Multimodal Physiological Indices During Surgery Under Anesthesia

Monitoring nociception, the flow of information associated with harmful stimuli through the nervous system even during unconsciousness, is critical for proper anesthesia care during surgery. Currently, this is done by tracking heart rate and blood pressure by eye. Monitoring objectively a patient’s nociceptive state remains a challenge, causing drugs to often be over- or under-dosed intraoperatively. Inefficient management of surgical nociception may lead to more complex post-operative pain management and side effects such as post-operative cognitive dysfunction, particularly in elderly patients. We collected a comprehensive and multi-sensor prospective observational dataset focused on surgical nociception (101 surgeries, 18,582 minutes, 49,878 nociceptive stimuli), including annotations of all nociceptive stimuli occurring during surgery and medications administered. Using this dataset, we developed indices of autonomic nervous system activity based on physiologically and statistically rigorous point process representations of cardiac action potentials and sweat gland activity. Next, we constructed highly interpretable supervised and unsupervised models with appropriate inductive biases that quantify surgical nociception throughout surgery. Our models track nociceptive stimuli more accurately than existing nociception monitors.

监测与有害刺激相关的信息流，即痛觉信息在神经系统中的传递，即使在无意识状态下，对于确保手术期间麻醉护理的恰当性至关重要。目前，这一过程是通过监测心率与血压来实现的。在手术过程中客观地监测患者的痛觉状态仍然是一项挑战，这往往导致术中药物剂量过多或过少。手术期间痛觉管理的不当可能会引发更为复杂的术后疼痛管理问题及副作用，如术后认知功能障碍，尤其是在老年患者中。我们收集了一个全面且多传感器的前瞻性观察数据集，专注于手术痛觉（101例手术，18,582分钟，49,878个痛觉刺激），包括手术期间发生的所有痛觉刺激及所使用的药物标注。利用此数据集，我们基于生理与统计上严谨的点过程表示，构建了自主神经系统活动的指标，这些指标以心脏动作电位和汗腺活动为基础。随后，我们构建了具有适当归纳偏好的高可解释性监督和未监督模型，以量化整个手术过程中的手术痛觉。我们的模型在跟踪痛觉刺激方面的准确性高于现有的痛觉监测设备。