A Network Analysis of the Associations Between Posttraumatic Stress Symptoms and Posttraumatic Growth Among Lung Cancer Patient

doi:10.21203/rs.3.rs-4438948/v1

Download PDF

Research Article

A Network Analysis of the Associations Between Posttraumatic Stress Symptoms and Posttraumatic Growth Among Lung Cancer Patient

https://doi.org/10.21203/rs.3.rs-4438948/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Lung cancer patients who experience a traumatic event may experience two types of psychological changes, positive posttraumatic growth (PTG) or negative posttraumatic stress disorder (PTSD), but the two types of psychological changes can also coexist.

Methods: This study used network analysis to examine the interaction between PTSD and PTG in 350 patients.

Results: The nodes with strong connections were from "Strong Negative Beliefs" to" Feeling Cut of from Others", " Strong Physical Reactions” to” Trouble with Positive Feelings", " More willing to express emotions" to " Ability to handle difficulties" and " Nightmares" to " Strong Physical Reactions". Network centrality showed the highest EI scores for " Nightmares", " Intrusive Memories", " Excessive Blame of Selfor Others", and " Concentration Dificulities". The bridging analysis showed that " lrritability", " Taking Risks", " Trouble with Positive Feelings" and " Accepting the workaround", "Interpersonal closeness", " Greater feeling of self-reliance", " Appreciating each day", and " Changed priorities" were the bridging points connecting the coexistence network, indicating the network coexistence of PTSD and PTG.

Limitations: The data used in this study were cross-sectional, and the temporal nature and causal inferences of associations with symptoms cannot be drawn.

Conclusions: PTSD and PTG are co-existing and non-independent post-traumatic psychological responses in patients with lung cancer.

PTSD

PTG

Network Analysis

Lung Cancer

Lung cancer is the most common malignant tumor, with the second highest global incidence (11.4%) and the highest global mortality (18%) (1, 2). The incidence and mortality rates of lung cancer are expected to continue to rise significantly in the 2lst century. The incidence of lung cancer is associated with poor air quality, living and working environments(3–5). Lung cancer is not easy to detect early. Lung cancer is not easily detected at an early stage, and most lung cancer patients are found to have severe metastasis at the time of diagnosis, with a high mortality rate and poor prognosis compared with other cancers in terms of cure rate. Lung cancer patients from diagnosis to treatment process, continuous treatment and side effects damage physical and psychological functions, and there are also uncertainties such as metastasis and recurrence after treatment (6). According to the survey, chemotherapy and surgery are the most important treatment modalities for lung cancer patients, but radiotherapy has a long course of treatment, high toxicity and side effects, high cost, and the surgical site is prone to wound infection, which increases the incidence of complications such as incisional hernia, sepsis(7). After experiencing the impact of a series of major stressful events such as cancer diagnosis, surgery and treatment, patients are prone to induce negative emotions such as depression and anxiety, and even negative psychology such as symptoms of post-traumatic stress disorder. In the process of research and clinical intervention on patients’ psychological distress, researchers found that patients would simultaneously experience post-traumatic negative psychological responses such as PTSD and positive psychological responses such as post-traumatic growth during the process of diagnosis and treatment. PTSD and PTG coexist in populations experiencing trauma, and existing theoretical and empirical studies have found that post-traumatic stress disorder (PTSD) and PTG disorders are highly correlated and coexist in cancer patients(5) (8), thus it is necessary to include them together in this study and further explore their interrelationships.

National research on PTSD and post-traumatic growth in cancer patients targets the entire cancer patient population(9–11), with the most studies using a single cancer as abreast cancer patient(12, 13) and also on head and neck cancer(8, 14) and gastric cancer(15) and others, but there are few studies on lung cancer, and most prior studies of the relationship between PTSD and PTG used structural equation modeling and relied on a linear association from one structure to another. The network analysis used in this study is an emerging data-driven, data-independent, a priori assumption-independent visual analytic approach that effectively identifies the interactions between symptoms in a network(16), identifying "bridge variables" that play a key role in connecting different symptoms(17, 18).. Through network analysis, we can explore the characteristics of the PTSD-PTG network and its mechanisms in lung cancer patients, and identify the "bridge variables", so that we can find the key targets for lung cancer patients to reduce PTSD symptoms, enhance post-traumatic growth, and find the key targets for precise clinical interventions, and we can also provide a new perspective for the promotion of mental and physical health research, so that we can help the healthcare professionals to target the symptoms of PTSD in lung cancer patients, It also provides a new perspective for the promotion of psychosomatic health research, so as to facilitate the publicity, education and treatment of psychosomatic disorders for lung cancer patients by medical personnel.

2.1. Participants and procedures

The subjects of this study were selected from patients in the Department of thoracic surgery and Oncology of a third-grade hospital in Jinzhou City from February 2023 to December 2023. Inclusion criteria:①Meet the clinical diagnostic criteria of cancer;② Age > 18 years old; ③Stable condition, clear mind, normal thinking, normal language communication, and certain cognitive ability, ④Patients voluntarily participated in this investigation and signed the informed consent. Patient exclusion criteria: ①mental illness, cognitive impairment, infectious disease patients, ② within the past year have experienced accidents, bereavement, major diseases, Data collection was conducted by questionnaire survey method. After obtaining informed consent from patients and their families, the researcher distributed the questionnaires, checked for gaps and retrieved the questionnaires on the spot. The study was approved by the Ethics Committee of Jinzhou Medical University, and the researchers were uniformly trained; all questionnaires were collected on the spot by the researcher himself, and the average completion time of the questionnaires was 20 min. A total of 384questionnaires were distributed in the study, and 350 valid questionnaires were recovered, with an effective recovery rate of 90%.

2.2. Survey tools

Patient's general information questionnaire: age, gender, occupation, place of residence, religion, education, marital status, per capita monthly family income, mode of payment of medical expenses, smoking status, duration of illness, family history of tumor.

In this study, the severity of patients’ PTSD symptoms was assessed using the Chinese 5th edition of the Posttraumatic Stress Disorder Screening Scale, The scale was revised by Zhou(19)et al. to assess PTSD symptoms in cancer patients and their family companions. The scale has four dimensions: the intrusive symptom cluster, the negative cognitive and mood alteration symptom cluster, the avoidance symptom cluster, and the hypervigilance symptom cluster, Each entry is scored on a 5-point scale ranging from 0 (never) to 4 (always)The scale has been shown to have good reliability in national studies. The retest correlation coefficient in this study was 0.87.

Chinese version of the Post-Traumatic Growth Scale. in this study, the revised version of the Wang Inter-Chinese Version (20) was chosen to assess the positive changes of individuals in the process of fighting traumatic events. The scale consists of five dimensions: life sense. personal strength, new possibilities, relationship with others, and self-transformation, with a total of 20 entries and a full score of 100 points, the higher the total score, the better the patient's PTG level. The total score located at 0–35,36–70, and 71–100 represents three levels of growth: low, medium, and high. The retest correlation coefficient in this study was0.85.

2.3. Data analysis

This study used SPSS for descriptive statistics and R software for network analysis. Network analysis is a data-driven analysis method based on exploring not only the independent relationship between every two variables, but also the independent interactions between multiple variables in complex systems.

2.3.1. Network Estimation and Visualization

Fitting an undirected network between PTSD and PTG using Gaussian graphical models(21). The connected lines in the network represent the edge weights, whose values reflect the degree of association between nodes. Subsequently strength (the sum of consecutive weighted values of a node with respect to other nodes), tightness (the sum of distances of anode with respect to all other nodes), and mediativity (the degree to which a node falls between the distances of the other nodes) were calculated for assessing the importance of each node. Visualization of GGM using qgraph package(22), placing a positive correlation coefficient in the network with a green solid line and a negative correlation with a red solid line. The thicker the connecting edges, the higher the saturation and the stronger the connection between nodes.

2.3.2. Centrality estimates

Network centrality represents how much, how strongly, and how closely a node is connected to other nodes(23). Nodes with high centrality are more likely to activate other nodes in the network and thus may be effective targets for clinical intervention(23, 24). The central part of the network (23). Studies have calculated the expected impact value of nodes in the network via the qgraph package(22, 25), which calculates the centrality index in the network .El reflects the sum of the strength of a node's ties to all other nodes in the network, and is considered to be a strong measure of a node's centrality(25, 26).

2.3.3. Analysis Bridging

Bridging centrality is often used by malefactors to connect and maintain the bridging symptoms of two networks(27),in this study, the bridging influence (BEl) was calculated using the networktools package(18, 27). When a node in PTSD and PTG is able to activate the entire network at the same time indicates that PTG and PTSD may occur simultaneously in a patient population(28). The influence of nodes in the PTSD cluster on nodes in the PTG cluster was assessed by calculating the El between nodes with a potential likelihood of contact between different clusters(29, 30).

2.3.4. Stability analysis

In this study, the centrality stability coefficient (CS-coefficient) of each network is also calculated using bootnet package to assess the stability of node strength. It is generally accepted that the CS-coefficient cannot be less than 0.25, and networks greater than or equal to 0.50 are sufficiently stable(17).

3.1. General patient demographics

The average age of 350 patients was (52.95 ± 10.63) years old, among them, male, employed, living in the city, faithless, high school or secondary school, married, with an average monthly household income of 2000–2999 yuan, and NPPC accounted for most of them, as seen in Table 1

Table 1

General Patient Data Questionnaire
		Number of cases	Percentage (%)
Age	<50years	130	37.1
	50-59years	95	27.1
	60-69years	114	32.6
	≥ 70years	11	3.1
Sex	Male	242	69.1
	Female	108	30.9
Occupation	Retire	87	24.9
Occupation	On the job	192	54.9
	Unemployment	71	20.3
Place of residence	Rural areas	125	35.7
Place of residence	Cities	225	64.3
Religious beliefs	Yes	28	8
Religious beliefs	No	322	92
Degree of education	Junior high school and below	112	32
	High school or technical secondary school	138	39.4
	Junior college	86	24.6
	Bachelor degree or above	14	4
Marital status	Unmarried	7	2
	Married	304	86.9
	Get divorced	15	4.3
	Widowed spouse	24	6.9
Average monthly household income	Less than 1000 yuan	6	1.7
	1000–1999 yuan	39	11.1
	2000-2999yuan	159	45.4
	3000-3999yuan	90	25.7
	4000-4999yuan	42	12
	5000 yuan and more	14	4
The main source of medical expenses	Medical insurance for Urban residents	116	33.1
	Employee health insurance	89	25.4
	New rural cooperative medical system	128	36.6
	Out of pocket	17	4.9
Status of Smoking	Smoking	92	26.3
	Don't smoke	138	39.4
	Quit smoking for less than 5 years	79	22.6
	Quit smoking for more than 5 years	41	11.7
Duration of illness	0–3 months	11	3.1
	3–6 months	64	18.3
	6–12 months	168	48
	1–3 years	101	28.9
	More than 3 years	6	1.7
Family history of cancer	No	314	89.7
Family history of cancer	Yes	36	10.3

3.2. Common method bias check←

Harman's one-way test was used to test for common method bias, and it was found that in the sample of lung cancer patients, 17 factors were obtained without rotation, and the variance explained by the first factor was 7.7%, which was less than the critical value of40%, so there was no significant common method bias in this study(31).

3.3. The Establishment of ptsd and ptg network structure.

The results of the network analysis are shown in Fig. 1, which shows that all the nodes in the network are directly or indirectly connected to each other, and internally they form a more tightly connected network, The shorter and darker the connecting line between two nodes in the network, the stronger the correlation between them. In the network structure, the two nodes with the strongest connections are PTST9-PTSD13, the connection between " Strong Negative Beliefs" and " Feeling Out of from Others" ,followed by PTSD5-PTSD14, the connection between " Strong Physical Reactions”to” Trouble with Positive Feelings",and PTG9-PTG10,between "More willing to express emotions" to " Ability to handle diffculties"and PTSD2-PTSD5, the link between " Nightmares" and " Strong Physical Reactions”.

3.4. Centrality analysis<

The results of the centrality analysis of the PTSD-PTG co-occurring network in lung cancer patients are shown in Fig. 2.When the centrality of the co-occurring network was examined, we found that the nodes with the highest El values were, in order. " Nightmares", "Intrusive Memories", "Excessive Blame of Self or Others" and " Concentration Difficulties".

3.5. Bridging Analysis

The results of the bridging analysis of the PTSD-PTG coexistence network in lung cancer patients are shown in Fig. 3, which shows that there are eight bridge nodes. Specifically,“lrritability", " Taking Risks", and " Trouble with Positive Feelings” in PTSD symptoms and " Accepting the Workaround", " Interpersonal Closeness", "Greater feeling of Self-reliance" ”Appreciating each day” and” Changed priorities” in PTG symptoms are bridging nodes connecting coexisting networks, PTSD and PTG in lung cancer patients showed coexistence in the psychopathological network.

3.6. Network accuracy and stability estimates

Stability was assessed by correlation stability coefficient (rcS), which showed rcS = 0.51indicating that this result is sufficiently stable. The accuracy of the network structure was tested by estimating 95% confidence intervals (Cls) for the edge weights. The Bootstrapped variability test of the edge weights shows that the 95% Cls of the Bootstrapped sampling set overlap with the original dataset, indicating that the edge weights of the constructed network are sufficiently accurate.

In this study, the network structure of PTSD and PTG in patients after diagnosis of lung cancer was investigated in 350 clinical lung cancer patients. According to the analysis of previous studies, network analysis of PTSD and PTG symptoms has been performed by previous authors, but it is the first one to conduct a study on lung cancer patients.

With regard to the assessment of PTSD, we found differences in several key aspects from the criteria for PTSD recently proposed in the 1lth edition of the International Classification of Diseases (lCD).The new ICD for PTSD includes only six of the 20 symptoms currently in the DSM-5. These six symptoms are nightmares of traumatic events, intrusive thoughts, avoidance of trauma-related thoughts, avoidance of cues suggestive of trauma, hypervigilance, and excessive startle response, as they are considered core symptoms of PTSD and are not the same as other disorders(32, 33). However, only two of the six ICD-11PTSD symptoms presented in our analysis exhibited high El.

In the network model, "nightmares of traumatic events" is the core symptom of the network, For patients, nightmares affect the quality to their sleep, cause them to teel strong negative emotions, and then wake them up, leading to the maintenance of poor physical and psychological conditions, and at the same time, patients in the depressing environment of the hospital, it is difficult to obtain effective resources for coping with trauma, making the role of trauma continue to increase. Difficulty in accessing effective resources for coping with trauma makes the role of trauma continue to increase. Intrusive thinking is the second most common symptom of this network, and intrusive thinking can lead to low mood, anxiety, depression, and other psychological problems in patients(34, 35). It can also lead to loss of socialization and inability to communicate normally with people around them, which in turn leads to negative emotions such as loneliness and helplessness, which is not conducive to the recovery of the patient, Negative emotions can cause memories of the traumatic event, and clearer memories of the traumatic event make trauma-related nightmares more likely to occur, and trauma-related nightmares make memories of the traumatic event more intrusive(36). In individuals with PTSD, nightmares and intrusive thoughts about traumatic events are often considered to be a result of the traumatic experience. The following are some of the reasons for this. Difficulty concentrating and intrusive thoughts are important core symptoms of the network that

According to the sensitization model of PTSD, patients become increasingly sensitive to threats after experiencing trauma, leading to stronger and stronger stimulus responses(37) and the interaction between these two symptoms may lead to further worsening and persistence of symptoms. Consistent with our findings, blcNally and colleagues (2015)suggested that intrusive thoughts may lead to sleep difficulties, which in turn may lead to inattention. One strategy to improve attention difficulties may be positive thinking meditation(38, 39),In addition, positive thinking meditation is considered an effective intervention for improving PTSD symptoms(40) and regulating emotional dysregulation(41, 42).

The Cognitive distortion or blaming others is an important symptom of the network, for lung cancer patients, at the beginning of the diagnosis of lung cancer will suffer a huge psychological blow, resulting in the psychology of despair, but also in the hospital during the treatment period, for the increasingly serious condition and for the disease is full of fear of the heart and other negative emotional distress, which will make the patient's cognitive changes, negative emotions are serious and will also blame others. Difficulty in concentrating has a high El value, consistent with the results of previous studies(28, 43–46). In addition, studies have shown that 15%-45% of patients with tumors have difficulty concentrating after the disease, which is manifested by reduced learning ability and memory, impaired spatial awareness, and reduced speed of processing and execution, which seriously reduces the quality of life of patients and their ability to work(47).

In the coexisting network of PTSD and PTG, the strongest link was between negative beliefs and emotional detachment in the PTSD node, followed by physiological reactions to traumatic events and the inability to experience positive emotions, increased openness and affirmation of self-efficacy, and a strong link between nightmares of traumatic events and physiological reactions to traumatic events.

A diagnosis of lung cancer can undermine a patient's self-confidence in life and can lead to negative beliefs(48).Meanwhile, as cancer is a consumptive disease, lung cancer treatment usually requires surgical resection and long-term chemotherapy and radiotherapy, and the huge cost of treatment and follow-up medical expenses will bring a serious financial burden to patients and their families(49), making patients reluctant to drag their family members down and gradually alienating themselves from their families.

The Conditioned Response Theory of PTSD views the physiological and psychological responses triggered by traumatic cues as central to the development of the PTSD pathway(50).The conditioned response theory of PTSD recognizes the physiological and psychological responses triggered by traumatic cues as central to the development of PTSD(50). Psychological and physiological responses triggered by traumatic cues have also been found to play an important role in the evolution of symptoms in lung cancer patients(51). This result also supports the physiological response theory. This result also supports the physiological response theory that the onset of PTSD symptoms is associated with physiological responses elicited by traumatic events. Traumatic events cause individuals to develop a range of physiological responses(52), such as rapid heartbeat, shortness of breath, and sweating. These physiological responses are caused by the body's stress response and are designed to cope with dangerous situations. However, when these responses persist, they can interfere with an individual's ability to regulate their emotions, making it difficult for the individual to experience positive emotions or respond to positive stimuli. On the one hand, persistent physiological responses cause the body to be under stress, consuming large amounts of energy and resources. This makes it difficult for individuals to have enough energy to experience and express positive emotions(53). On the other hand, traumatic events may cause individuals to be less sensitive to positive stimuli. As a result of past traumatic experiences, individuals may react to positive situations or information with avoidance or vigilance, thus limiting the experience of positive emotions.

Trauma-related nightmares are often associated with strong emotional memories, and the process of cancer diagnosis and treatment may cause great psychological stress and suffering to patients. These emotional memories are interrelated with nightmares, causing patients to replay these traumatic situations in their dreams, further exacerbating psychological and physiological tensions, resulting in decreased quality of life, insomnia, and anxiety.

Increased patient openness and affirmation of self-efficacy can reinforce and support each other, working together to provide more holistic and positive patient care. This dual power can help patients take better control of their health and participate more actively in their treatment and recovery programs. By combining openness and affirmation of self, efficacy, patients are better able to achieve individualized care, are better able to achieve their personal needs and goals, and achieve greater satisfaction and self-fulfillment.

In previous studies, PTSD network results showed that interpersonal alienation-somatic response and somatic response-positive affective deficits were the strongest positive edges of the association(54). In the PTSD network of childhood sexual assault victims, interpersonal alienation-emotional numbing and startle response-hypervigilance had the strongest associations(55). The present study, the first to use lung cancer patients as research subjects, showed heterogeneous results compared with previous studies, suggesting that although those who experience different types of traumatic events are all at risk of developing PTSD, there are symptom-level differences. Therefore, care should betaken to avoid blind migration of intervention methods during clinical interventions, and targeted intervention programs should be developed based on the characteristics of post-traumatic responses in breast cancer patients.

We found that the two nodes of self-destructive or reckless behavior and aggressive behavior were the most influential bridging symptoms in this network, Previous related studies have the same findings(56, 57). Self-destructive or reckless behavior was also the strongest bridging node in the network structure of PTSD and PTG among Chinese college students after the typhoon(58),self-destructive or reckless behavior had the greatest clinical significance in this study, reflecting the fact that after the disease, lung cancer patients experience a lot of psychological and physiological setbacks, embodying a high level of symptom burden and treatment needs, and that the development of targeted interventions is urgent and requires a greater understanding of the factors influencing this symptom. However, this finding is not consistent with some previous studies, and in addition to possibly being a result of the different traumatic events discussed in previous studies and the timing of the studies, it may also be due to the different assessment criteria used in the different studies. A subset of researchers were based on the Posttraumatic Stress Symptom Network constructed by the DS-4(36, 43, 59, 60). While self-destructive or reckless behavior is a new addition to the DSM-5, this symptom node does not appear in the DSM-4-based network, Self-destructive or reckless behavior and aggression can lead patients into more severe intrusive rumination thinking, which can hinder the emergence of post-traumatic growth. In addition, some foreign studies have identified stronger religiosity as the strongest bridging symptom between PTSD and PTG(61). In the present study, we used the Wang et al. Chinese scale, which was removed because the authors took into account the fact that there is a significant difference in the understanding of "religious belief between the Chinese and the many religious people in foreign countries when doing the cultural moderation.

Going with the flow, close relationships with others, increased self-empowerment, and appreciating each day are also core bridging symptoms in lung cancer patients, which is partially consistent with previous findings in breast cancer populations(62). Among lung cancer patients, close relationships with others was a core bridging symptom, whereas the nodes of interest discovery and discovering new directions were less bridging, which may be related to the fact that luna cancer patients have a greater sense of social isolation [65. 66As for the individuals in the treatment process, in addition to the important role of close relationship with others in the occurrence of PTG, individuals with increased self-efficacy and discovering the beauty of life from the bottom of their hearts are more likely to affirm their self-efficacy, discover the beauty of humanity, and make their lives more meaningful, which in turn leads to positive change and growth, The bridging role of the node of changing event priorities is also significant, and changing priorities, referred to as the important things in life, is a common phenomenon among survivors struggling with major difficulties or disasters(46, 63). In particular, the study sample experienced cancer, and some even had worse relationships with loved ones and friends(64)and, as a result, they tend to experience significant shifts in the way they approach and experience daily life. A change in event prioritization would result in patients experiencing more proactive rumination thinking and a reduction in their heart distress. Bridging symptoms can be targeted in practical intervention training in a variety of forms, such as lectures or group counseling to cultivate patients’ self-affirming positive mindset, enhance warm and beneficial interpersonal connections, and guide patients to discover the benefits of life, in order to more actively and efficiently help lung cancer patients to achieve a positive shift.

The above results show that in lung cancer patients, PTG and PTSD show coexistence in the psychopathological network, and PTG may arise as a result of the reduction of a symptom of PTSD, while a change in the level of a node of PTG may lead to the activation of a patient's PTSD symptom. Highly correlated and core symptoms may be a central target for intervention in the treatment of posttraumatic stress symptoms in lung cancer patients. Relief of highly correlated and core symptoms may lead to relief of other symptoms, contributing to the efficiency and effectiveness of the intervention. However, studies testing the above hypotheses have not been consistent in their findings(25, 26, 65, 66).

There are also limitations to this study. First, because the data used in this study were cross-sectional, it was not possible to draw causal inferences related to the temporal nature of the associations between symptoms and factors and direct effects [71l The study was notable to draw causal inferences related to the temporal nature and direct effects of symptoms and factors(67). Therefore, future longitudinal studies aimed at gating the timeseries and potential causal relationships between PTSD and PTG are needed. Second, the assessment was based on self-report scales that may increase recall risk and social desirability bias. Finally, our findings may not be generalizable to other populations with different racial/ethnic groups, ages, or sociodemographic backgrounds. Future studies should consider associations between cross-cultural symptom networks to investigate emergent phenomena(68) ( PTSS and PTG).

Identifying central and bridging symptoms in network models of PTSD and PTG has possible clinical implications; targeting these symptoms could help prevent, and improve target PTSD symptoms in lung cancer patients. Therefore, in future interventions targeting post-traumatic psychological conditions in lung cancer patients, central symptoms need to be treated with particular caution; therefore, rational use of limited resources, while doing so in a way that promotes the patient's PTG levels and focuses on the alleviation of his or her PTSD symptoms, can have a multiplier effect in improving the mental health of lung cancer patients,

Acknowledgements

Thanks to the medical staff of the Department of Oncology and Thoracic Surgery of the First Affiliated Hospital of Jinzhou Medical University for collecting the data used in this study. The authors thank the participants for their cooperation and support.

Author contributions

Jinmiao Gao: data summary, data analysis, manuscript writing.

Lan Zhang: design research, research progress, review of the manuscript.

All the authors read and approved the final manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and analyzed during the current study are not publicly available because of our agreement with the participants, but are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The cross-sectional study based on our survey was conducted in accordance with the Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects. The Ethics Committee of Jinzhou Medical University approved this study, ethics number: JZMULL2023030. Families were informed of the study and provided with information sheets describing the study. If the patient's family consents, the patient is included.

Consent for publication.

Not applicable.

Competing interests.

The authors declare no competing interests.

Author details

Jinzhou Medical University
The First Affiliated Hospital of Jinzhou Medical University

Sung, H., Ferlay, J., Siegel, R., Laversanne, M., Soerjomataram, I., Jemal, A.,Bray, F,Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.A Cancer Journal for Clinicians.2021;71(3):209-249.
Ye, Y., Li, L., Dai, Q., Yan, L.,Lin, S,Comprehensive analysis of histone methylation modification regulators for predicting prognosis and drug sensitivity in lung adenocarcinoma.Frontiers in Cell and Developmental Biology.2022;10(198-209.
Cao, M.,Chen, W,Epidemiology of lung cancer in China.Thoracic Cancer.2019;10(1):3-7.
You, s., Mao, D., Yang, J.,He, M,Epidemiology of Lung Cancer - ScienceDirect.Surgical Oncology Clinics of North America.2016;25(3):439-445.
Liu, W., Zhou, L.,Shen, H,Changes of Sleep and Quality of Life in Patients with Advanced Non-small Cell Lung Cancer before and after Chemotherapy and their Relationship.Acta Med Univ Sci Technol Huazhong.2019;48(01):98-103.
Lee, J.,Minju, K,Relationships among Uncertainty, Distress, and Quality of Life in Lung Cancer Patients: Mediating effect of Resilience.Journal of Muscle and Joint Health.2018;25(2):148-156.
Franchini, A., Myers, J., Jin, G., Shepherd, D.,Lawrence, B,Genome-Wide Transcriptional Analysis Reveals Novel AhR Targets That Regulate Dendritic Cell Function during Influenza A Virus Infection.ImmunoHorizons.2019;3(6):219-235.
Salsman, JM., Segerstrom, SC., Brechting, EH., Carlson, CR.,Andrykowski, MA,Posttraumatic growth and PTSD symptomatology among colorectal cancer survivors: a 3-month longitudinal examination of cognitive processing.Psycho-oncology.2009;18(1):30-41.
Nipp, R., Aree, C.,AndyFuh, CXC, Daniel. ,Symptoms of posttraumatic stress disorder among hospitalized patients with cancer.2018;124(16).
Gür, G.,Öztürk, F,Rumination, Basic Beliefs, and Posttraumatic Growth in Cancer Patients.Cancer Nursing.2023;1(1):1-11.
Wang, Q., Zhou, M., Zhao, D., Lv, G., Zhang, Y., Xie, Z.,Li, P,Effects of Resilience-Promoting Interventions on Cancer Patients' Positive Adaptation and Quality of Life.Cancer Nursing.2023;46(6):343-354.
Gundogmus, AG., Katar, KS., Orsel, S., Ozturk, G.,Yilmaz, KB,The relationship of potential biomarkers with psychological resilience and post-traumatic growth in female patients with breast cancer.Plos One.2023;17(12):1-14.
Cannavo, M., Sanchez-Ruiz, M-J.,Barberis, N,Structural links from trait emotional intelligence to life satisfaction and depressive symptoms in women with breast cancer: post-traumatic responses as mediators.Archives of Womens Mental Health.2024;1(1):1-10.
Zhang, Z., Bin, A., Mohammad, FIL., Shari, NI.,Lu, P,Acceptance and commitment therapy versus mindfulness-based stress reduction for newly diagnosed head and neck cancer patients: A randomized controlled trial assessing efficacy for positive psychology, depression, anxiety, and quality of life.Plos One.2022;1(1):1-18.
Cho, HM,Effects of Psychological Acceptance and Social Support on Posttraumatic Growth in Stomach Cancer Patients.Journal of Korean Clinical Nursing Research.2019;25(3):265-274.
Chen, C., Wang, L., Cao, C.,Li, G,Psychopathological network theory, methods and challenges.Advances in Psychological Science.2021;29(10):1724-1739.
Epskamp, S., Borsboom, D.,Fried, EI,Estimating Psychological Networks and their Accuracy: a Tutorial Paper.Behavior Research Methods.2016;50(4).
Jones, PJ., Ma, R.,Mcnally, RJ,Bridge Centrality: A Network Approach to Understanding Comorbidity.Multivariate Behavioral Research.2019;1(1):1-15.
Zhou, X., Wu, X.,Zhen, R,Assessing the Latent Structure of Dsm-5 Ptsd among Chinese Adolescents after the Ya'an Earthquake.Psychiatry Research.2017.
Wang, J., Chen, Y., Wang, Y.,Liu, X,Revision of the Posttraumatic Growth Inventory and Testing Its Reliability and Validity.Journal of Nursing Science.2011;1(1):26-28.
Costantini, G., Epskamp, S., Borsboom, D., Perugini, M., M?ttus, R., Waldorp, LJ.,Cramer, AJ,State of the aRt personality research: A tutorial on network analysis of personality data in R.Journal of Research in Personality.2015;54(13-29.
Epskamp, S., Angélique, C., Waldorp, L., Schmittmann, V.,Borsboom, D,qgraph: Network Visualizations of Relationships in Psychometric Data.Journal of Statistical Software.2012;48(4):367-371.
Borsboom, D.,Cramer, A,Network analysis: an integrative approach to the structure of psychopathology.Annu Rev Clin Psychol.2013;9(1):91-121.
AngeLique, C.,Borsboom, D,Problems Attract Problems: A Network Perspective on Mental Disorders.Emerging Trends in the Social andBehavioral Sciences.2015;2(1):1-15.
Robinaugh, D., Millner, A.,Mcnally, R,Identifying Highly Influential Nodes in the Complicated Grief Network.Jouranal of Abnorm Psychol.2016;125(6):747-757.
Fionneke, B., Snippe，, E., Stijndevos, J.,Wichers., HM,Can We Jump from Cross-Sectional to Dynamic Interpretations of Networks? Implications for the Network Perspective in Psychiatry.Psychotherapy Psychosomatics.2017.
Afzali, M., Sunderland, M., Teessonmaree, CN, Katherine. .,Slade, T,A network approach to the comorbidity between posttraumatic stress disorder and major depressive disorder: The role of overlapping symptoms.Journal of Affective Disorders.2017.
Richard, M., Donald, R., Gwyneth, WW., Li, W., Marie, D.,Denny, B,Mental Disorders as Causal Systems: A Network Approach to Posttraumatic Stress Disorder.Clinical Psychological Science.2015;1(1):216-218.
Bellet, B., Jones, P., Neimeyer, R.,Mcnally, R,Bereavement Outcomes as Causal Systems: A Network Analysis of the Co-Occurrence of Complicated Grief and Posttraumatic Growth.Clinical Psychological Science.2018;1(1):1-14.
Oosterhoff, B., Metzger, A.,Alvis, L,Similarities and Differences in Adolescents' Organized and Civic Activity Networks across Rural and Non-Rural Communities.Journal of Youth and Adolescence.2019;15(1):1-17.
Podsakoff, P., Mackenzie, S., Lee, JY.,Podsakoff, N,Common method biases in behavioral research: a critical review of the literature and recommended remedies.The Journal of applied psychology.2003;88(5):879-903.
Cloitre, M., Garvert, D., Brewin, C., Bryant, R.,Maercker, A,Evidence for proposed ICD-11 PTSD and complex PTSD: a latent profile analysis.European Journal of Psychotraumatology.2013;4(1):20706-.
Andreas, M., Brewin, C., Bryant, R., Marylene, C., Reed, G., Mark, VO., Asma, H., Jones, L., Ashraf, K.,Llosa, A,Proposals for mental disorders specifically associated with stress in the International Classification of Diseases-11.Lancet (London, England).2013;381(98):1683-1685.
David, B., Eva, A., Dan, A., Anders, B., Stefan, C., Jonas, H., Karin, SK., Anna, L., Hanna, N., Mattias, P., Gunnar, S., Thordis, T., Peter, W.,Eva, H,Do negative intrusive thoughts at diagnosis predict impaired quality of life, depressed mood and waking up with anxiety 3, 12 and 24 months after radical prostatectomy? – a longitudinal study Scandinavian Journal of Urology.2020;54(3):220-226.
Spinhoven, P., Hemert, AV.,Penninx, B,Repetitive negative thinking as a predictor of depression and anxiety: A longitudinal cohort study.Journal of affective disorders.2018;241(1):216-225.
Birkeland, MS.,Heir, T,Making connections: exploring the centrality of posttraumatic stress symptoms and covariates after a terrorist attack.European Journal of Psychotraumatology.2017;8(3):1-13.
Stam, R,PTSD and stress sensitisation: a tale of brain and body Part 1: human studies.Neuroscience Biobehavioral Reviews.2007;31(4):530-557.
Hlzel, B., Lazar, S., Gard, T., Schuman Olivier, Z.,Ott, U,How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective.Perspectives on Psychological Science.2011;6(1):537-559.
Keng, SL., Smoski, M.,Robins, C,Effects of mindfulness on psychological health: a review of empirical studies.Clinical Psychology Review.2011;31(6):1041-1056.
Lara, H., Alici, RM., Benjamin, C., Eric, A., Melony, S., Marika, B., Roberta, M.,Shanman, SH,Meditation for Posttraumatic Stress: Systematic Review and Meta-Analysis.Psychological Trauma.2017;9(4):453-460.
Mitchell, J., Mcintyre, E., English, J., Dennis, M.,Kollins, S,A Pilot Trial of Mindfulness Meditation Training for ADHD in Adulthood: Impact on Core Symptoms, Executive Functioning, and Emotion Dysregulation.Journal of Attention Disorders.2017;21(13):1105.
Mitchell, K., Wolf, E., Bovin, M., Lee, L., Green, J., Rosen, R., Keane, T.,Marx, B,Network Models of DSM-5 Posttraumatic Stress Disorder: Implications for ICD-11.Abnorm Psychol.2017;126(3):355-366.
Fried, E., Eidhof, M., Palic, S., Costantini, G., Dijk, HV., Bockting, C., Engelhard, I., Armour, C., Nielsen, A.,Karstoft, KI,Replicability and Generalizability of Posttraumatic Stress Disorder (PTSD) Networks: A Cross-Cultural Multisite Study of PTSD Symptoms in Four Trauma Patient Samples.Sage Choice.2018;6(3).
Sophia, VS., Eiko, F., Cherie, A.,Robert, P,Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of US military veterans.Journal of affective disorders.2018;229(63-68.
Birkeland, MS., Greene, T.,Spiller, TR,The network approach to posttraumatic stress disorder: a systematic review.European Journal of Psychotraumatology.2020;11(1):1-14.
Jacqueline, P., Benjamin, B., Payton, J., Gwyneth, W., Li, W.,Richard, M,Posttraumatic stress or posttraumatic growth? Using network analysis to explore the relationships between coping styles and trauma outcomes Journal of Anxiety Disorders.2021;78(1):1-10.
Li, S.,Xie, C,Research Progress on Chemo-brain：Pathogenesis and Treatment with Integrated Traditional Chinese Medicine and Western Medicine.Journal Of Oncology.2019;25(4):355-358.
Sarma, E., Quaife, S., Rendle, K.,Kobrin, S,Negative cancer beliefs: Socioeconomic differences from the awareness and beliefs about cancer survey.Psycho‐Oncology.2020;23(1):66-78.
Long, X., Lu, F., Xiang, X., Yang, J., Xu, C., Zhang, P., Yu, S., Wang, Q.,Hu, C,Economic Burden of Malignant Tumors——Yichang City,Hubei Province,China,2019.Chinese Center for Disease Control and Prevention Weekly Report.2022;4(15):1-4.
Keane, T., Marshall, A.,Taft, C,Posttraumatic Stress Disorder: Etiology, Epidemiology, and Treatment Outcome.Social Science Electronic Publishing.2006;2(1):161-197.
Liang, Y., Zheng, H.,Liu, Z,Changes in the network of posttraumatic stress disorder among children after the Wenchuan earthquake: A four-year longitudinal study.Acta Psychologica Sinica.2020;52(11):1301-1312.
Andrea, P., Richard, K., Christopher, W., Suzy, R., Naomi, R.,David, F,An Ambulatory Polysomnography Study of the Post-traumatic Nightmares of Post-traumatic Stress Disorder.Sleep.2017;23(1):1-32.
Bonanno, G,Resilience in the Face of Potential Trauma.Journal of Clinical Psychology.2005;14(3):135-138.
Lazarov, A., Suarez-Jimenez, B., Levy, O., Coppersmith, D.,Neria, Y,Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients.Psychological Medicine.2019;50(13):1-17.
Richard, M., Alexandre, H.,Donald, R,A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse.European Journal of Psychotraumatology.2017;8(1):1-10.
Jiang, W., Ren, Z., Yu, L., Tan, Y.,Shi, C,A Network Analysis of Post-traumatic Stress Disorder Symptoms and Correlates During the COVID-19 Pandemic.Frontiers in Psychiatry.2020;11(1-13.
Talbot, M.Gender Differences in Risk-Taking, Impulsivity, and Inhibitory Control Related to PTSD.University of Colorado Colorado Springs,Colorado.(2020)
Yuan, G., Park, C., Birkeland, S., Yip, P.,Hall, B,A Network Analysis of the Associations Between Posttraumatic Stress Symptoms and Posttraumatic Growth Among Disaster‐Exposed Chinese Young Adults.Journal of Traumatic Stress.2021;1(0):1-13.
Bryant, R., Mark, C., Meaghan, D., David, F., Mcfarlane, A., Derrick, S.,Dusan, HP,Acute and chronic posttraumatic stress symptoms in the emergence of posttraumatic stress disorder: a network analysis.JAMA Psychiatry.2017;74(2):1-8.
Richard, M,Can network analysis transform psychopathology?Behaviour Research and Therapy.2016;86(1):95-104.
Ma, Z., Zhu, Y., Tao, Y., Yang, Z., Huang, S., Liu, W., Chen, Y., Ye, H.,Fan, F,Using network analysis to explore the key bridge symptoms between posttraumatic stress symptoms and posttraumatic growth among survivors 10 years after the Wenchuan earthquake in China.Journal of psychiatric research.2022;150(1):173-179.
Tan, R.Coexistence and Influencing Mechanism of Posttraumatic Stress Disorder and Growth in Breast Cancer Patients.ZheJiang University,Hang,Zhou.(2022)
Richard, T.,Lawrence, C,Posttraumatic Growth: Conceptual Foundations and Empirical Evidence.Psychological Inquiry.2004;15(1):1-18.
Ran, Z., Sun, R., Jia, J., Che, Z., Li, R., He, Y.,Chu, Q,Advances in Life Goal Adjustment Ability of Cancer Patients and Its Effect on Physical and Mental Health.Chinese General Medicine.2023;2(1):1-8.
Christina, H., Robinaugh, D., Anke, E.,Birgit, K,Understanding the Emergence of Chronic Posttraumatic Stress Disorder Through Acute Stress Symptom Networks.JAMA Psychiatry.2017;74(6):649-650.
Rodebaugh, T., Tonge, N., Piccirillo, M., Fried, E., Horenstein, A., Morrison, A., Goldin, P., Gross, J., Lim, M.,Fernandez, K,Does Centrality in a Cross-Sectional Network Suggest Intervention Targets for Social Anxiety Disorder?Journal of Consulting and Clinical Psychology.2018;86(10):831–844.
Lazarov, A., Jimenez, BS., Levi, O., Coppersmith, DDL.,Neria, Y,Symptom structure of PTSD and co-morbid depressive symptoms – a network analysis of combat veteran patients – corrigendum (author name).Psychological Medicine.2020;1-1.
Marianne, SB., Talya, G.,Tobias, RS,The network approach to posttraumatic stress disorder: a systematic review.European Journal of Psychotraumatology.2020;11(1):1-14.

No competing interests reported.

Download PDF

Editor assigned by journal
22 May, 2024
Submission checks completed at journal
21 May, 2024
First submitted to journal
17 May, 2024

You are reading this latest preprint version

A Network Analysis of the Associations Between Posttraumatic Stress Symptoms and Posttraumatic Growth Among Lung Cancer Patient

Status:

Version 1

Abstract

Figures

1. Instroduction

2. Methods

2.1. Participants and procedures

2.2. Survey tools

2.3. Data analysis

2.3.1. Network Estimation and Visualization

2.3.2. Centrality estimates

2.3.3. Analysis Bridging

2.3.4. Stability analysis

3. Results

3.1. General patient demographics

3.2. Common method bias check←

3.3. The Establishment of ptsd and ptg network structure.

3.4. Centrality analysis<

3.5. Bridging Analysis

3.6. Network accuracy and stability estimates

4. Disscussion

5. Conclusions

Declarations

References

Additional Declarations

Status:

Version 1