Exploring core and bridge symptoms in patients recovering from stroke: a network analysis

 No clue how anything here is going to get you recovered. 

Exploring core and bridge symptoms in patients recovering from stroke: a network analysis

Yao Huang^1,2Songmei Cao^1*Teng Li¹Jingjing Wang¹Zhuoran Xia¹

• ¹Department of Nursing, The Affiliated Hospital of Jiangsu University, Zhenjiang, China
• ²Department of Neurology, Changzhou Seventh People’s Hospital, Changzhou, China

Background: Patients recovering from stroke experience a variety of symptoms that present as a synergistic and mutually reinforcing “symptom cluster,” rather than as singular symptoms. In this study, we researched and systematic analyzed these symptom clusters, including core and bridge symptoms, to help determine the relationships between symptoms and to identify key symptom targets, providing a new approach for formulating precise symptom management interventions.

Methods: Convenience sampling was applied to select 432 stroke recovery patients treated in the Seventh People’s Hospital of Changzhou City from August 1, 2023 to April 14, 2024. Subsequently, a cross-sectional survey was conducted using the General Information Questionnaire and Stroke Symptom Experience Scale to extract symptom clusters via exploratory factor analysis. Finally, the “qgraph” and “bootnet” packages in the R language were used to construct a network layout to describe the relationships between symptoms and calculate the centrality index.

Results: The average age of the 432 enrolled recovering stroke patients was 68.17 ± 12.14 years, including 268 males (62.04%) and 164 females (37.96%), none of whom underwent surgical intervention. Among this cohort, the 3 symptoms with the highest incidence rates were “limb weakness” (A2, 80.56%), “fatigue” (A5, 77.78%), and “limitations of limb movement” (A1, 68.06%). A total of 5 symptom clusters were extracted: the somatic activity disorder, mood-disorder-related, cognitive–linguistic dysfunction, somatic-pain-related, and foot dysfunction symptom clusters. In the symptom network, the 2 most common symptoms in terms of intensity and expected impact were “fatigue” (A5, r_s = 1.14, r_e = 1.00) and “pessimism about the future” (B3, r_s = 1.09, r_e = 1.02). The symptom with the strongest bridge intensity was “limb pain” (D1, r_s = 2.64).

Conclusion: This study uses symptom network analysis to explore the symptoms of stroke patients during recovery, identifying core symptoms and bridge symptoms. Based on these findings, we can develop more targeted management plans to improve the accuracy and efficiency of interventions. Through this management approach, we can enhance treatment effectiveness, reduce unnecessary medication, lower adverse drug reactions, and optimize the allocation of medical resources.

1 Introduction

Stroke is an acute cerebrovascular disease caused by the sudden rupture of blood vessels in the brain, or the obstruction of blood vessels, resulting in a lack of blood flow to the brain, resulting in brain tissue damage or dysfunction. This disease is characterized by high prevalence, mortality, recurrence, and disability rates (1, 2). According to Global Burden of Disease study data (3), as of 2019, there were approximately 101 million stroke patients worldwide, with stroke emerging as the second leading cause of death worldwide. As the emergency medical service system has continuously improved, the survival rate of stroke patients has improved; however, after acute treatment, these patients need to progress through a long recovery. Patients in the poststroke recovery period not only suffer from complications caused by the disease itself, such as hemiparesis, aphasia, and dysphagia, but are also prone to symptoms such as depression, anxiety, fatigue, sleep disorders, and chronic pain (4–7). These symptoms often co-occur and are interrelated, with varying degrees of severity, forming distinct symptom clusters. Synergistic effects between symptom clusters can further aggravate the symptom burden of patients, seriously impacting their quality of life (8).

A review of the prior literature on post-stroke sequelae revealed that many studies primarily focused on exploring single symptoms experienced by stroke patients, such as fatigue (8), sleep disturbances (9), depression (10), and anxiety (9), with less attention paid to symptom clusters, core symptoms, and bridging symptoms. For example, Schepers et al. (11) predicted the occurrence of depressive symptoms in recovering stroke patients in a longitudinal follow-up study; while Kirkevold et al. (12) explored the experience, prevalence, characteristics, and contributing factors of fatigue poststroke in a qualitative interview study. Further, using a questionnaire-based study, Wallace et al. (13) reported that stroke may further exacerbate sleep disturbances, which can in turn affect the stroke recovery process and increase the risk of stroke recurrence. Diamond et al. (14) explored the prevalence of anxiety in stroke survivors and its relative impact on quality of life following a cross-sectional study design. Although useful, all of these prior studies explored various sequelae caused by stroke, but subsequently focused on only one symptom. In fact, patients who have recovered from stroke rarely experience only a single symptom, and the common interaction of multiple symptoms may increase the symptom burden of patients recovering from stroke, leading not only to impaired physical functioning, but also seriously impacting their psychological and social functioning. However, few studies have provided information about symptom clusters in patients recovering from stroke, which is crucial for improving the efficacy of symptom interventions (8–14). As such, the present study aimed to provide information about symptom clusters in patients recovering from stroke, which is crucial for improving the efficacy of symptom interventions.

Although the concept of symptom clusters can facilitate cluster symptom management, the lack of differentiation between primary and secondary relationships can lead to ineffective management. In the context of relationships, the terms “primary” and “secondary” are commonly used to describe the level of importance or significance. Primary relationships are typically the most important related core symptom (15), while secondary relationships include relationships that are still important and meaningful, but may not carry the same level of importance in stroke patients as primary relationships (16). In recent years, the concept of symptom networks has been gradually applied in chronic disease management, an approach that uses nodes and edges to represent symptoms and their relationships, providing a new method to identify core symptoms and gain insight into the complexity of symptom clusters by visualizing and quantitatively interpreting the relationships between various symptoms and symptom clusters (17). The core symptoms in a network include those that are most strongly associated with other symptoms, playing a key role in activating other symptoms (18). Interventions targeting core symptoms can accelerate the deactivation of the symptom network, as well as increasing the precision and efficiency of interventions (19, 20). In addition, previous related studies (21–23) have shown that bridging symptoms are associated with the structure of symptom clusters in the symptom network. Bridge nodes or edges play a critical transmission role and accelerate the spread of information in the propagation of symptoms from one cluster to another (24). By intervening in bridging symptoms, we can prevent the interconversion of symptoms, thereby breaking the connections between symptom clusters and reducing the symptom burden faced by patients.

The primary objectives of this study were to identify symptom occurrence and analyze symptom clusters in patients recovering from stroke and to generate a symptom network of patients recovering from stroke, exploring core and bridge symptoms to provide a basis for symptom management in patients recovering from stroke.