• Dong Ah Shin, Sun-Ho Lee, Sohee Oh, Changwon Yoo, Hee-Jin Yang, Ikchan Jeon, and Sung Bae Park.
• Department of Neurosurgery, Yonsei University College of Medicine, Seoul, South Korea.
• Ann. Med. 2023 Dec 1; 55 (1): 22329992232999.

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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