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 underdosed intraoperatively. Inefficient management of surgical nociception may lead to more complex postoperative pain management and side effects such as postoperative cognitive dysfunction, particularly in elderly patients. We collected a comprehensive and multisensor prospective observational dataset focused on surgical nociception (101 surgeries, 18,582 min, and 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. We also demonstrate that the characterizing signature of nociception learned by our models resembles the known physiology of the response to pain. Our work represents an important step toward objective multisensor physiology-based markers of surgical nociception. These markers are derived from an in-depth characterization of nociception as measured during surgery itself rather than using other experimental models as surrogates for surgical nociception.

中文翻译：

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使用多传感器生理模型监测手术伤害感受


监测伤害感受，即即使在失去知觉期间与有害刺激相关的信息流通过神经系统，对于手术期间的适当麻醉护理至关重要。目前，这是通过眼睛跟踪心率和血压来完成的。客观监测患者的伤害感受状态仍然是一个挑战，导致术中药物经常过量或不足。手术伤害感受管理效率低下可能导致更复杂的术后疼痛管理和副作用，例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的多传感器前瞻性观察数据集，专注于手术伤害感受 （101 次手术、18,582 分钟和 49,878 次伤害感受刺激），包括手术期间发生的所有伤害性刺激和给药的注释。使用这个数据集，我们根据生理学和统计学上严格的心脏动作电位和汗腺活动的点过程表示开发了自主神经系统活动的指数。接下来，我们构建了高度可解释的监督和无监督模型，具有适当的归纳偏差，可以量化整个手术过程中的手术伤害感受。我们的模型比现有的伤害感受监测器更准确地跟踪伤害性刺激。我们还证明，我们的模型学习的伤害感受的特征特征类似于已知的对疼痛反应的生理学。我们的工作代表了朝着客观的基于多传感器生理学的手术伤害感受标志物迈出的重要一步。 这些标志物来自手术本身测量的伤害感受的深入表征，而不是使用其他实验模型作为手术伤害感受的替代物。