Evidence map›Paper›PMID 40496136›Full record

ArticleFrontiers in pain research (Lausanne, Switzerland)2025

Modeling chronic pain interconnections using Bayesian networks: insights from the Qatar Biobank study.

Aisha Ahmad M A Al-Khinji, Dhafer Malouche

Abstract read
In one paragraph

Article in Frontiers in pain research (Lausanne, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Aisha Ahmad M A Al-KhinjiCollege of Medicine, Qatar University, Doha, Qatar.
Dhafer MaloucheClinical Translational Science Research Group, QU Health, Qatar University, Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study examines the interdependencies among different chronic pain locations and their relationships with age and gender, critical for effective clinical strategies. Methods: A Bayesian network approach was applied to 2,400 adult participants (18+ years; 50% male, 50% female) from the Qatar Biobank (QBB). Participants were categorized into young (18-35 years, 40.9%), middle-aged (36-60 years, 50.6%), and seniors (61+ years, 8.5%). Results: The model identified direct and indirect associations among pain locations and demographic factors, quantified by odds ratios (ORs). Younger females had the highest probability of headaches or migraines (48.6%) compared to younger males (31.2%), with probabilities decreasing across age (OR 1.917; 95% CI 1.609-2.284). Hand pain strongly correlated with hip pain (OR 8.691; 95% CI 6.074-12.434) and neck or shoulder pain (OR 4.451; 95% CI 3.302-6.000). Back pain was a key predictor of systemic pain, with a 37.9% probability of generalized pain when combined with hand pain (OR 7.682; 95% CI 5.293-11.149), dropping to 6.6% for back pain alone. Age, back pain, and foot pain collectively influenced knee pain, which reached 72.7% in older individuals with foot and back pain (OR 4.759; 95% CI 3.704-6.114). Discussion: These Bayesian network parameters explicitly reveal probabilistic interdependencies among pain locations, suggesting that targeted interventions for key anatomical regions could effectively mitigate broader chronic pain networks. The model also elucidates how demographic predispositions influence downstream pain patterns, providing a clear and actionable framework for personalized chronic pain management strategies.

Indexed as

Bayesian networkconditional probabilitiespain interdependenciesprobabilistic modelingQatar Biobank (QBB)systemic pain

Identifiers

PMID40496136
PMCPMC12148875

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.