Bayesian networks for Risk Assessment and postoperative deficit prediction in intraoperative neurophysiology for brain surgery

Abstract

To this day there is no consensus regarding evidence of usefulness of Intraoperative Neurophysiological Monitoring (IONM). Randomized controlled trials have not been performed in the past mainly because of di culties in recruitment control subjects. In this study, we propose the use of Bayesian Networks to assess evidence in IONM. MethodsSingle center retrospective study from January 2020 to January 2022. Patients admitted for cranial neurosurgery with intraoperative neuromonitoring were enrolled. We built a Bayesian network with utility calculation using expert domain knowledge based on logistic regression as potential causal inference between events in surgery that could lead to central nervous system injury and postoperative neurological function. ResultsA total of 267 patients were included in the study: 198 (73.9%) underwent neuro-oncology surgery and 69 (26.1%) neurovascular surgery. 50.7% of patients were female while 49.3% were male. Using the Bayesian Network´s original state probabilities, we found that among patients who presented with a reversible signal change that was acted upon, 59% of patients would wake up with no new neurological de cits, 33% with a transitory de cit and 8% with a permanent de cit. If the signal change was permanent, in 16% of the patients the de cit would be transitory and in 51% it would be permanent. 33% of patients would wake up with no new postoperative de cit. Our network also shows that utility increases when corrective actions are taken to revert a signal change. ConclusionsBayesian Networks are an effective way to audit clinical practice within IONM. We have found that IONM warnings can serve to prevent neurological de cits in patients, especially when corrective surgical action is taken to attempt to revert signals changes back to baseline properties. We show that Bayesian Networks could be used as a tool to calculate the utility of conducting IONM, which could save costs in healthcare when performed.