Longitudinal symptom network patterns in cancer patients during the end of life

Patients with cancer at the end of life experience a wide range of distressing physical and psychological symptoms, which often interact and intensify each other. Understanding how these symptoms are connected is essential for improving quality of life and providing effective palliative care. This study analyzed data from 26,318 medical visits of 8,026 patients receiving palliative care, focusing on symptom interactions during the final months of life. Symptom networks were estimated using the Ising Model across six time periods prior to death, with centrality measures used to identify key symptoms, and the Walktrap algorithm applied to detect symptoms communities. The results revealed that different symptoms and their relationships were important at different stages, although certain symptoms-such as weakness, cognitive problems, and issues with physical appearance-consistently played central roles. Strong and persistent connections were observed between nausea and vomiting, as well as anxiety and depression. Additionally, five distinct symptom communities were identified over time. These findings provide insight into the dynamic nature of symptom interactions near the end of life and highlight key targets for timely and tailored palliative interventions, ultimately aiming to improve patient comfort and support a more peaceful dying process.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-11-23
DOI
https://doi.org/10.1038/s41598-025-29627-6
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Longitudinal symptom network patterns in cancer patients during the end of life

Ayoub Mohammadian, Sahand Mostajeran, Elnaz Nasirzadeh
Scientific Reports
Mental Health Research Topics
article

Longitudinal symptom network patterns in cancer patients during the end of life

Ayoub Mohammadian, Sahand Mostajeran, Elnaz Nasirzadeh
article en

Abstract

Patients with cancer at the end of life experience a wide range of distressing physical and psychological symptoms, which often interact and intensify each other. Understanding how these symptoms are connected is essential for improving quality of life and providing effective palliative care. This study analyzed data from 26,318 medical visits of 8,026 patients receiving palliative care, focusing on symptom interactions during the final months of life. Symptom networks were estimated using the Ising Model across six time periods prior to death, with centrality measures used to identify key symptoms, and the Walktrap algorithm applied to detect symptoms communities. The results revealed that different symptoms and their relationships were important at different stages, although certain symptoms-such as weakness, cognitive problems, and issues with physical appearance-consistently played central roles. Strong and persistent connections were observed between nausea and vomiting, as well as anxiety and depression. Additionally, five distinct symptom communities were identified over time. These findings provide insight into the dynamic nature of symptom interactions near the end of life and highlight key targets for timely and tailored palliative interventions, ultimately aiming to improve patient comfort and support a more peaceful dying process.

Scientific ReportsVol. 15(1)
University of Tehran (IR), Industrial Management Institute (IR)
Openalex Percentile: Top 62%
Mental Health Research Topics
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.