Exploring the dynamics of postoperative symptom networks in liver cancer patients: a cross‑lagged panel network analysis

Abstract Objective: To dynamically analyze symptom networks of hepatocellular carcinoma patients at 3 early postoperative time points using cross-lagged network analysis and to explore predictive relationships between postoperative. Methods: A longitudinal study was conducted involving 498 hepatocellular carcinoma patients who underwent surgical treatment at a tertiary hospital in Kunming between February 2024 and March 2025. The Anderson Symptom Scale and a hepatocellular carcinoma-specific module were used to assess symptom prevalence and severity on postoperative day 2 (T1), day 4 (T2), and day 7 (T3). Cross-lagged networks were constructed using longitudinal data in R software to identify key symptom nodes and explore predictive and interactive relationships. Results: Fatigue exhibited the highest out-strength and out-expected influence (out-EI) during both T1→T2 and T2→T3. Specifically, during T1→T2, fatigue (out-strength=0.10, out-EI=0.10) was prospectively associated with distress, heartburn, and shortness of breath at T2. During T2→T3, fatigue (out-strength=0.24, out-EI=0.24) was prospectively associated with poor appetite, bloating, and jaundice at T3. Conclusion: During the early postoperative period, fatigue consistently was prospectively associated with subsequent symptoms and had predictive value in hepatocellular carcinoma patients. Fatigue should be prioritized as a key symptom for monitoring and intervention in clinical symptom management to optimize treatment strategies.

Authors

Institutions

Publication Details

Journal
Interdisciplinary Nursing Research
Published
2025-12-01
DOI
https://doi.org/10.1097/nr9.0000000000000106
Primary Topic
Cancer survivorship and care
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the dynamics of postoperative symptom networks in liver cancer patients: a cross‑lagged panel network analysis

Yaxian Wang, Jie Zhao, Chengxin Sun, Ke Lin et al.
Interdisciplinary Nursing Research
Cancer survivorship and care
article

Exploring the dynamics of postoperative symptom networks in liver cancer patients: a cross‑lagged panel network analysis

Yaxian Wang, Jie Zhao, Chengxin Sun, Ke Lin, Qinghong Yang, Fan Xue
article en

Abstract

Abstract Objective: To dynamically analyze symptom networks of hepatocellular carcinoma patients at 3 early postoperative time points using cross-lagged network analysis and to explore predictive relationships between postoperative. Methods: A longitudinal study was conducted involving 498 hepatocellular carcinoma patients who underwent surgical treatment at a tertiary hospital in Kunming between February 2024 and March 2025. The Anderson Symptom Scale and a hepatocellular carcinoma-specific module were used to assess symptom prevalence and severity on postoperative day 2 (T1), day 4 (T2), and day 7 (T3). Cross-lagged networks were constructed using longitudinal data in R software to identify key symptom nodes and explore predictive and interactive relationships. Results: Fatigue exhibited the highest out-strength and out-expected influence (out-EI) during both T1→T2 and T2→T3. Specifically, during T1→T2, fatigue (out-strength=0.10, out-EI=0.10) was prospectively associated with distress, heartburn, and shortness of breath at T2. During T2→T3, fatigue (out-strength=0.24, out-EI=0.24) was prospectively associated with poor appetite, bloating, and jaundice at T3. Conclusion: During the early postoperative period, fatigue consistently was prospectively associated with subsequent symptoms and had predictive value in hepatocellular carcinoma patients. Fatigue should be prioritized as a key symptom for monitoring and intervention in clinical symptom management to optimize treatment strategies.

Interdisciplinary Nursing ResearchVol. 4(4)
Gansu Agricultural University (CN), Kunming Medical University (CN), Chongqing Three Gorges Central Hospital (CN)
Openalex Percentile: Top 31%
Cancer survivorship and care
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.