0 0,3
Table 3 shows сoefficients and scores values for laboratory indicators of patients with open and
endovascular surgical interventions.
Table 3
Scores and coefficients values for laboratory indicators of patients with open endovascular and hybrid
surgical interventions
Indicator Value Coefficient Score
Erythrocytes ≥4,5 2,5 0,4
Monocytes ≥3,6 4,8 0,8
Creatinine ≥79,3 2,5 0,4
Urea ≥6,6 1,0 0,1
AST ≥25,2 16,4 2,6
ALT ≥24,6 15,8 2,5
Bilirubin ≥11,3 12,1 1,9
К ≥5,0 1,2 0,2
LDL ≥3,4 3,0 0,5
Cholesterol ≥4,7 2,4 0,4
Thrombin time ≥99,3 1,4 0,2
Table 4 shows сoefficients and scores values for indicators of ultrasound examination of the
symptomatic limbs of patients with open and endovascular surgical interventions.
Table 4
Scores and coefficients values for indicators of ultrasound examination of the symptomatic limbs of
patients with open endovascular and hybrid surgical interventions
Indicator Coefficient Score
51-70% stenosis in the aorto/iliac segment 1,0 0,1
≥ 71% stenosis or occlusion in the aorto- 1,0 0,1
iliac segment
≥ 71% stenosis or occlusion of the femoral 26,7 3,4
segment
≥ 71% stenosis or occlusion of the deep 1,0 0,1
femoral artery
≥ 71% stenosis or occlusion of the 24,1 3,0
superficial femoral artery
≥ 71% stenosis or occlusion of the 1,3 0,2
a.poplitea
Stenosis/occlusion of a. tibialis posterior 6,7 0,9
Stenosis/occlusion of a. tibialis anterior 7,5 1,0
Stenosis/occlusion of peroneal artery 6,5 0,8
Ankle-brachial index ≤ 0,53 2,3 0,3
sPO2 before surgery ≤ 83,4 1,0 0,1
Table 5 shows сoefficients and points values for ultrasound examination indicators of the contra-
lateral limbs of patients with open and endovascular surgical interventions.
Table 5
Points and coefficients values for ultrasound examination indicators of the contra-lateral limbs of
patients with open endovascular/hybrid surgical interventions
Indicator Coefficient Score
51-70% stenosis in the aorto/iliac segment 1,0 0,1
≥ 71% stenosis or occlusion in the aorto-iliac 1,0 0,1
segment
≥ 71% stenosis or occlusion of the femoral 26,7 3,4
segment
≥ 71% stenosis or occlusion of the deep 1,0 0,1
femoral artery
≥ 71% stenosis or occlusion of the superficial 24,1 3,0
femoral artery
≥ 71% stenosis or occlusion of the a.poplitea 1,3 0,2
Stenosis/occlusion of a. tibialis posterior 6,7 0,9
Stenosis/occlusion of a. tibialis anterior 7,5 1,0
Stenosis/occlusion of peroneal artery 6,5 0,8
Ankle-brachial index ≤ 0,53 2,3 0,3
sPO2 before surgery ≤ 83,4 1,0 0,1
Table 6 shows scoring system for assessing the risk of developing complications.
Table 6
Scoring system for assessing the risk of developing complications.
Anamnestic parameters
Indicator Score
Age ≥ 65 years 0,7
Bad Habits 0,3
Body mass index ≥ 22,6 0,3
Carotid disease atherosclerotic genesis 1,6
Diabetes (in the stage of compensation) 0,3
Diabetes (in the stage of sub- and decompensation) 1,8
History of stroke 0,4
History of infarction of miocardium 0,3
Failure of respiratory tract 2,8
Pulmonary hypertension 0,3
Diseases of the cardiovascular system (cardiosclerosis, heart 0,3
failure І-ІІ stage, coronary heart disease,)
Heart failure with reduced left ventricular ejection fraction ≤ 0,3
49%
Gastrointestinal tract pathology 0,3
Oncological diseases history 0,3
Laboratory indicators
Indicator Score
Erythrocytes ≥ 4,5 *1012 /l 0,4
Monocytes ≥ 3,6 % 0,8
Creatinine ≥ 79,3 μmol/l 0,4
Urea ≥ 6,6 mmol/l 0,1
AST ≥ 25,2 u/l 2,6
ALT ≥ 24,6 u/l 2,5
Bilirubin ≥ 11,3 μmol/l 1,9
К ≥ 5,0 mmol/l 0,2
LDL ≥ 3,4 mmol/l 0,5
Cholesterol ≥ 4,7 mmol/l 0,4
Thrombin time ≥ 99,3 % 0,2
Ultrasound examination indicators of symptomatic limb
Indicator Score
51-70% stenosis in the aorto/iliac segment 0,1
≥ 71% stenosis or occlusion in the aorto-iliac segment 0,1
≥ 71% stenosis or occlusion of the femoral segment 3,4
≥ 71% stenosis or occlusion of the deep femoral artery 0,1
≥ 71% stenosis or occlusion of the superficial femoral 3,0
artery
≥ 71% stenosis or occlusion of the a.poplitea 0,2
Stenosis/occlusion of a. tibialis posterior 0,9
Stenosis/occlusion of a. tibialis anterior 1,0
Stenosis/occlusion of peroneal artery 0,8
Ankle-brachial index ≤ 0,53 0,3
sPO2 before surgery ≤ 83,4 0,1
Ultrasound examination indicators of contralateral limb
Indicator Score
51-70% stenosis in the aorto/iliac segment 0,1
≥ 71% stenosis or occlusion in the aorto-iliac segment 0,1
≥ 71% stenosis or occlusion of the femoral segment 3,4
≥ 71% stenosis or occlusion of the deep femoral artery 0,1
≥ 71% stenosis or occlusion of the superficial femoral 3,0
artery
≥ 71% stenosis or occlusion of the a.poplitea 0,2
Stenosis/occlusion of a. tibialis posterior 0,9
Stenosis/occlusion of a. tibialis anterior 1,0
Stenosis/occlusion of peroneal artery 0,8
Ankle-brachial index ≤ 0,53 0,3
sPO2 before surgery ≤ 83,4 0,1
Based on the results obtained from the scoring system to define the risk of postoperative
complications, we scaled the risk level of postoperative complications in patients with main artery
disease undergoing open endovascular and hybrid surgery (Table 7). Thus, the risk of developing
postoperative complications was defined as the total score of all directions of our study: low risk 1 to
10points, moderate 11 to 20points, high risk 21 to 30points, and very high risk 31 to 40 points.
Table 7 shows cumulative scoring system (total points in all areas of research) for assessing the risk
of developing complications.
Table 7
Scoring system (all areas of research total points) for assessing the risk of developing complications.
Risk rate Total points
Risk are low 1–10
Risk are moderate 11–20
Risk are high 21–30
Risk are very high 31–40
The following is a suggested range of scales for defining postoperative complications: SVS WIfI
(2019), GLASS (2019), EuroSCORE II (2012), CRAB/2YLE (2013), TASC II (2007), Caprini (1991)
[18, 19, 20]. Each considers individual criteria for the pathology of organs and their impact on major
pathologies.
The proposed measure of risk stratification for the development of afteroperative complications
from surgical treatment of the great arteries of the lower extremity takes into account the multifactorial
nature of clinical, laboratory, and experimental measurement studies [21, 22, 23]. Consideration of the
combination of risk factors that shown the condition of the organ or system influences the choice and
method of reconstructive treatment [24, 25, 26].
4.4. Development web page based on a scoring system for assessing the risk
of developing postoperative complications
Based on the results of the scoring system for determining the risk of afteroperative complications
[27, 28, 29], the web page was developed fig. 4, for ease of use on any device in Ukrainian and English
with a visualized scale, where you need to put a check mark under the selected sign and an automatic
calculation of parameters will take place with an illustration of the actual result for a patient with
diseases of the magistral arteries of the lower limbs [30, 31], within risk are low 1–10, risk are moderate
11-20, risk are high 21-30 and 31-40 risk are very high
Figure 4: Webpage by applying neural network clustering in the assessment of the risk of developing
postoperative complications of surgical interventions on the magistral arteries of the lower extremities
considering anamnestic/laboratory indicators
Figure 5 shows neural network clustering in the assessment of the risk of developing postoperative
complications of surgical interventions on the great arteries of the lower extremites considering
ultrasound exam (indicators of symptomatic and contralateral limb) [32, 33].
Figure 5: Neural network clustering in the assessment of the risk of developing postoperative
complications of surgical interventions on the great arteries of the lower extremites considering
ultrasound exam (indicators of symptomatic and contralateral limb).
5. Conclusions
In order to predict the likelyhood of postoperative complications from surgical intervention on the
magistral arteries of the lower extremity, risk levels for the development of complications were defined
by clustering the indices of clinical, anesthetic, and laboratory equipment tests in a neural network and
processing them with the NeuroXL Classifier program.
Four levels of risk of developing complications were defined based on the level of determination of
the likelihood of afteroperative complications of surgery on the great arteries of the lower extremities:
risk are low 1–10, risk are moderate 11-20, risk are high 21-30 and 31-40 risk are very high.
Acknowledgements
There were no external sources of funding and support. No fees or other compensation were paid.
The authors who participated in this study declared that they have no conflict of interest regarding this
manuscript.
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