Probabilistic graphical modelling using Bayesian networks for predicting clinical outcome after posterior decompression in patients with degenerative cervical myelopathy

BACKGROUND: Probabilistic graphical modelling (PGM) can be used to predict risk at the individual patient level and show multiple outcomes and exposures in a single model. OBJECTIVE: To 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. METHODS: We 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. RESULTS: In 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. CONCLUSIONS: The 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.