Exploring Symptom Clusters in Chinese Patients with Diabetic Kidney Disease: A Network Analysis

Int J Gen Med. 2024 Mar 7:17:871-884. doi: 10.2147/IJGM.S447921. eCollection 2024.

Abstract

Purpose: The research on symptom management in patients with diabetic kidney disease (DKD) has shifted from separate symptoms to symptom clusters and networks recently. This study aimed to evaluate the unpleasant symptoms of DKD patients, and to investigate how these symptom clusters could affect patients.

Methods: 408 DKD patients were recruited in this cross-sectional study. The symptoms of DKD patients were measured using the modified Dialysis Symptom Index. Network analysis was employed to evaluate the symptom network and the characteristics of individual nodes, while factor analysis was utilized to identify symptom clusters.

Results: Blurred vision was the most prevalent symptom among DKD patients. The symptoms identified as the most distressing, severe, and frequent were light headache or dizziness, arteriovenous fistula/catheterization pain, and diarrhea, respectively. Five symptom clusters were obtained from factor analysis, and the most central symptom cluster in the entire symptom network was sexual dysfunction.

Conclusion: This study identified five symptom clusters in Chinese DKD patients, with sexual dysfunction emerging as the most central cluster. These findings carry significant clinical implications, underscoring the necessity of assessing symptom clusters and their associations to enhance symptom management in DKD patients. Further research is essential to elucidate the underlying mechanisms of symptoms and to clarify the associations among symptoms in DKD patients across different disease trajectories or treatment modalities.

Keywords: diabetic kidney disease; network analysis; symptom cluster; symptom management.

Grants and funding

The West China Nursing Discipline Development Special Fund Project, Sichuan University, Grant/Award Number: HXHL21013. The Scientific and Technological Department of Sichuan Province, China, Grant/Award Number: 2022YFS0266.