Probabilistic Graphical Model using Bayesian Networks for Predicting Clinical Outcome after Posterior Decompression in Patients with Cervical Spondylotic Myelopathy

Abstract BackgroundThe objective of this study was to develop a probabilistic graphical model (PGM) to show the personalized prediction of clinical outcome in patients with cervical spondylotic myelopathy (CSM) with different clinical conditions after posterior decompression and to use the PGM to identify causal predictors of the outcome.MethodsWe included data from 59 patients who had undergone cervical posterior decompression for CSM. 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 disease, dementia, psychiatric disorders, arthritis, ossification of the posterior longitudinal ligament, cord signal change in T1-weighted images, postoperative kyphosis, and cord compression ratio. Statistical and Bayesian network analyses were used to create the PGM and identify predictive factors.ResultsIn multiple linear regression analysis, preoperative JOA score, presence of a psychiatric disorder, and ASIA grade were identified as significant factors associated with the last JOA score. Dementia, sex, preoperative JOA score, and gait impairment were causal factors in the PGM with 93.2% accuracy. Sex, dementia, and preoperative JOA score were direct causal factors related to the last JOA score. Being female, having dementia, and a low preoperative JOA score were significantly related to having a low last JOA score. The PGM showed that preoperative JOA score and sex did not affect the last JOA score in patients with dementia. The probability of having a high last JOA score was higher in men with a high preoperative JOA score than in women with the same preoperative state (74% vs. 2%, respectively).ConclusionsThe causal predictors of surgical outcome for CSM were sex, dementia, and preoperative JOA score. Use of the PGM with the Bayesian network may be useful personalized medicine tool for predicting the outcome for each patient with CSM.