Machine-Learned Nociception Index for Perioperative Opioid Dosing
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Solution Overview
Problem
Current anesthesia practices lack reliable methods for objectively assessing nociception in anesthetized patients, leading to variability in opioid dosing and associated negative consequences such as delayed recovery, hemodynamic instability, and increased pain levels.
Innovation Solution
A device and method using machine learning to compute a nociception index from multiple physiological parameters, providing a treatment recommendation or automatic analgesic administration based on thresholds and clinical concerns to maintain optimal nociceptive-anti-nociceptive balance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If surrogate measures (heart rate, blood pressure, tearing, pupillary response, sweating) are used to assess nociception, then the assessment can be performed non-invasively, but the reliability is uncertain due to influence by other factors such as hypoxia, hypercarbia, hypo- or hyperthermia, and medications
Solution Approach 1:
The patent segments the assessment of nociception from other physiological functions by using a dedicated nociception index calculation that processes multiple physiological parameters separately and combines them through a specialized algorithm. This segmentation allows the nociception assessment to be isolated from confounding factors like hypoxia, hypercarbia, and medication effects that influence other surrogate measures.
Solution Approach 2:
The patent introduces a nociception index as an intermediary parameter that mediates between raw physiological measurements and clinical nociception assessment. This index is calculated by processing multiple physiological parameters (heart rate, blood pressure, respiratory rate, temperature, oxygen saturation, carbon dioxide saturation) through a specialized algorithm that weights and combines them to provide a reliable nociception-specific assessment that is not directly influenced by other physiological states.
2Measurement precision
If multiple physiological parameters are monitored and processed through machine learning algorithms to compute a nociception index, then the precision of nociception assessment is improved, but the device complexity increases
Solution Approach 1:
The patent applies multi-functionality by using a single integrated monitoring system that simultaneously tracks multiple physiological parameters (heart rate, blood pressure, respiratory rate, temperature, oxygen saturation, carbon dioxide saturation) and processes them through a unified machine learning algorithm to generate the nociception index. This universal approach consolidates what would otherwise require separate specialized monitoring systems into one comprehensive device.
Solution Approach 2:
The patent transforms raw physiological parameters into a standardized nociception index through parameter changes and computational processing. The machine learning algorithm processes multiple input parameters and converts them into a single interpretable output metric that directly represents nociception level, simplifying the complexity of multi-parameter monitoring into a unified measurement scale.
3Adaptability or versatility
If opioid dosing is adjusted based on uncertain surrogate measures, then the dosing can be modified in response to physiological changes, but the variability in dosing increases leading to excessive or insufficient opioid doses
Solution Approach 1:
The patent implements feedback by continuously monitoring physiological parameters and using the computed nociception index to dynamically adjust opioid dosing recommendations. The system provides real-time feedback to clinicians about the patient's nociception level, enabling informed decisions about opioid administration that are based on objective, reliable data rather than uncertain surrogate measures.
Solution Approach 2:
The patent applies preliminary action by calculating the nociception index in advance of opioid dosing decisions, allowing clinicians to have a reliable baseline assessment of nociception before adjusting opioid doses. This preliminary computation provides a solid foundation for subsequent dosing adjustments, reducing variability and improving reliability.
Data Source
AI summary
Method, device, system and associated processing logic for providing optimal perioperative nociception management to anesthetized patient, including monitoring a plurality of nociception-related physiological parameters of the patient using at least two non-invasive physiological sensors; combining the plurality of nociception-related physiological parameters into a nociception index value indicative of a level of nociception of the patient, by applying a machine learning algorithm on the plurality of nociception-related physiological parameters and/or features derived therefrom; comparing the nociception index value to a first threshold value indicative of an upper limit of nociceptive-anti-nociceptive balance (NANB) and to a second threshold value indicative of a lower limit of NANB; and providing a treatment or treatment recommendation based on the comparison and on a clinical concern.