Department of Neurosurgery, King's College Hospital NHS Foundation Trust, ISIN AI Committee Chair, London, England.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, England.
J Clin Monit Comput. 2024 Oct;38(5):1043-1055. doi: 10.1007/s10877-024-01159-w. Epub 2024 May 9.
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 difficulties in recruitment control subjects. In this study, we propose the use of Bayesian Networks to assess evidence in IONM.
Single 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.
A 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 deficits, 33% with a transitory deficit and 8% with a permanent deficit. If the signal change was permanent, in 16% of the patients the deficit would be transitory and in 51% it would be permanent. 33% of patients would wake up with no new postoperative deficit. Our network also shows that utility increases when corrective actions are taken to revert a signal change.
Bayesian Networks are an effective way to audit clinical practice within IONM. We have found that IONM warnings can serve to prevent neurological deficits 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 mathematical tool to calculate the utility of conducting IONM, which could save costs in healthcare when performed.
时至今日，关于术中神经生理监测（IONM）有用性的证据仍未达成共识。过去未进行随机对照试验，主要是因为难以招募对照受试者。在这项研究中，我们提出使用贝叶斯网络来评估 IONM 的证据。
这是一项 2020 年 1 月至 2022 年 1 月期间进行的单中心回顾性研究。纳入接受术中神经监测的颅神经外科患者。我们根据手术中可能导致中枢神经系统损伤和术后神经功能障碍的事件之间的潜在因果关系，使用基于逻辑回归的专家领域知识构建了一个具有效用计算的贝叶斯网络。
共有 267 例患者纳入研究：198 例（73.9%）接受神经肿瘤学手术，69 例（26.1%）接受神经血管手术。50.7%的患者为女性，49.3%为男性。使用贝叶斯网络的原始状态概率，我们发现在出现可处理的可逆信号变化的患者中，59%的患者醒来时无新的神经功能缺损，33%的患者出现短暂性缺损，8%的患者出现永久性缺损。如果信号变化是永久性的，那么在 16%的患者中，缺陷将是短暂的，在 51%的患者中，缺陷将是永久性的。33%的患者醒来时无新的术后缺陷。我们的网络还表明，当采取纠正措施使信号变化恢复正常时，效用会增加。
贝叶斯网络是审核 IONM 临床实践的有效方法。我们发现，IONM 警告可以防止患者出现神经功能缺损，尤其是当采取纠正性手术措施试图使信号变化恢复到基线特征时。我们表明，贝叶斯网络可以用作计算进行 IONM 的效用的数学工具，当在医疗保健中执行时，可以节省成本。
