Symptom clusters and symptom networks of symptom experiences in patients with SARS-CoV-2 infection.

CONTEXT

Symptom clusters and symptom networks can potentially enhance the precision in managing symptoms. However, limited research has been conducted on the symptom network experienced by patients infected with Severe Acute Respiratory Syndrome Coronavirus 2.

OBJECTIVES

To identify the composition of symptom clusters in SARS-CoV-2 infected patients, establish a symptom network to explore the centrality indices, and investigate independent risk factors influencing the occurrence of symptom clusters.

METHODS

Between February 2022 and June 2023, a total of 418 patients diagnosed with SARS-CoV-2 infection were recruited in the Second Affiliated Hospital of Chongqing Medical University. A symptom questionnaire was utilized to assess three dimensions encompassing a comprehensive range of 40 symptoms. Principal component analysis was employed to identify distinct symptom clusters, while network analysis elucidated the interconnections among these symptoms. Univariate analysis and multiple linear regression analysis were conducted to investigate the factors influencing the manifestation of these symptom clusters.

RESULTS

Eight symptom clusters were identified, namely the nasopharyngeal-related symptom cluster, the circulatory-related symptom cluster, the neural-related symptom cluster, the physical-related symptom cluster, the digestive-related symptom cluster, the respiratory-related symptom cluster, the fever-related symptom cluster, and the sensory-related symptom cluster. The three centrality indices with the highest values were chest tightness (rs = 7.84, rc = 0.013, rb = 6.99), muscle aches (rs = 7.32, rc = 0.013, rb = 2.72), and smell abnormality (rs = 6.56, rc = 0.011, rb = 4.58). Variables including age, gender, income, education, hyperlipidemia, chronic bronchitis, and tumor were associated with the occurrence of these eight symptom clusters.

CONCLUSION

The findings of this study highlight the necessity to explore symptom clusters and symptom networks in order to enhance the effectiveness of symptom management in patients with SARS-CoV-2 infection. Particularly crucial is the evaluation of centrality indices as an integral component of caring for such patients. Early detection of high-risk individuals within each symptom cluster can provide a scientific foundation for developing interventions that will optimize patient prognosis.