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Symptom interactions and candidate intervention targets in patients with esophageal cancer: Network analysis with
Xiaomeng Wen1, Yan Liu2, Yuan Lin2
1Department of Anesthesiology and Perioperative Medicine, The First People's Hospital of Changzhou, Changzhou, China.
Objective:
To characterize symptom interactions and identify candidate intervention targets in patients with esophageal cancer using network analysis and computer-simulated symptom perturbations.
Methods:
This two-center cross-sectional study recruited 255 patients with esophageal cancer via convenience sampling between May 2020 and May 2023. Participants completed the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30) and the esophageal cancer-specific module (EORTC QLQ-OES18). A Gaussian graphical model (GGM) was used to characterize symptom associations and centrality; node predictability was estimated using a mixed graphical model (MGM); and a bootstrap-averaged Bayesian network was constructed to explore directed probabilistic associations; thresholds applied for network modeling were defined as analytic settings rather than validated clinical cut-offs. Ising model-based simulations (NodeIdentifyR algorithm) were then used to explore the potential dynamic effects of symptom-targeted strategies.
Results:
Fatigue showed the highest static centrality, and appetite loss showed a directed association with sleep disturbance and taste disturbance. Simulated interventions-model-based projections rather than observed effects-indicated that alleviating appetite loss or fatigue reduced the expected number of active symptoms by 2.61 and 2.58, respectively. In the aggravation scenario, pain produced the largest projected increase; choking when swallowing produced the second-largest increase despite its low static centrality, indicating divergence between static and simulation-based findings.
Conclusions:
Appetite loss and fatigue emerged as candidate targets associated with the largest projected changes in global network activation, while pain emerged as the candidate target associated with the largest projected increase, consistent with its structural prominence in the network; choking when swallowing emerged as an additional simulation-informed target despite modest static centrality. As model-based projections, these hypothesis-generating findings warrant prospective evaluation in future interventional studies.
