Network-guided symptom targets in maintenance hemodialysis using in silico interventions: a multicenter...

Area of Science:

• Nephrology
• Symptom Science
• Computational Biology

Background:

• Maintenance hemodialysis (MHD) patients face heterogeneous, concurrent symptoms.
• A framework to prioritize symptom interventions based on interdependencies is needed.
• In silico simulations can guide symptom management in resource-limited settings.

Purpose of the Study:

• To quantitatively rank modifiable symptom targets in MHD patients.
• To provide decision support for symptom-focused management.
• To address the lack of evidence frameworks for symptom interdependencies.

Main Methods:

• Recruited 962 adult MHD patients from 13 centers in Southwest China.
• Assessed symptoms using the modified Dialysis Symptom Index.
• Employed exploratory factor analysis, latent profile analysis, and NodeIdentifyR for in silico perturbation simulations.

Main Results:

• Identified seven symptom clusters and two symptom-burden profiles.
• Feeling nervous showed highest centrality; fatigue had the strongest bridging role.
• Simulated alleviation of anxiety, worry, and nervousness reduced overall burden by 14.4-17.3%.

Conclusions:

• Emotional symptoms are priority targets for intervention in MHD patients.
• Findings offer non-causal decision support for symptom management and resource allocation.
• Further longitudinal studies and intervention trials are warranted.