ArticleAnnals of medicine2023
Probabilistic graphical modelling using Bayesian networks for predicting clinical outcome after posterior decompression in patients with degenerative cervical myelopathy.
Article in Annals of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 8 citations in OpenAlex.
- Socioeconomic Factors Associated with Household Food Insecurity in Guamote Canton, Ecuador.Nutrients · 2026Article
- Bayesian Graphical Models for Multiscale Inference in Medical Image-Based Joint Degeneration Analysis.Diagnostics (Basel, Switzerland) · 2025Review
- Identification of Bone Metastasis-Related Gene Networks in Lung Cancer: Implications for Bone Metabolism.Journal of bone metabolism · 2025Article
- Duration of symptoms before diagnosis in degenerative cervical myelopathy: A systematic review and meta-analysis.Brain & spine · 2025Review
- Application of artificial intelligence in postoperative orthopedic rehabilitation: a scoping review.Frontiers in digital health · 2025Review
- Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.Digital healthArticle
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProbabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model.
objectiveTo develop PGM for the prediction of clinical outcome in patients with degenerative cervical myelopathy (DCM) after posterior decompression and to use PGM to identify causal predictors of the outcome.
methodsWe included data from 59 patients who had undergone cervical posterior decompression for DCM. The candidate predictive parameters were age, sex, body mass index, trauma history, symptom duration, preoperative and last Japanese Orthopaedic Association (JOA) scores, gait impairment, claudication, bladder dysfunction, Nurick grade, American Spinal Injury Association (ASIA) grade, smoking, diabetes mellitus, cardiopulmonary disorders, hypertension, stroke, Parkinson's disease, dementia, psychiatric disorders, arthritis, ossification of the posterior longitudinal ligament, cord signal change, postoperative kyphosis and the cord compression ratio.
resultsIn regression analyses, preoperative JOA (PreJOA) score, presence of a psychiatric disorder, and ASIA grade were identified as significant factors associated with the last JOS score. Dementia, sex, PreJOA score and gait impairment were causal factors in the PGM. Sex, dementia and PreJOA score were direct causal factors related to the last follow-up JOA (LastJOA) score. Being female, having dementia, and having a low PreJOA score were significantly related to having a low LastJOA score.
conclusionsThe causal predictors of surgical outcome for DCM were sex, dementia and PreJOA score. Therefore, PGM may be a useful personalized medicine tool for predicting the outcome of patients with DCM.
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