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

Annals of Medicine, 2023 · DOI: https://doi.org/10.1080/07853890.2023.2232999 · Published: July 1, 2023

Simple Explanation

This study uses a special kind of computer model to predict how well patients with a specific spinal cord problem (DCM) will do after surgery. This model looks at many factors at once to give a personalized prediction. The model helps identify which factors are most important for predicting a good outcome after surgery. This can help doctors make better decisions about who should have surgery. The study found that a patient's sex, whether they have dementia, and their condition before surgery are key factors in predicting how well they will do after surgery for DCM.

Study Duration

Minimum follow-up of 12 months

Participants

59 patients who had undergone cervical posterior decompression for DCM

Evidence Level

Not specified

Key Findings

1
Preoperative JOA score, presence of a psychiatric disorder, and ASIA grade were identified as significant factors associated with the last JOS score.
2
Sex, dementia and PreJOA score were direct causal factors related to the last follow-up JOA (LastJOA) score.
3
Being female, having dementia, and having a low PreJOA score were significantly related to having a low LastJOA score.

Research Summary

The study developed a probabilistic graphical model (PGM) using Bayesian networks (BNs) to predict clinical outcomes in patients with degenerative cervical myelopathy (DCM) after posterior decompression surgery. The PGM identified sex, dementia, and preoperative JOA score as causal predictors of surgical outcome for DCM. The authors conclude that PGM may be a useful personalized medicine tool for predicting the outcome of patients with DCM.

Practical Implications

Personalized Medicine

PGM may be a useful tool for predicting the outcome of patients with DCM, aiding in personalized treatment plans.

Informed Decision-Making

The identified causal factors (sex, dementia, PreJOA) can help surgeons make more informed decisions about patient selection and surgical approach.

Risk Stratification

The Bayesian network structure can assist in predicting the probability of clinical outcomes for individual patients undergoing posterior decompressive surgery.

Study Limitations

1
The number of patients with a full dataset was too small to create a complete predictive model.
2
The heterogeneity of the patients enrolled in the present study may be a source of bias.
3
The imaging status such as severity of the T1 and T2 signal changes in the cervical cord on MRI was not considered in the present study.

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