Detection of the Mild Myocardial Injury during Percutaneous Coronary Intervention Using Evaluation of Minor Changes on Electrocardiogram and Heart Rate Variability

doi:10.21203/rs.3.rs-4337869/v1

Background

As cardiovascular mortality continues to increase globally, Percutaneous Coronary Intervention (PCI) with stent placement stands out as a cutting-edge and highly effective treatment for severe cardiovascular diseases. However, the inherent invasiveness of any endovascular procedure introduces the risk of coronary vessel and myocardial damage. This study aimed to evaluate the utility of a novel electrocardiographic metric for detecting subtle myocardial injuries after coronary stenting.

Materials and Methods

This investigation was conducted in 2021 at the Kyiv Heart Institute of the Ministry of Healthcare of Ukraine. The study involved 23 patients who underwent PCI, each subject to a meticulous preoperative examination. A paired measurement approach was employed, encompassing 3-minute ECG recordings both before and several hours after the operation, using a compact ECG device. Each pair of electrocardiograms underwent thorough analysis, scrutinizing 240 primary and computed ECG parameters.

Results

The analysis delineated a distinct subgroup that exhibited significant myocardial damage after stenting. This subgroup was characterized by an older average age and more stents than their counterparts. Notably, a concurrent reduction in the psychoemotional state index was observed alongside ECG alterations in these patients, suggesting a correlation between myocardial damage and psychoemotional distress.

Discussion

The introduction of a new electrocardiographic index has illuminated the subtle myocardial damage incurred during PCI. The findings of this study underscore the complex interplay between physical cardiac trauma and psychological stress, highlighting the importance of a comprehensive approach to post-PCI patient assessment.

Conclusions

The newly devised electrocardiographic metric proved to be a significant advancement in the early detection of myocardial damage after PCI. The metric's ability to capture not only physiological but also psychoemotional changes is a pivotal step toward more integrative post-operative patient care, paving the way for enhanced recovery protocols and personalized treatment strategies in interventional cardiology.

electrocardiography

coronary artery stenting

scaling

myocardial injury

informative ECG parameters

In recent decades, despite scientific progress, the number of people with cardiovascular disease (CVD) has been increasing worldwide, mainly due to the worldwide dominance of the obesity epidemic worldwide. Cardiovascular mortality increases relentlessly with each passing year. It is responsible for approximately half of all lethal cases in the United States and Europe [1, 2]. According to the American Heart Association (AHA) data for the past year, 7.5% of men and 6.2% of women suffer from coronary heart disease (CHD), while 3% of citizens over the age of 20 experience myocardial infarction. In Ukraine, the number of CHD patients is rapidly approaching 10 million, confidently taking the lead.

Therefore, there is an urgent need to improve methods for heart disease detection. First, it applies to noninvasive methods that are the most accessible and safe.

Analysis of electrical activity of the heart is still the most common, affordable, and cheapest method for objective examination of the heart. However, the sensitivity and specificity of routine electrocardiographic examinations are not sufficiently high. For example, it is known that resting ECG, assessed by its routine criteria, remains normal in approximately 50% of patients with chronic coronary heart disease, including during episodes of chest discomfort [3]. Improving diagnostics is only possible by implementing innovative technologies.

Among the other treatment options for severe forms of CHD, X-ray-controlled endovascular angioplasty with stent placement (Percutaneous Coronary Intervention or PCI) is one of the most effective and up-to-date. The introduction of new diagnostic and treatment options for cardiac patients, namely, the increase in the number and quality of coronary artery stenting, presents new opportunities for patients. The number of PCI, among all surgical interventions for CVD, is increasing annually. According to the Medical Statistics Center of the Ministry of Healthcare of Ukraine, in three years (from 2014 to 2017), the number of endovascular interventions on heart arteries in Ukraine doubled [4].

However, any endovascular intervention on its own inevitably causes damage to the coronary vessels and myocardium. The resulting PCI vascular trauma over a relatively short period (from weeks to months) triggers a complex inflammatory and reparative process that leads to endothelialization and formation of a neointimal pathological lining. According to histopathological findings, post-stent implantation neointimal hyperplasia mainly consists of proliferating smooth muscle cells in the proteoglycan rich extracellular matrix [5].

Therefore, since the first years of the widespread use of X-ray-monitored endovascular coronary interventions, researchers have been aiming at developing diagnostic methods of myocardial damage and clearing up its prognostic value for mortality, recurrent need for revascularization, and myocardial infarction rate during the first several years after the intervention [6–8].

Currently, such damage is conventionally diagnosed by detecting perioperative elevation of myocardial biomarkers suchas CK-MB and troponin. Numerous studies on the link between elevated CK-MB and troponin levels and mortality and serious cardiovascular events (especially from a short-term perspective) have been conducted; however, the results of these studies were non-homogeneous. Many questions remain controversial, particularly regarding the CK-MB and troponin prognostic value ratio (the data about the significance of elevated troponin is especially controversial), threshold levels at which biomarker elevation gains prognostic value, and general perioperative indications for measuring these markers.

The uncertainty of these aspects and the desire to minimize costs result in most hospitals not conducting routine perioperative measurements of myocardial biomarkers. Even in the United States, this is true for approximately 75% of hospitals [9]. At the same time, there is a trend of lower mortality and higher compliance in hospitals that regularly conduct perioperative measurement of the biomarkers mentioned above, reflecting the better overall quality of care in these hospitals.

In earlier studies on myocardial damage during endovascular coronary interventions, analysis of myocardial markers was combined with electrocardiographic analysis. First, the formation of pathological Q waves was investigated. It has been found that this wave rarely forms during endovascular angioplasty and coronary stenting. Recently, there has been decreased interest in electrocardiography as a method of perioperative control of myocardial damage.

In this context, the advanced analysis of ECG might be highly demanded.

The only way to increase the diagnostic value of ECG examination is to develop proper information technology (IT) – i.e. combination of up-to-date methods and equipment bound in a chain that provides collection, storage, preprocessing, interpretation, conclusion, and dissemination of information.

The advancement of diagnostic methods, especially instrumental methods (i.e., methods of functional diagnostics), primarily entails constant increases in the ability to detect subtle changes in the function examined by one method or another.

Such opportunities emerge due to progress in technical measurement tools of a certain function and even more due to the development of informational technologies; in other words, due to the creation of new metrics — numerical parameters using which one can assess the aspects of functioning of various human organs and systems that were previously inaccessible.

As a result, new ways of improving the diagnostic accuracy of a certain method within its traditional application scenarios are discovered. Second, familiar methods find unconventional uses in new areas.

In this context, an innovative technology to analyze subtle changes in ECG was developed, aiming to make any electrocardiography informative [10]. Routine ECG analysis is based on the presence of certain ECG syndromes or phenomena defined within one of the existing ECG analysis algorithms. However, in the majority of cases no ECG syndrome can be identified during the analysis of an individual electrocardiogram, at least not one that clearly reflects cardiac pathology i.e., belongs to the “major” category according to the, for example, Minnesota coding system. During routine analysis, one is forced to assign a single class to all these electrocardiograms — electrocardiograms with no major ECG syndrome identified. However, the following question arises: are all these electrocardiograms the same in terms of their relative “distance” to the “ideal” electrocardiogram of a healthy human? Obviously, this is not the case. This “distance” can be further or closer depending on the myocardial condition; moreover, there is a reasonable hypothesis that this “distance” reflects the likelihood of serious cardiovascular events occurring. This is where routine electrocardiogram analysis is uninformative.

This is why the Universal Scoring system method and software for ECG scaling that can provide a quantitative evaluation of the slightest changes in ECG signals were developed.

This approach is based on, first of all, measuring the maximum number of ECG parameters and heart rate variability and, secondly, on positioning each parameter value on a scale between the absolute norm and extreme pathology. In fact, the suggested approach follows a popular Z-scoring ideology, when quantitative, usually point-based assessment of test results is determined using a special scale containing data about intra-group test result variation. To calculate the Z-score mean test value of the group, its standard deviation is required [11].

This study aimed to investigate the value of a new electrocardiographic metric for detecting subtle myocardial damage due to coronary stenting.

2.1 Patient group

The study was conducted on May 1st 2021, using the Kyiv Heart Institute of the Ministry of Healthcare of Ukraine facilities. The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Shupik National Healthcare University of Ukraine, (protocol code №5 and date of approval 04/04/2021).

Written informed consent was obtained from all subjects involved in the study.

23 patients were examined with a mean age of 58.8 ± 11.2 years. All the patients underwent coronary artery stenting. Fifteen patients underwent PCI as a part of acute Q-infarction treatment, 8 as a treatment for chronic CAD, and stable angina CCS class II-III. All the patients received intraoperative analgesia. The mean duration of the intervention was 40.2 ± 21.1 minutes, while the number of stents was 1.8 ± 0.9.

The exclusion criteria were age > 75 years, liver dysfunction, progressive renal failure, acute and chronic infection, cardiac insufficiency, anemia, inflammation, peripheral vascular disease, pregnancy, suspected systemic thrombotic diseases, diabetes, cancer, other heart diseases, thyroid dysfunction, and autoimmune diseases.

Preoperative clinical and laboratory parameters as well as anthropometric values are presented in Table 1.

Table 1. Demographics and selected clinical features of the patients who have undergone coronary stenting.

Parameter	Value
Age (years)	63.4±5.3
Sex (m/f)	16/7
BMI (kg/m²)	28.4±5.2
AH (n (%))	20 (86.9%)
Hb (g/L)	136.3±17.1
Ht	41.2±4.4
Тr (*10^9/L)	231.3±38.2
PTI	93.4±10.5
Urea (mmol/L)	6.4±2.2
Cr (μmol/L)	94.7±22.5
Ischemic heart disease	23 (100%)
Heart failure	21 (91.3%)
Pulmonary hypertension	7 (30.4%)
Pulmonary disease	9 (39.1%)
Vessel disease	12 (52.2%)

Abbreviations: BMI, body mass index; AH, arterial hypertension; Hb, hemoglobin; Ht, hematocrit; Tr, platelet count; PTI, prothrombin index; urea, urea; Cr, creatinine.

2.2 ECG and heart rate variability scaling method

After the surgical intervention, all patients underwent a modified computer ECG with subsequent analysis using an innovative scaling method.

Recently, an innovative ECG and heart rate variability scaling method was developed by the Institute of Cybernetics of the National Academy of Sciences of Ukraine. The original software was developed using this method.

The program was developed on the basis of a hierarchical principle. It consists of the four levels listed below in ascending order:

1) The lowest level is comprised of multiple separate parameters describing a) various aspects of heart rate variability, b) amplitude and time parameters, as well as the shape of ECG waves, and c) the presence of main rate, rhythm, and sequence of myocardial contraction abnormalities (in other words, arrhythmias).

2) The second level is comprised of groups of related parameters with close to each other physiological sense:

3) The third level is represented by three integral sections, each of which represents a different aspect of cardiovascular system functioning that can be assessed through ECG: the sections of regulation assessment, state of the myocardium, and arrhythmia diagnostics.

4) The fourth, highest level — the collective integral criteria of the cardiovascular system’s functional status.

Multiple quantitative parameters registered by the program and used for analysis had different units of measurement (s, mV, etc.) or none at all. Naturally, there is a problem with converting data to compact and suitable analysis formats that would also be convenient for making conclusions and decisions, that is, switching to, for example, dimensionless parameters. To solve this problem, a functional scaling method was used. The interval scale from 0 to 100 notional points was divided into four equal intervals: 0–25, 26–50, 51–75, 76–100 is used. These intervals match four status grades: normal, mild, moderate, and severe abnormalities. The median value of the normal range of each parameter in absolute units (e.g., in seconds) was taken as 100 points on the interval scale of functional status used. Thus, for each parameter, four intervals of absolute values were determined, which matched four equally wide (25 points each) intervals on the scale we used. In the next step, linear relations between discrete parameter values in absolute units and the corresponding number of points for this discrete value are established within each interval. Consequently, a linear correspondence scale between the absolute parameter values and the number of points on the functional status scale was obtained for each parameter. With the transfer to higher analysis levels, generalization and aggregation of information obtained at the previous level occurs. The composite index, present in this software, was formed based on the evaluation of the conventional and original parameters of heart rate variability, as well as the characteristics of ECG waves and complexes.

The suggested scaling method was invented precisely for solving practical problems and already has widespread applications [12–14] in solving various tasks in different areas of clinical medicine, sports medicine, and large-scale population studies, including the analysis of large ECG data arrays of the Oxford Population Health study.

It is worth mentioning that the suggested scaling method includes both a series of adapted conventional algorithms for physician-led ECG assessment (cardiac rhythm abnormalities and morphological analysis of ECG figures) and ECG analysis algorithms that have proven prognostic value for predicting serious cardiovascular events in large international studies. Finally, an analysis of psychoemotional state was performed through HRV analysis using a modified R. McCraty algorithm based on the neurovisceral integration model [15].

2.3 Registration and analysis of ECG and heart rate variability

In this study, paired measurements were used — 3 minute-long ECG registration both prior to and several hours after the surgery. The examination was conducted using a fully certified “Cardio + P” innovative device, developed by V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences and manufactured by “Metekol” LLC. A total of 23 pairs of electrocardiograms were analyzed. 240 primary and calculated ECG parameters were analyzed for each electrocardiogram, among which 11 were composite parameters (from 0 to 100 points).

Statistical processing of the results was conducted using the STATISTICA 10 software package. Quantitative values are presented as mean ± standard deviation (in the case of normal distribution). Anomaly detection in the data (presence of outliers) was conducted using the variance range criteria and Kolmogorov-Smirnov test. The Shapiro-Wilk's W test, recommended for small samples, was used to test the hypothesis about the type of distribution. Homogeneous groups were identified among patients using cluster analysis (K-means clustering algorithm). Repeated measures analysis of variance (repeated measures ANOVA) was used to analyze the impact of the stenting factor. The effect of this factor was determined in this study using partial eta squared (ɳ²). The statistical significance of mean value change in overall composite parameters before and after stenting was tested using a t-test for two dependent samples (in case of normal distribution), and in all other cases using a non-parametric Wilcoxon test. The P-value significance level was set to 0.05, in compliance with the conventional medico-biological study guidelines.

Simple visual analysis of composite parameter diagrams before and after stenting showed that the studied group was nonhomogeneous, indicating the probable presence of separate clusters.

Furthermore, significant individual differences were observed in the values of composite indicators at the beginning of the study and in the trends of the indicators, which is another sign of the heterogeneity of the group.

K-means cluster analysis was applied to identify homogeneous groups (clusters). Homogenous groups were formed based on four criteria representing differences in composite parameter values before and after stenting. For this purpose, composite parameters that changed the most and met the conditions of the K-means algorithm application — normal distribution were selected. These parameters were as follows: “Composite myocardial status parameter” (CP MS), “Composite functional status assessment”(CA FS), “Composite serious cardiovascular events risk assessment” (CA CVE), and finally “Universal score” (Uscore).

Using the K-means algorithm with 10-fold cross-validation, three clusters were identified that were most different from each other in composite parameter changes such as regulation (CA FS) and myocardial status (ΔCP MS). The use of cross-validation allows the identification of the optimal number of clusters and minimizes the error in assigning a patient to a certain cluster. Cluster 1 included five patients with the most negative composite parameter changes (decrease compared with the initial values). Cluster 2 included 7 patients with positive deviations (the end values were higher).Cluster 3 – includes 11 patients with insignificant deviations in composite parameters. Table 2 shows the changes in the composite parameters and number of patients for each of the three clusters.

Basic patient preoperative parameter analysis in different clusters showed that patients from the first cluster were somewhat older (63.7 years vs 58.5 years in cluster 2 and 54.1 years in cluster 3). Patients in cluster 1 also had a higher average number of implanted stents – 1.7 vs 1.2) than those in the other two clusters. The majority of patients in cluster 3 had chronic CAD (7 out of 11) in clusters 1 and 2 from acute myocardial infarction.

Table 2. Mean changes (Δ) in composite parameters after stenting in the group as a whole and in subgroups (clusters).

	Cases	ΔCP MS	ΔCA FS	ΔCA CVE	ΔUscore	P
The whole group	23	-2.2	-2.3	-0.3	-2.5	-
Cluster 1	5	-9.2	-20.0	-26.2	-17.4	0.002560
Cluster 2	7	6.3	13.9	10.3	9.7	0.000001
Cluster 3	11	-4.4	-4.6	4.6	-3.5	0000015

Abbreviations: ΔCP MS = changeComposite Myocardial Status Parameter, ΔCA FS = change in Composite Functional Status Assessment, ΔCA CVE = change in Composite serious CardioVascular Events risk Assessment, ΔUscore = change in universal score, p according to ANOVA.

Basic patient preoperative parameter analysis in different clusters showed that patients from the first cluster were somewhat older (63.7 years vs 58.5 years in cluster 2 and 54.1 years in cluster 3). Patients in cluster 1 also had a higher average number of implanted stents – 1.7 vs 1.2) than those in the other two clusters. The majority of patients in cluster 3 had chronic CAD (7 out of 11) in clusters 1 and 2 from acute myocardial infarction.

The changes in certain composite parameters in the clusters used by Repeated-measures ANOVA in the defined clusters are shown in Figures 1- 2.

As we can see, in both cases after the stenting composite parameter values in cluster 2 increased (positive changes), while in cluster 1 these values decreased (negative changes). The impact of stenting on the change in the composite parameters was assessed (for Uscore: ɳ²= 0.67, p =0.00001, for CA CVE partial ɳ²= 0.46, p =0.001). Note that for cluster 2, the decrease in Uscorewas statistically significant (the post-hocp-levels for the Bonferroni test p=0.00004), as well as a decrease in CA CVE (p=0.02).

The next step was to identify the specific ECG and HRV parameters that changed the most after stenting and thus contributed to the composite parameter dynamics. For this purpose, a feature selection procedure was used for features related to the corresponding composite parameters. The F-test was used for variable selection.

Table 3 shows the primary signs of ECG, identified as the best predictors for composite indicators in the entire dataset.

Table 3. Primary ECG parameters - the best predictors of a composite indicators

ECG and HRV parameters	F-value	P-value
T wave amplitude in lead II	30.817	0.000
T wave amplitude in lead AvR	21.883	0.000
QRS-T angle in frontal plane	8.622	0.0001
Heart rhythm abnormalities index	4.449	0.008
HF-QRS	3.429	0.021
alpha T angle in frontal plane	2.867	0.022
Psychoemotional state index	1.964	0.094

The diagrams below demonstrate the changes in several selected ECG and HRV parameters (Figures 3-5). It should be noted that changes in the primary indicators were less pronounced for some indicators in the clusters in which they were in different directions. The average T wave amplitude (lead II) decreased after stenting (according to T-test for dependent samples Δ T wave amplitude= 35.5µV, P= 0.039).The impact of stenting was estimated by ɳ²= 0,069, P =0.19. The average T-wave amplitude (lead AvR) increased after stenting in all clusters, mostly in cluster 1 (according Wilcoxon test Δ T wave am (lead AvR) = -50.2 µV, p= 0.07).

After stenting, the averageQRS-T angle in the frontal plane in cluster 1 increased (ΔQRS-T angle = - 34.8º) according to the Wilcoxon test (p=0.043), indicating negative changesin myocardial, while in other clusters it somewhat decreased in cluster 2 (ΔQRS-T angle = 24.4°, P=0.027).

The integral characteristic of the interval ST-T shape in lead II (mainly T wave amplitude) dynamics is shown in Figure 3.The effect of stenting on various changes in the clusters was significant (ɳ²= - 0.33, p=0.016), the changes ST-T shape in the clusters are multidirectional.

It would be interesting to observe psychoemotional state trends based on HRV. Prior to stenting psychoemotional state index in all subgroups was roughly the same, however after stenting itdecreased only in cluster 1 (decreased only in cluster 1 from 64.7 to 44.4 points). (Figure 4).

Prior to stenting, the subgroups significantly differed in heart rhythm abnormality levels; namely, there were more significant heart rhythm abnormalities in cluster 2, but after stenting, heart rhythm abnormalities in this group became less significant. At the same time, opposite changes were observed in group 1; the detected heart rhythm abnormalities became more significant, demonstrating negative dynamics (Figure 5).The impact of stenting on various changes in heart rhythm abnormalities in the clusters was significant(ɳ²= 0.34, p=0.015).

It is important to note that routine ECG interpretation systems, both more general, such as the Minnesota score and UCL algorithm, and more IHD-specific, such as the Selvester score, failed to detect significant changes in the studied subgroups after stenting.

Recent advancements in cardiac imaging and diagnostics have underscored the need for integrated approaches that combine various physiological signals [16]. The findings of this study underscore the incremental diagnostic value provided by the conjoint analysis of the ECG and HRV parameters. Our methodology builds on the premise that multimodal data synthesis can enhance the predictive accuracy for myocardial damage beyond that achievable through single-parameter analysis.

This study, as well as our previous studies, showed that the combination of ECG and HRV parameters had the best diagnostic value. HRV analysis results revealed a decreased psychoemotional state index in subgroup 1 (i.e., in the subgroup with intervention-induced myocardial damage). Changes in individual ECG and HRV parameters demonstrate only certain aspects of the examined phenomenon. Moreover, they can occur in opposite directions. Therefore, to reach a certain conclusion, in our case concerning the degree of subtle myocardial damage, a certain summarizing index that would synthesize the effects of individual components is necessary. The calculation method of such an index might be different; however, it must always include sequential steps as a theoretical justification of the composite index for a particular task, selection of data adequate to the problem at hand, analysis of this data (including its normalization) using methods of multivariate statistics, selection of informative private indicators (including the exclusion of correlated parameters), and finally, the actual construction of the composite index through aggregation of private indicators. As shown above, we performed all these steps. At the same time, it is important to consider that, in addition to the composite index, various partial indicators prove to be the most informative for detecting subtle changes in various clinical scenarios.

The construction of a composite index represents a significant advancement over traditional single-parameter measures. Its ability to encapsulate the multifaceted aspects of myocardial health offers a nuanced view that single metrics often fail to convey. This approach aligns with the growing consensus that cardiovascular health can cannot be fully represented by discrete, isolated clinical findings, but is better understood through the integrative analysis of multiple indicators.

Usually, these are modern electrocardiographic indices with a common pathophysiological basis. All of them are aimed at assessing the electrical homogeneity of the myocardium through different means, as the more heterogeneous the myocardium is from an electrical point of view (the higher the dispersion of the generated transmembrane action potentials in amplitude and length), the higher the likelihood of serious cardiovascular events. The identification of electrocardiographic indices as key indicators of myocardial electrical homogeneity has intriguing clinical implications. These indices, reflecting the underpinnings of myocardial pathology, could provide an early warning system for clinicians. This would allow for the prognostication of adverse events, potentially guiding timely therapeutic interventions.

The angle between the QRS complex and T-wave peaks is, in fact, an improved Wilson ventricular gradient, known since 1934. Recent large-scale studies have shown that this simple marker is a strong predictor of cardiovascular events and mortality in the general population.

The SVD of the T wave represents the complexity of ventricular repolarization. When repolarization is uniform, as in normal individuals, one major spatial component (eigenvector) can be identified. Conversely, when the repolarization pattern becomes fragmented, the relative value of the smaller vectors increases proportionally. Such an approach allows a comparison between the morphology of the T-wave across the 12 leads and quantification of T-wave abnormalities in an observer-independent manner [22]. Applying SVD to the T wave offers a mathematical and unbiased exploration of repolarization heterogeneity, a feature often obscured in routine clinical evaluations. This quantifiable measure holds promise for identifying individuals at heightened risk who may be asymptomatic using conventional assessments, facilitating an evolution toward precision medicine.

This is the first study dedicated to the assessment of minor changes in electrocardiograms using the original scaling method immediately after coronary stenting.

This study has certain limitations. First, the sample size was relatively small. Second, no comparison of trends in minor ECG changes with the levels of biomarkers of myocardial damage and inflammation was performed. Finally, analysis of the prognostic value of detected ECG changes regarding mortality, recurrent need for revascularization, and myocardial infarction rate during the first several years after the intervention has not yet been conducted.

Consequently, planned large-scale studies will not only aim to corroborate the current findings. However, they will also seek to evaluate the longitudinal impact of the detected ECG changes on clinical outcomes. Additionally, exploring the interplay between these electrocardiographic changes and other clinical parameters, such as patient-reported symptoms and exercise capacity, will further refine the utility of the composite index for post-stenting patient management.

In conclusion, the current study introduces and validates an innovative ECG and HRV scaling method, demonstrating its utility in capturing and interpreting minor changes in the electrocardiogram immediately after PCI with stent insertion. The advanced ECG parameters pivotal in this novel analytical approach have emerged as more informative and precise than traditional ECG interpretation systems, which need to be revised for the subtle nuances required in post-PCI assessments.

Our research identified a specific patient subgroup, subgroup 1, that exhibited myocardial damage attributable to the stenting process. This subgroup not only showed differential ECG changes, but was also distinguished by a higher average number of implanted stents, indicating a potentially more significant procedural impact on cardiac function. The delineation of this subgroup within the broader patient population indicates the potential for individualized monitoring and management strategies in the post-operative care of patients undergoing PCI.

Furthermore, the integration of HRV analysis provided additional depth to our findings, revealing a decrease in the psychoemotional state index within subgroup 1. This psychological dimension, in tandem with the physiological data obtained from ECG analysis, provides a complete picture of the post-stenting condition, affirming the multifaceted nature of the cardiac response to stent placement.

These insights underscore the complexity of myocardial injury and patient recovery following PCI, and highlight the necessity for a more nuanced and integrated approach to cardiac monitoring. As such, the implications of this study extend beyond immediate clinical assessment to influence the long-term management of patients post-PCI. Future research, particularly with larger patient cohorts and biomarker trends, will be instrumental in reinforcing these findings and translating them into practice, with the ultimate aim of enhancing patient outcomes following coronary interventions.

The scientific novelty of this research lies in the development and application of a sophisticated ECG and HRV scaling method designed to detect subtle myocardial changes immediately after PCI with stent placement. This innovative approach transcends the capabilities of conventional ECG analysis by employing modern electrocardiographic indices that provide a more granular and insightful evaluation of myocardial electrical homogeneity. The method's pioneering integration of HRV parameters further distinguishes it from existing practices, augmenting its diagnostic value with psychoemotional assessments. By synthesizing these multifaceted data points, this study provides a nuanced understanding of myocardial injury and patient response to stenting, thus contributing a novel perspective to the corpus of cardiovascular electrophysiology.

From a practical standpoint, the application of this novel method in clinical settings has the potential to revolutionize post-PCI patient monitoring and follow-up care. By enabling the identification of patients with stent-induced myocardial damage, clinicians can tailor their therapeutic approaches, potentially improving individual patient outcomes and reducing the incidence of postprocedural complications. The sensitivity of this method to psychoemotional state fluctuations also opens the door for more comprehensive post-operative care strategies that address both the physical and psychological well-being of patients. This dual focus on physiological and psychological health post-PCI represents a significant step toward holistic patient care in interventional cardiology, offering a promising template for future clinical protocols.

Acknowledgments: The authors are sincerely grateful to Mrs. Anna Starynska (CardiolyseOy, Finland) for long-term support in relation to data management.

Author Contributions statement: Conceptualization, ChaikovskyІ., Dziuba D., Todurov B. and Loskutov О.; methodology, Dziuba D., ChaikovskyІ.; software, Kryvova .; validation, Dziuba D., Loskutov О.; formal analysis, ChaikovskyІ.,Kryvova O., Chumachenko D.; investigation, Dziuba D., Loskutov O.; resources, Todurov D.,.; data curation, DziubaD. and Loskutov О.; writing—original draft preparation, ChaikovskyІ.,Dziuba D., Kryvova O.; writing—review and editing, ChaikovskyІ., Dziuba D.and Chumachenko D.; visualization, KryvovaO.; supervision, Loskutov О.; project administration, ChaikovskyІ., Dziuba D.; All authors have read and agreed to the published version of the manuscript.

Ethics Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Shupik National Healthcare University of Ukraine, Kyiv, (protocol code №5, date of approval 05/04/2021).

Informed Consent Statement: Written informed consent was obtained from all participants involved in the study.

Conflicts of Interest: The authors declare no conflicts of interest.

Data availability statement: All datasets generated for this study are included in thearticle/supplementary material.The data presented in this study are available upon request from the corresponding authors. The data were not publicly available because of privacy concerns.

Funding: This work was performed in accordance with the main areas of research interest of the Department of Anesthesiology and Intensive Care of the Shupik National Healthcare University of Ukraine under a grant from the Ministry of Health of Ukraine (Grant Number 0121U114715).

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No competing interests reported.

Detection of the Mild Myocardial Injury during Percutaneous Coronary Intervention Using Evaluation of Minor Changes on Electrocardiogram and Heart Rate Variability

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