Psychological consistency network characteristics and influencing factors in patients after percutaneous coronary intervention treatment

Abstract

BACKGROUND

A psychological sense of coherence (SOC) in percutaneous coronary intervention (PCI) patients is important for disease prognosis, and there is considerable variation between their symptoms. In contrast, network analysis provides a new approach to gaining insight into the complex nature of symptoms and symptom clusters and identifying core symptoms.

AIM

To explore the psychological coherence of symptoms experienced by PCI patients, we aim to analyze differences in their associated factors and employ network analysis to characterize the symptom networks.

METHODS

A total of 472 patients who underwent PCI were selected for a cross-sectional study. The objective was to investigate the association between general patient demographics, medical coping styles, perceived stress status, and symptoms of psychological coherence. Data analysis was conducted using a linear regression model and a network model to visualize psychological coherence and calculate a centrality index.

RESULTS

Post-PCI patients exhibited low levels of psychological coherence, which correlated with factors such as education, income, age, place of residence, adherence to medical examinations, perceived stress, and medical coping style. Network analysis revealed that symptoms within the sense of psychological coherence were strongly interconnected, particularly with SOC2 and SOC8, demonstrating the strongest correlations. Among these, SOC10 emerged as the symptom with the highest intensity, centrality, and proximity, identifying it as the most central symptom.

CONCLUSION

The network model has strong explanatory power in describing the psychological consistency symptoms of patients after PCI, identifying the central SOC symptoms, among which SOC10 is the key to overall SOC enhancement, and there is a strong positive correlation between SOC2 and SOC8, emphasizing the need to consider the synergistic effect of symptoms in intervention measures.

Key Words: Percutaneous coronary intervention; Symptom network; Core symptoms; Sense of psychological coherence; Influencing factors

Core Tip: This study employs network analysis to investigate the core symptoms of sense of coherence (SOC) in post-percutaneous coronary intervention patients, assessing coping styles and perceived stress. It reveals that low SOC is linked to medical coping and stress. Network analysis identified central SOC symptoms, with SOC10 being pivotal for overall SOC enhancement. The study also notes a strong positive correlation between SOC2 and SOC8, emphasizing the need to consider symptom synergy in interventions.

INTRODUCTION

Percutaneous coronary intervention (PCI) has rapidly progressed and emerged as a pivotal approach and standard of care for myocardial revascularization in clinical practice[1]. Substantial evidence indicates that PCI not only enhances the survival rates of patients with acute coronary syndrome but also alleviates physical symptoms in those with chronic myocardial ischemia[2-4]. However, there is an imperative need to focus on psychological rehabilitation post-PCI, alongside interventional therapies[5,6]. Reports indicate that a significant number of patients undergoing PCI experience adverse emotional responses, including anxiety, depression, and other negative reactions, which are independent of surgical quality[7]. These individuals often face dual psychological stressors stemming from both myocardial ischemia symptoms and the PCI procedure itself. Such adverse psychological responses can directly impede postoperative cardiac rehabilitation. Consequently, these negative emotions are recognized as risk factors predicting poor prognoses in cardiac rehabilitation following PCI[5]. Studies demonstrate that up to 63.8% of patients with coronary artery disease exhibit negative mood symptoms, with anxiety symptoms reported in up to 39.6% of patients post-PCI[8]. This highlights that negative mood states are prevalent among patients undergoing PCI and necessitate attention during the post-procedural period.

In recent years, the advancement of the biopsychosocial model and positive psychology has led an increasing number of researchers to explore methods for leveraging patients’ positive emotions to bolster their confidence and ability to overcome illness[9,10]. The psychological sense of coherence (SOC), a core component of positive psychology and health ontology, has garnered significant attention and research interest for its role in maintaining the physical and mental health of individuals with chronic illnesses[11,12]. As demonstrated, SOC is an important predictor of health-related behaviors among patients with coronary heart disease, and by improving health behaviors, it can enhance the quality of life and outcomes for these patients[13,14]. Consequently, regulating and controlling the level of SOC can help prevent and reduce the incidence of adverse moods, thereby maintaining normal psychological conditions. Despite this, current research on managing SOC in patients who have undergone PCI has primarily focused on symptom summaries using a single scale. This approach has failed to comprehensively analyze the mechanisms by which symptoms are associated with other relevant factors[15].

Moreover, network analysis grounded in proteomics, rehabilitation omics, and symptomics offers a vital framework for elucidating the mechanisms underlying symptom-level interconnections[16-18]. An omics-based symptom network can streamline symptom sets and guide healthcare providers and researchers in devising precise, personalized interventions[18,19]. A prior study evaluated night shifts, insomnia, anxiety, and depression among Chinese nurses during the coronavirus disease 2019 pandemic remission period. It suggested that employing network analysis to identify key symptoms could enable early interventions, thereby alleviating symptoms and further preventing disease exacerbation[20]. Brinkhof et al[21] conducted a network analysis study on the interactions between quality of life and resilience factors, identified key resilience factors, and found that the core factors had a contributory effect on quality of life. Using positive psychology as an entry point, this study explores the core symptoms of SOC through network analysis of patients’ SOC and centrality, evaluates coping styles and perceived stress, and then analyses the links between the two to provide a scientific theoretical basis for medical staff to promote patient psychological rehabilitation models.

MATERIALS AND METHODS

Participants

This was a cross-sectional study with a final inclusion of 472 patients. Patients undergoing PCI in a tertiary care Hospital of Guangxi Medical University from September 2022 to May 2023 were selected as the study participants. Inclusion criteria were set as follows: (1) Age ≥ 18 years; (2) Received PCI procedure; and (3) Voluntary participation with informed consent. The exclusion criteria were as follows: (1) Patients with severe medical conditions who could not cooperate with the investigation; and (2) Patients with previous psychiatric or language impairment. This study was consistent with the principles of volunteering, and all participants signed informed consent and volunteered to participate in this study.

Measures

Questionnaire on the general status of PCI patients: Sociodemographic data included race, sex, age, occupational status, marital status, education level, per capita monthly household income, residential status, surgical history, duration of illness, and hospitalization days.

SOC scale-13: The SOC scale-13 (SOC-13) consists of 13 items and is divided into 3 dimensions: “sense of comprehensibility”, “sense of control” and “sense of meaning”. The total score for each item is evaluated based on a 7-point Likert scale, wherein items 1, 2, 3, 8, and 13 are reverse-scored[22]. The total score ranges from 13 to 91, with 13-63 being low, 64-79 being medium and 80-91 being high, wherein higher scores indicate higher levels of mental coherence.

Medical coping modes questionnaire: The medical coping modes questionnaire (MCMQ) has 20 items and is divided into 3 dimensions: Confrontation, avoidance, and submission[23]. The confrontation dimension contains items 1, 2, 5, 10, 12, 15, 16, and 19; the avoidance dimension includes items 3, 7, 8, 9, and 11; and the submission dimension involves items 4, 6, 13, 18, and 20. Each item is divided into 4 levels, with scores ranging from 1 to 4. Each subscale is scored separately, with high scores indicating a patient’s preference for this particular coping style.

Chinese version perceived stress scale: The Chinese version perceived stress scale (CPSS) consists of 14 items and is divided into two dimensions: “tension” and “loss of control”[24]. Each item is scored based on a five-point scale with scores ranging from 0 to 4, of which items 4, 5, 6, 7, 9, 10, and 13 are reverse scored. The total score ranged from 0 to 56, with a total score between 0 and 28 being a normal level of stress perception, between 29 and 42 being an intermediate level and between 43 and 56 being a high level. The higher the total score, the more stress the individual perceives.

Statistical analysis

All statistical analyses were performed using R version 4.2.3. Frequencies, percentages, means and standard deviations were used to describe the demographic characteristics and severity of symptoms. We constructed contemporaneous networks for the 13 symptoms of SOC. In this study, R4.2.3 software was used to build a Gaussian model network (Gaussian graphical models), and estimate network and qgraph packages were used to fit and visualize the data, resulting in the Gaussian network model map and partial correlation network (regularized partial correlation network) results. In the network model diagram, the connections between nodes (i.e. edges) represents the strength of association between them. Blue represents positive correlation, while red represents negative correlation; the shorter the connection between nodes, the darker the stronger the correlation[25]. This study uses Centrality and qgraph packages to calculate centrality metrics in the network model. Among these indicators, strength centrality is considered a meaningful and fairly robust measurement method. The strength centrality of a node is defined by the sum of the absolute weights of all edges it is connected to. However, if the network contains edges with positive and negative weights, the strength centrality of a node may not accurately reflect its relative importance within the network[26]. In order to evaluate the accuracy and robustness of the network, this study used the bootnet package in R4.2.3 software. Firstly, by performing the non-parametric self-help method 2000 times, calculate the 95% confidence interval (CI) of the edge weights to verify their credibility. Subsequently, the sample reduction self-help method was used to obtain correlation stability (CS) indicators, in order to evaluate the stability of node centrality. The CS coefficient (CS coefficient = 0.7) indicates a 95% confidence level that the correlation between the centrality index calculated on the new dataset and the centrality index on the original dataset is at least 0.7. Usually, a CS coefficient exceeding 0.5 is considered a sign of good stability.

RESULTS

Comparison of participant characteristics and SOC-13 scores of patients after PCI

A total of 472 patients were involved in this study. According to the SOC-13 score, it was found that the absence of previous surgery, the lower the number of comorbidities, the lower the number of days of hospitalization, the shorter the duration of illness, and the lower the number of hospitalizations in the last 5 years, the higher the level of psychological congruence among the patients. The rest of the general demographic information is shown in Table 1.

Table 1 Comparison of sense of coherence scale-13 scores of patients after percutaneous coronary intervention with different demographic characteristics (n = 472), n (%).

Variables	Classification	Frequency	SOC-13 score, mean ± SD	t/F	P value
Age		63.28 ± 11.31	-	0.269	< 0.001
Gender	Male	324 (68.6)	54.56 ± 14.31	-2.420	0.16
	Female	148 (31.4)	58.04 ± 14.58
Residence	Rural	167 (35.4)	59.01 ± 14.32	3.754	< 0.001
	Urban	305 (64.6)	53.82 ± 14.31
Cigarette smoking	No	245 (51.9)	57.04 ± 14.50	2.865	0.58
	Give up smoking	86 (18.2)	55.45 ± 14.61
	Yes	141 (29.9)	53.38 ± 14.41
Drinking wine	No	282 (59.7)	56.43 ± 14.51	0.985	0.374
	Give up drinking	77 (16.3)	54.60 ± 15.29
	Yes	113 (23.9)	54.45 ± 14.14
Marriage	Unmarried	6 (1.3)	47.00 ± 15.74	0.916	0.433
	Married	443 (93.9)	55.77 ± 14.64
	Divorced	14 (3)	53.86 ± 17.24
	Others	9 (1.9)	58.67 ± 14.03
Education	Primary school education	196 (41.5)	51.27 ± 13.92	10.473	< 0.001
	Junior high school	182 (38.6)	58.39 ± 14.87
	High school education	74 (15.7)	59.45 ± 17.76
	College degree	13 (2.8)	66.46 ± 11.13
	Bachelor’s degree and above	7 (1.5)	47.29 ± 8.98
Monthly personal income (dollar)	< 1000	200 (42.4)	59.30 ± 14.67	12.5	< 0.001
	1000-3000	195 (41.3)	50.63 ± 13.203
	3000-6000	50 (10.6)	56.24 ± 14.69
	6000-8000	19 (4)	62.16 ± 10.92
	> 8000	8 (1.7)	68.13 ± 10.66
Occupation	Farmer	190 (40.3)	58.90 ± 14.43	4.194	0.001
	Laborer	39 (8.3)	52.82 ± 14.20
	Employee	57 (12.1)	53.93 ± 13.69
	Administrator	19 (4)	47.05 ± 13.11
	Individual	14 (3)	56.21 ± 16.02
	Others	153 (32.4)	54.03 ± 14.33
Keep up with the medical checkups	Yes	103 (21.8)	50.23 ± 12.68	-4.358	< 0.001
	No	369 (78.2)	57.17 ± 14.69
Previous surgery or not	Yes	89 (18.9)	54.24 ± 14.14	-1.021	0.308
	No	383 (81.1)	55.98 ± 14.64
Caregiver	Spouse	257 (54.4)	53.92 ± 14.41	5.242	< 0.001
	Child	173 (36.7)	59.31 ± 14.35
	Caregiver	33 (7)	49.79 ± 12.27
	Friends	2 (0.4)	51.50 ± 23.36
	Others	7 (1.5)	57.71 ± 5.939
Number of comorbidities		3.67 ± 1.58	-	-0.310	< 0.001
Duration of illness		4.17 ± 4.52	-	-0.186	< 0.001
Days of hospitalization		6.18 ± 2.39	-	-0.182	< 0.001
Hospitalization in the last five years		1.95 ± 0.83	-	-0.178	< 0.001

SOC: Sense of coherence.

Correlation of SOC-13 scores with CPSS and MCMQ scores in patients after PCI

The CPSS score of patients after PCI was 40.12 ± 4.26, with a tension dimension 20.71 ± 3.92 and a loss of control dimension 19.41 ± 4.16. The MCMQ score of PCI patients was 49.77 ± 5.88, with confrontation 20.46 ± 1.20, avoidance 16.79 ± 3.14 and submission 12.51 ± 2.35. The SOC-13 total score and each dimension score showed a correlation with the CPSS score and the MCMQ score (all P < 0.05), and detailed information is shown in Table 2.

Table 2 Correlation of sense of coherence scale-13 with Chinese version perceived stress scale and medical coping modes questionnaire in patients after percutaneous coronary intervention (n = 472).

Variable	mean ± SD	SOC	Sense of comprehensibility	Sense of control	Sense of significance	CPSS	Sense of tension	Loss of control	Medical coping modes questionnaire	Confront	Avoidance	Submission
SOC	55.65 ± 14.55	1
Sense of comprehensibility	20.72 ± 5.83	0.979b	1
Sense of control	18.09 ± 4.03	0.967b	0.920b	1
Sense of significance	16.85 ± 5.06	0.976b	0.930b	0.923b	1
CPSS	40.12 ± 4.26	-0.281b	-0.301b	-0.240b	-0.270b	1
Sense of tension	20.71 ± 3.92	-0.785b	-0.810b	-0.726b	-0.745b	0.485b	1
Loss of control	19.41 ± 4.16	0.452b	0.455b	0.438b	0.425b	0.567b	-0.445b	1
Medical coping modes questionnaire	49.77 ± 5.82	-0.435b	-0.435b	-0.400b	-0.429b	0.014	0.391b	-0.354b	1
Confront	20.46 ± 1.20	-0.118a	-0.131b	-0.099a	-0.109a	0.027	0.122b	-0.087	0.604b	1
Avoidance	16.79 ± 3.14	-0.464b	-0.459b	-0.433b	-0.460b	0.016	0.414b	-0.373b	0.950b	0.465b	1
Submission	12.51 ± 2.35	-0.397b	-0.399b	-0.363b	-0.393b	0.000	0.355b	-0.333b	0.900b	0.365b	0.781a	1

^aP < 0.05 (2-tailed).

^bP < 0.01 (2-tailed).

SOC: Sense of coherence; CPSS: Chinese version perceived stress scale.

Multifactor analysis of SOC-13 scores in post-PCI patients

Analysis of the multifactorial results showed that age, residence, education, adherence to physical examination, each CPSS dimension, and each MCMQ dimension were the main influencing factors on the SOC-13 scores of patients after PCI. This multifactor analysis predicted 66.3% of the variance in SOC-13 scores, as shown in Table 3.

Table 3 Multifactorial analysis of sense of coherence scale-13 scores in patients after percutaneous coronary intervention.

Model	Classification	Standardized coefficient	t	Significance
Residence	-	Beta	-	-	VIF
	Constant	-	21.559	< 0.001	-
	Age	0.081	2.851	0.005	1.128
	Urban	0.116	2.489	0.013	1.384
Education	High school education	0.332	7.315	< 0.001	1.317
	College degree	0.322	6.678	< 0.001	1.492
	Bachelor’s degree and above	0.215	3.728	< 0.001	2.133
	Junior high school	0.085	1.94	0.053	1.239
Keep up with the medical checkups	Yes	0.146	3.346	0.001	1.215
CPSS	Sense of tension	-0.614	-18.218	< 0.001	1.586
	Loss of control	0.105	3.352	0.001	1.381
MCMQ	Confront	0.052	1.685	0.093	1.335
	Avoidance	-0.175	-3.698	< 0.001	3.135
	Submission	0.006	0.137	0.891	2.638
	Duration of illness	-0.059	-2.123	0.034	1.093

Dependent variable: Total consistency score, adjusted R2 = 0.663, F = 9.936, P < 0.001. VIF: Variance inflation factor; CPSS: Chinese version perceived stress scale; MCMQ: Medical coping modes questionnaire.

Network analysis of SOC-13 symptoms in patients after PCI

Network structure diagram of SOC-13 symptoms in patients after PCI: The symptom network of SOC-13 in patients after PCI is shown in Figure 1A. The marginal thickness of the symptom network showed that the strongest correlation (r = 0.59) was found between SOC2 (the extent to which patients do things that surprise you in response to people they know very well) and SOC8 (the extent to which they have feelings of failure). There was also a strong correlation between SOC6 (the degree of feeling out of control) and SOC10 (the degree of often having mixed feelings) (r = 0.38).

Figure 1 Network structure diagram of sense of coherence scale-13 symptoms in patients after percutaneous coronary intervention. A: Symptoms network of sense of coherence scale-13 (SOC-13) in patients after percutaneous coronary intervention (PCI). The edge between the two connected nodes represented their association strength. Wherein, the blue indicated positive association, red showed negative correlation. And the shorter and darker the edges were, the stronger their association were; B: Centrality analysis of SOC-13 symptoms in patients after PCI. Closeness is the inverse of the sum of the shortest path distances from all other nodes to that node, betweenness is the frequency of a node being on the shortest paths of any two other nodes, and strength is the sum of the weighted values of all the connecting lines of a node. The higher the score of point degree centrality of a symptom, the more central the symptom is in the network, i.e., it is the core symptom in the network; C: Stability coefficients for the expected impact of symptoms. The stability coefficient for the expected impact of symptoms was > 50, representing the model was stable. SOC: Sense of coherence.

As described in the centrality of SOC symptoms (Figure 1B), SOC10 (rs = 1.54) was the primary “strength” symptom, indicating that it was the most highly influential and the most important symptom in the symptom network. SOC10 (rb = 0.0126) was also the most important “closeness” symptom, located in the center of the symptom network and relating most closely to the other symptoms. Moreover, SOC10 (rc = 16) and SOC6 (rc = 11) were the two most prominent symptoms in the “betweenness”, showing that they had the greatest influence on the symptom interaction and were important bridge symptoms in the symptom network. SOC9 (re = 1.23) was the most priority symptom in the “Expected influence” symptoms, suggesting that it was most likely to influence the symptom network as a whole. Finally, after data validation, the above model of SOC-13 symptoms had good network stability with a CS coefficient of 0.75 (Figure 1C).

Marginal weighting analysis of SOC-13 symptoms in post-PCI patients: As shown in the SOC edge weight calculation (Figure 2A), the gray area indicates the 95%CI area of the edge weights using the bootstrap method. The small 95%CI (gray intervals) of edge weights in our study revealed that the SOC-13 symptom model was accurate by analyzing the edge weights through the network. In addition, the results demonstrated that the edges (Figure 1A) were stably estimated by this marginal weighting analysis, suggesting that the SOC-13 symptom model had good network accuracy. The bootstrap difference test of edge weights (Figure 2B) showed that the black squares indicated edges that were significantly different from each other, while gray squares indicated edges that were not significantly different from each other. with SOC2-SOC8 being significantly different.

Figure 2 Marginal weighting analysis of sense of coherence scale-13 symptoms in post-percutaneous coronary intervention patients. A: Marginal weighting analysis of sense of coherence scale-13 symptoms in post-percutaneous coronary intervention patients. Edge-weights were ordered from strong to weak, top to bottom. Grey area indicated the bootstrapped confidence intervals for each edge; B: Marginal difference test results for sense of coherence scale-13 symptoms in patients after percutaneous coronary intervention. Black boxes indicate significant difference, while gray boxes indicate non-significant difference between edges located on the corresponding X and Y-axis. The color of diagonal boxes indicated the strength and direction of its corresponding edge. Blue indicates positive and red indicates negative edge. Darker color indicates stronger edge. More numbers of black boxes with regard to an edge indicate that it is significantly stronger than most other edges. SOC: Sense of coherence.

DISCUSSION

In this study, our objective was to investigate the current status of SOC symptoms in post-PCI patients and to analyze the factors influencing these symptoms, with the aim of identifying central SOC symptoms through network analysis. The findings revealed that the patients exhibited low levels of SOC. Specifically, higher numbers of hospitalizations and longer disease duration were associated with lower SOC levels. Additionally, post-PCI patients reported higher levels of stress perception, which correlated negatively with SOC levels. Furthermore, these patients frequently adopted avoidance and submissive coping strategies, accompanied by negative attitudes. Network analysis results indicated a relatively strong correlation between SOC2 and SOC8. Moreover, SOC9 had the highest expected impact, while SOC10 demonstrated the highest closeness, betweenness centrality, and strength centrality. These findings have multiple implications for understanding the causal model of SOC and its treatment.

The overall SOC score in the study was moderately low. Among the three dimensions of SOC, the highest mean score was for a sense of comprehensibility, while the lowest was for a sense of meaning. Notably, patients with lower SOC levels often lose confidence after PCI surgery due to psychological fears related to stent implantation, long-term postoperative medication, and high recurrence possibilities[27,28]. Comprehensibility, as an integral part of SOC, is closely linked to the perception of internal and external stimuli and plays a crucial role in predicting individual mental health[29]. In our study, the comprehensibility score was relatively high, indicating that these patients had a better understanding of their disease state. Additionally, the findings of this study indicated that higher numbers of hospitalizations, longer hospital stays, and longer illness durations were associated with lower levels of SOC[30,31]. This relationship can potentially be explained by the fact that repeated hospitalizations signify a deterioration in the patient’s condition. As the number of hospitalizations increases, along with longer durations of hospital stays and illness, patients may start losing confidence in their ability to effectively manage and recover from their illness. Hence, based on our study, nursing staff should focus on interventions to enhance PCI patients’ perception and understanding, stimulating them to utilize internal and external resources to cope with their illness. Considering the dynamic and flexible characteristics of SOC, medical professionals can strengthen patients’ SOC through interventions such as knowledge education, resource utilization, and perception of meaning[32]. With a better ability to assess their physiological and psychological conditions when facing diseases and perceive available resources more comprehensively, patients after PCI can effectively deal with stress and ultimately improve their quality of life.

This study revealed that post-PCI patients experienced heightened levels of stress perception, which displayed a negative correlation with their SOC. Moreover, our study highlighted that elevating the SOC level of post-PCI patients under stressful situations could mitigate negative emotions. Individuals with a high level of SOC had better adaptability to stress and maintained their physical and mental well-being following PCI surgery[33,34]. Consequently, boosting SOC contributes to reducing patients’ perceived stress levels and promoting physical and mental health. Furthermore, the study revealed that post-PCI patients exhibited higher scores in avoidance and submission coping styles than in norm coping styles, while their scores in confrontation coping styles were significantly lower. This suggests that patients tend to favor avoidance and submission strategies when facing the challenges of acute cardiac surgery and its potential complications[35]. With advancements in modern medicine and the continuous enhancement of nursing services, healthcare professionals should assess patients’ psychological well-being and physical health when delivering nursing care. It is of utmost importance to provide psychological guidance on disease management during health education sessions[36]. Therefore, clinical nursing staff should concentrate on implementing strategies to support post-PCI patients in coping with their conditions, assisting them in approaching their health issues rationally and positively. This, in turn, will facilitate more effective treatment and recovery for these patients.

Synchronous symptom networks serve as a valuable tool to identify the most significant symptoms within the network structure. The synergistic correlation of symptoms in symptom aggregation can be used for more effective and scientifically sound symptom management in the clinic[28]. Our study found that SOC2 and SOC8 were strongly associated with psychological consistency. Implementing targeted interventions on SOC2 and SOC8 could decrease the occurrence of undesirable emotions in post-PCI patients. Therefore, medical professionals should enhance their care and communication with post-PCI patients, aiming to foster their understanding and acceptance of their condition and surgical procedure. Improving these patients’ confidence in coping with the stress of their illness will increase their willingness to confront the diseases and surgery[37]. Additionally, SOC9 (re = 1.23) had the highest expected impact, which served as a reliable predictor of changes in the severity of psychologically consistent symptoms over time. Therefore, in patients’ daily management, healthcare professionals can utilize the characteristics of SOC9 to forecast changes in SOC levels. It enables healthcare professionals to implement targeted interventions at the opportune moment for patient benefit.

Network centrality serves as a valuable tool for researchers to identify potentially crucial symptoms from a mechanistic perspective[16]. The network centrality indicators in our study revealed that the symptom with the highest intensity, centrality, and proximity was SOC10 (rs = 1.54), which was the most influential symptom in the network regarding the overall SOC. Targeting interventions on this SOC10 symptom can effectively enhance the patient’s overall SOC. Psychological research suggests that the feeling of loss of control is closely associated with specific experiences[38]. In clinical practice, it is crucial to pay greater attention to patients’ feelings of loss of control and proactively seek to identify, implement, and tailor management strategies early to improve somatic symptoms and treatment outcomes[39]. Studies have demonstrated that cognitive behavioral therapy is particularly effective in reducing stress and anxiety among post-PCI patients. Therefore, healthcare professionals can routinely employ interventions based on cognitive behavioral therapy to target patients’ emotions and reduce adverse emotional experiences[40,41].

CONCLUSION

The level of SOC among post-PCI patients is generally low and is associated with medical coping styles and perceived stress. To gain a comprehensive understanding of SOC symptoms, we conducted a network analysis, leading to the identification and construction of SOC symptoms specific to the post-PCI population. This approach offers complementary insights beyond traditional classification and dimensional models. Notably, SOC10 emerged as a central component within the symptom structure, signifying its significance in the overall improvement of SOC levels. Focusing on these core symptoms may prove beneficial in enhancing SOC among patients. Furthermore, the study highlights the close relationship between SOC2 and SOC8, demonstrating the strongest positive correlation. This underscores the importance of considering the facilitation of symptom synergy in subsequent interventions and follow-ups. The sample size of this study is relatively small, and the inclusion and selection of the sample from a tertiary hospital may limit its representativeness. The sample lacks hypertensive patients from primary hospitals and communities, which limits its representativeness and extrapolation. This study is a cross-sectional study design, which only infers the factors related to the symptoms of hypertension and insomnia, and cannot yet explain the causal relationship between the influencing factors in the research conclusions and insomnia symptoms. Based on network analysis methods, the conclusions drawn in this study have enlightening significance for the prevention and intervention of insomnia symptoms in hypertensive patients, but there is still a lack of empirical intervention research to test them. Such studies would be instrumental in uncovering the core and bridging roles of key symptoms, as well as identifying the underlying mechanisms of symptom clustering. This approach provides a solid foundation for accurate and effective symptom management strategies.