System and method for monitoring nociceptive sensation and pain loss during administration of general anesthesia

Abstract

Systems and methods are provided for patient monitoring and / or treatment. The system may include one or more sensors configured to measure electroencephalogram (EEG) and skin electrical signals of a patient subjected to at least one anesthetic and at least one analgesic during a surgical medical procedure. The system may also include a processor operably coupled to the one or more sensors. The processor is configured to receive the electroencephalogram and skin electrical signals, and to generate a report indicative of a real-time injury sensory status of the patient during a surgical medical procedure when the patient is subjected to at least one anesthetic using the electroencephalogram or skin electrical signals, and to generate a pain management plan based on a desired post-surgical outcome.

Description

Cross-references

[0001] This application is based on, claims priority to, U.S. Provisional Patent Application Serial No. 63 / 320,535 filed on March 16, 2022, and is incorporated herein by reference in its entirety for all purposes. Technical Field

[0002] Generally, the present disclosure relates to the field of medicine, and more particularly, to the field of medical treatment planning and / or patient monitoring. Background Art

[0003] Tens of thousands of patients die from opioid-related overdoses each year, making opioid-related overdoses one of the largest ongoing public health problems in the United States. Surgery is a major risk factor for developing opioid dependence, as the majority of surgical patients are prescribed opioids to manage their postoperative pain. Postoperative pain is strongly influenced by the degree of intraoperative nociception control during surgery and general anesthesia. Therefore, effective nociception control during general anesthesia and surgery is critical to reducing postoperative pain and postoperative opioid requirements.

[0004] Surgical nociception is often treated by administering opioid analgesics, but these drugs must be administered with caution. Excessive opioid administration can induce respiratory depression and excessive sedation, increase postoperative hospital stays, and induce central sensitization, which can increase downstream opioid requirements. On the other hand, ineffective control of surgical nociception can lead to increased postoperative pain, which can increase postoperative opioid requirements. Currently, anesthesiologists rely on changes in heart rate (HR) and blood pressure (BP) to indicate nociception and titrate opioids and other medications to minimize these changes. Unfortunately, HR and BP are affected by many intraoperative factors, such as blood loss, anesthetics, and antihypertensive medications, making them unreliable indicators of nociception. Therefore, there is a clear need for improved methods to monitor surgical nociception during general anesthesia. Fortunately, HR and BP are not the only physiological variables that can be used to monitor and track nociception. Electrodermal activity (EDA), also known as skin conductance response (SCR), changes in response to noxious stimuli through autonomic mechanisms. Fluctuations in the electroencephalogram (EEG) can also track arousal and nociception. Recently, our laboratory has also identified a novel, robust, and specific EEG drug effect profile for opioids that can be used to monitor opioid effects distinct from other anesthetics.

[0005] Up to 10% of surgical patients develop opioid dependence following surgery. Poorly controlled acute postoperative pain can lead to chronic pain, which may require long-term use of opioids to control. Opioids are the first-line treatment for acute postoperative pain but are highly addictive. Anesthesiologists and ICU physicians and nurses do not have any tools to help them monitor patients’ nociception during surgery and / or during ICU care, nor do they have any tools to help them monitor the efficacy of the opioid analgesics they administer to treat nociception, leading to oversedation and suboptimal postoperative pain outcomes. Summary of the invention

[0006] The present disclosure solves the above shortcomings by describing systems and methods for monitoring patient parameters during medical procedures (such as surgery), and / or patient treatment plans to reduce the possibility of undesirable risks associated with some operations or post-operative care (such as when administering opioids). In a non-limiting example, systems and methods are provided for patient planning and / or monitoring to better understand nociception and develop plans for managing postoperative medication use or other treatments (such as the administration of opioids). In a non-limiting example, a system can be used that is configured to obtain patient information during general anesthesia and / or sedation and determine a pharmacodynamic image in the information that is highly correlated with opioid concentrations and can be used to titrate opioids (independent of sedative hypnotic drugs). Such pharmacodynamic images can also be used with other physiological features to monitor nociception and analgesia during anesthesia and intensive care. These features can be combined into a monitoring index that can be used to improve postoperative pain and opioid requirements. A surgical nociception monitor can be provided to reduce postoperative opioid requirements. In another non-limiting example, integrated measurements of nociceptive control based on neurophysiological markers of arousal and nociception (using EEG as a non-limiting example) and autonomic markers (using EDA as a non-limiting example) provide the anesthesiologist with a monitor that enables the clinician to develop a surgical and / or post-operative plan that minimizes post-operative pain and post-operative opioid requirements.

[0007] In one aspect of the present disclosure, a surgical patient monitoring system is described, the system comprising: one or more sensors configured to measure electroencephalogram (EEG) and galvanic skin signals of a patient subjected to at least one anesthetic and at least one analgesic during a surgical medical procedure; and a processor operably coupled to the one or more sensors, the processor configured to: receive the EEG and galvanic skin signals; use the EEG or galvanic skin signals to monitor the patient's real-time nociceptive state during the surgical medical procedure; and use at least one nociceptive state of the patient during the surgical medical procedure or the at least one analgesic administered to the patient for the surgical medical procedure to generate a post-operative pain management plan.

[0008] In another aspect of the present disclosure, a surgical patient monitoring system is described, the system comprising: one or more sensors configured to measure electroencephalogram (EEG) and skin conduction signals of a patient subjected to at least one anesthetic and at least one analgesic during a surgical medical procedure; and a processor operably coupled to the one or more sensors, the processor configured to: receive the EEG and skin conduction signals; and use the EEG or skin conduction signals to generate a report indicating the patient's real-time nociceptive state during the surgical medical procedure when the patient is subjected to the at least one anesthetic. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Some embodiments of the present disclosure are described in this application with reference to the accompanying drawings. This specification, together with the accompanying drawings, makes it clear to those skilled in the art how some embodiments of the present disclosure can be implemented. The accompanying drawings are for the purpose of exemplary discussion and do not attempt to show the structural details of the embodiments in more details than necessary for a basic understanding of the teachings of the present disclosure.

[0010] Figure 1 A schematic diagram of a closed-loop monitoring and control system according to the present disclosure is shown.

[0011] Figure 2A Predicted effect site concentrations (ESCs) for a representative subject are shown.

[0012] Figure 2B The exposure to fentanyl corresponding to the changes in the spectrogram is shown.

[0013] Figure 2C The changes in the power spectrum for a representative subject corresponding to fentanyl exposure are shown, with significant increases in the theta (4-8 Hz) band and the slow / delta (0-4 Hz) band. The increase in theta power corresponds to increasing fentanyl concentrations.

[0014] Figure 2DA mixed effects model constructed between subjects with 95% confidence intervals is shown to further describe the association between theta power and changes in fentanyl concentrations.

[0015] Figure 3A Theta oscillations (4-8 Hz) that appear to be unique to fentanyl are shown to occur during loss of consciousness. The theta band signal appears to be responsive to noxious stimuli as it is significantly reduced during intubation.

[0016] Figure 3B Significant changes in the spectrogram occurred during loss of consciousness at 25 minutes and during intubation at 28 minutes.

[0017] Figure 4A Fentanyl concentrations estimated using the Pk / Pd model are shown.

[0018] Figure 4B It was shown that theta band power was closely correlated with reaction time throughout the protocol.

[0019] Figure 4C The estimated prediction error of the θ power model Pk / Pd model and the combined model is compared by K-fold cross validation.

[0020] Figure 5A The traditional spectral analysis and state-space method showed the EEG pharmacodynamic profile in the slow, theta, and alpha bands during general anesthesia with sevoflurane and fentanyl.

[0021] Figure 5B Figure 3 AIC analysis using the 2-oscillator and 3-oscillator state-space models showed that the 3-oscillator model consistently outperformed the 2-oscillator model, indicating that the fentanyl theta component can be detected and extracted in the context of sevoflurane-induced slow and alpha oscillations.

[0022] Fig. 6A Shown is an EEG spectrogram during surgery under general anesthesia, annotated to indicate the time points at which propofol was administered and lidocaine infusion was stopped.

[0023] Figure 6B The EDA SCR was significantly reduced after the propofol bolus, which is consistent with the presence of an antinociceptive agent.

[0024] Figure 6C showed that after surgery, the lidocaine infusion intended to provide analgesia was stopped; the patient remained unconscious, but the EDASCR increased significantly, indicating increased nociception.

[0025] Fig.6DSpecific periods of potentially noxious stimulation were assessed by event recordings obtained from the patient's anesthesia record. Periods with noxious stimulation showed an increase in SCRs compared to baseline. This pattern was consistent during maintenance of general anesthesia, although SCR levels were reduced, possibly due to the presence of anesthetic agents.

[0026] Fig. 7A is an EEG spectrogram showing an example of a patient having consistent alpha band oscillations associated with unconsciousness (left) versus disrupted alpha oscillations associated with nociception / arousal (right).

[0027] Figure 7B It is shown that alpha-band oscillations of patient EEG recordings can be extracted using the previously described state-space model, components of which can be used to additionally explore disruptions during various stimulations, including the amplitude or envelope as shown, or the frequency or phase of the oscillations.

[0028] Fig. 8A showed that alpha-band power appeared to decrease as SCR increased during lumbar puncture.

[0029] Figure 8B is a spectrum plot showing the alpha power fluctuations aligned with the SCR response.

[0030] Figure 8C Estimated cross-correlations are shown, which revealed similar associations between alpha power and SCR across multiple noxious events and subjects (mean = 0.5135, SD = 0.0558).

[0031] Fig. 9 A is a summary of forward-looking data collection for model, algorithm and index development.

[0032] Fig.10 Shown is a schematic diagram comparing a classical approach to developing operating room monitors with an approach according to the present disclosure for developing algorithms to optimize and calibrate intraoperative monitoring variables with respect to post-operative outcomes.

[0033] Fig.11A Mean fentanyl concentrations during surgery for three representative patients are shown.

[0034] Fig. 11B Median theta EEG power during surgery for the same three representative patients is shown.

[0035] Fig. 12A Shown are the total amount of opioids required by the same three representative patients during the first 24 hours after surgery.

[0036] Fig. 12B Shown are the maximum pain experienced by the same three representative patients during the first 24 hours after surgery.

[0037] Fig.13 Median skin conductance values ​​during surgery for the same three representative patients are shown.

[0038] Fig.14A Fluctuations in EEG alpha oscillation amplitude during surgery are shown for the same three representative patients.

[0039] Fig. 14B Fluctuations in the instantaneous frequency of EEG alpha oscillations during surgery are shown for the same three representative patients. DETAILED DESCRIPTION

[0040] Described below are systems and methods for monitoring nociception and analgesia using a combination of physiological measurements including electroencephalogram (EEG) and electrodermal activity (EDA). In a non-limiting example, separate sensors are used to obtain EEG and EDA data. In one embodiment, one or more EEG sensors are applied to the patient's scalp. Alternatively, one or more EEG sensors are applied to the patient's forehead. In a non-limiting example, one or more EDA sensors are applied to the patient's skin surface, such as the palm of the hand. In an embodiment, one or more EDA sensors are placed on the patient's forehead. In another embodiment, a single sensor can be used to obtain EEG and EDA measurements. In an embodiment, a single sensor measuring EEG and EDA is placed on the patient's forehead.

[0041] The embodiments employ novel methods of extracting information from these signals that is relevant to the patient's autonomic response to nociception, their brain response to nociception, and the patient's pharmacological response to opioid analgesics.

[0042] Commercial EEG-based anesthesia monitors have been on the market for decades. However, existing devices focus on monitoring the patient's level of consciousness. However, monitoring nociception (i.e., experiencing and responding physiologically to noxious stimuli) is something that the prior art cannot do. Some benefits of the described aspects of the present invention include 1) using and processing a novel EEG pharmacodynamic image of opioids, 2) using and processing a novel sensor for electrical skin activity, 3) using and processing a novel pharmacodynamic image of the brain's response to nociception, 4) integrating all of these features into a quantitative system to predict postoperative outcomes of interest, including postoperative pain and opioid consumption, and 5) calibrating this integrated monitoring feature to minimize postoperative pain and opioid consumption.

[0043] We have conducted three studies supporting these concepts: 1) a prospective laboratory study in n = 25 human patients designed to detect the EEG pharmacodynamic profile of opioids; 2) a retrospective analysis of clinical EEG data screening thousands of surgeries under general anesthesia; and 3) a retrospective cohort study including tens of thousands of cases that examined the relationship between patient baseline variables, intraoperative opioid administration, and postoperative pain and opioid outcomes.

[0044] refer to Figure 1 , illustrates an exemplary system 110 according to the present invention. System 110 includes a patient monitoring device 112 (such as a physiological monitoring device), which may include an electroencephalogram (EEG) sensor array. However, it is contemplated that the patient monitoring device 112 may also include a mechanism for monitoring skin electrodermal activity (EDA), for example, to measure arousal to external stimuli, or other monitoring systems (such as a cardiovascular monitor, including an electrocardiogram and a blood pressure monitor). In a non-limiting example, the EEG electrodes and the EDA electrodes are integrated into the same sensor of the patient monitoring device 112. Alternatively, the EEG sensor array and the EDA sensor array may be separate. In a preferred embodiment, the patient monitoring device 112 is placed on the forehead surface of the patient. In an alternative embodiment, the EEG sensor array may be placed on the scalp or forehead, and the EDA sensor array may be placed on the palm of the patient.

[0045] The patient monitoring device 112 is connected via a cable 114 to communicate with a monitoring system 116. In addition, the cable 114 and similar connections can be replaced by wireless connections between components. As shown, the monitoring system 116 can be additionally connected to a dedicated analysis system 118. In addition, the monitoring system 116 and the analysis system 118 can also be integrated.

[0046] In a non-limiting example, the monitoring system 116 can be configured to receive raw signals obtained by the combined EEG sensor and EDA sensor array, assemble and display the raw signals as EEG waveforms, EEG spectrograms, and / or EDA skin conductance response (SCR) data. Thus, the analysis system 118 can receive the EEG waveform from the monitoring system 116 and, as will be described, analyze the EEG waveform and drug effect profile therein based on a selected anesthetic compound, determine the patient's state based on the analyzed EEG waveform and drug effect profile, and generate a report, for example, as a printed report, or, preferably, display the drug effect profile information and the determined state in real time. However, it is also contemplated that the functions of the monitoring system 116 and the analysis system 118 can be combined into a common system.

[0047] In a non-limiting example, the analysis system 118 can additionally generate a post-operative pain management plan based on signals received from the patient monitoring device. As will be described in detail below, the effect site concentration (ESC) pharmacodynamic profile of a particular analgesic (e.g., an opioid) can provide information about the patient's nociceptive state. In addition, changes in nociceptive state are detected by changes in EDA due to neurally mediated effects on sweat gland permeability (which are observed as changes in the skin's resistance to a small electrical current or voltage differences between different parts of the skin).

[0048] Alternatively, in a non-limiting example, an intraoperative plan may be generated by a system that is calibrated to target and optimize postoperative outcomes, including but not limited to postoperative pain, opioid requirements, cognitive recovery time, and respiratory depression. The system may be configured to characterize the relationship between intraoperative opioid administration and postoperative pain and opioid requirements. For example, a relationship may be represented as increased intraoperative opioid administration and decreased postoperative pain, decreased opioid requirements, decreased respiratory depression, and / or decreased length of stay.

[0049] In a non-limiting example, Table 1 provides a non-exhaustive list of postoperative pain management outcomes, which may include maximum pain score during the postanesthesia monitoring unit (PACU), cumulative opioid doses administered during the PACU, frequency of uncontrolled pain at 24 hours, examples of new chronic pain diagnoses between 3 months and 1 year, total opioid use at 24 hours and during hospitalization, inpatient opioid prescriptions at 30 days, 90 days, and 180 days after surgery, frequency of new persistent opioid use at 90 days and 180 days, maximum pain score in the first 24 hours and during hospitalization, incidence of opioid-related complications in the PACU (postoperative nausea and vomiting (PONV), sedation, and respiratory depression), length of stay (LOS) in the PACU and hospitalization, 30-day readmission, and 30-day mortality.

[0050] The expected effect of adding 100 μg of fentanyl or 500 μg of hydromorphone to the observed intraoperative exposure per patient in our study population is shown in Table 1. These analyses showed that an increase of 100 μg of intraoperative fentanyl corresponded to a mean decrease of 0.26 points in the maximum pain score in the PACU, and an increase of 500 μg of intraoperative hydromorphone resulted in a mean decrease of 0.12 points in the maximum pain score in the PACU.

[0051] For postoperative opioid administration, a 100-microgram increase in intraoperative fentanyl corresponded to a mean decrease in postoperative opioid administration of 0.44 morphine milligram equivalents (MME) (-16.0%) in the PACU, a mean decrease of 3.2 MME (-29.3%) at 24 hours, and a mean decrease of 6.6 MME (-14.5%) during hospitalization. Meanwhile, a 500-microgram increase in intraoperative hydromorphone corresponded to a mean decrease in opioid administration of 0.26 MME (-9.3%) in the PACU, a mean decrease of 1.9 MME (-17.7%) at 24 hours, and a mean decrease of 1 MME (-2.2%) during hospitalization.

[0052] A 100-microgram increase in intraoperative fentanyl was equivalent to an 8.2-hour decrease in hospital stay (-12.5%), while a 500-microgram increase in intraoperative hydromorphone was equivalent to an increase in hospital stay of 4.2 hours (+6.3%). For opioid prescriptions, a 100-microgram increase in intraoperative fentanyl was equivalent to a 20.7-per-1,000 decrease (-8.3%) after 30 days, a 22.6-per-1,000 decrease (-8.6%) after 90 days, a 23.1-per-1,000 decrease (-8.3%) after 180 days, and a 16.9-per-1,000 decrease in persistent opioid use (-10.2%). A 500-microgram increase in intraoperative hydromorphone was equivalent to 11.3 additional opioid prescriptions per 1,000 cases after 30 days (+4.5%), 11.3 additional opioid prescriptions per 1,000 cases after 90 days (+4.3%), 11.2 additional opioid prescriptions per 1,000 cases after 180 days (+4.0%), and 4.3 additional cases of continued use per 1,000 cases (+2.6%). Table 1. Counterfactual predictions. For non-pain score outcomes, expected changes and 95% confidence intervals are also reported as percentages. PACU: postanesthesia monitoring unit; MME: morphine milligram equivalent; PONV: postoperative nausea and vomiting.

[0053] In a non-limiting example, the system can also be configured to select or identify at least one of the postoperative pain and opioid requirement variables with the highest correlation. The system can be further calibrated by limiting the postoperative pain and opioid requirement results to an acceptable range. In addition, the system can be refined by subspace and a series of modifiable physiological variables that result in acceptable postoperative outcomes.

[0054] As will be described in detail, the system 110 may also include a drug delivery system 120. The drug delivery system 120 may be coupled to the analysis system 118 and the monitoring system 116 such that the system 110 forms a closed-loop monitoring and control system. As will be described, such a closed-loop monitoring and control system according to the present invention is capable of a wide range of operations, but includes a user interface 122 to allow a user to configure the closed-loop monitoring and control system, receive feedback from the closed-loop monitoring and control system, and, if necessary, reconfigure and / or rewrite the closed-loop monitoring and control system.

[0055] In a non-limiting example, the drug delivery system 120 may include a number of specific subsystems to administer any of anesthetics, analgesics (opioids and non-opioids), vasopressors, antihypertensives, opioid antagonists, analgesics, and anesthetic adjuvants, or a combination thereof. It may also account for CYP3 A4 inducers and inhibitors that may alter the dose response characteristics of any of the above-mentioned therapeutic drugs.

[0056] In non-limiting examples, anesthetic agents may include nitrous oxide, propofol, desflurane, isoflurane, and sevoflurane.

[0057] In non-limiting examples, opioid analgesics can include: fentanyl analgesics, hydromorphone, morphine, methadone, oxycodone, pethidine, remifentanil, codeine, hydrocodone, oxymorphone, sufentanil, alfentanil, nalbuphine, buprenorphine, butorphanol, levomorphan, pentazocine, tramadol, tapentadol, dihydrocodeine, opium, and camphor tincture.

[0058] In non-limiting examples, non-opioid analgesics may include: aspirin, celecoxib, diclofenac, diflunisal, etodolac, fenoprofen, flurbiprofen, ibuprofen, indomethacin, ketorolac, ketoprofen, magnesium salicylate, meclofenamate, mefenamic acid, meloxicam, nasimetone, naproxen, oxaprozin, piroxicam, salsalate, sulindac, tolmetin, acetaminophen, gabapentin, pregabalin, carbamazepine, oxcarbazepine, valproic acid, topiramate. ester, dexamethasone, prednisone, amitriptyline, nortriptyline, doxepin, clomipramine, duloxetine, venlafaxine, milnacipran, desvenlafaxine, lamotrigine, cyclobenzaprine, methocarbamol, bacenofin, tizanidine, clonidine, propranolol, verapamil, almotriptan, eletriptan, fluvatriptan, naratriptan, rizatriptan, sumatriptan, zolmitriptan, ketamine, lidocaine, pamidronate, zoledronic acid, denosumab, capsaicin, diclofenac.

[0059] In non-limiting examples, vasopressors may include: dopamine (100 μg / kg / min), ephedrine, epinephrine (1 μg / kg / min), norepinephrine (1 μg / kg / min), phenylephrine (10 μg / kg / min), and vasopressin (0.4 μg / kg / min).

[0060] In non-limiting examples, CYP3A4 inducers can include: apalutamide, carbamazepine, enzalutamide, fosphenytoin, lumacator, lumacator-ivacator, mitotane, phenobarbital, phenytoin, primidone, rifampicin, rifamycin, bexarotene, bosentan, sambacot, dabrafenib, dexamethasone, efavirenz, elagolix, eslicarbazepine, etravirine, lorlatinib, modafinil, nafcillin, pilsidatinib, rifabutin, rifapentine, St. John's wort, nevirapine and griseofulvin.

[0061] In non-limiting examples, CYP3A4 inhibitors can include: atazanavir, ceritinib, clarithromycin, cobicistat, darunavir, idelalinib, indinavir, itraconazole, ketoconazole, lonafarnib, lopinavir, mifepristone, nefazodone, nelfinavir, ombipari-palipari-ritonavir, ombipari-palipari-ritonavir-dasabuvir, posaconazole, ritonavir, saquinavir, tolbutamide, tadalafil ... catinib, voriconazole, amiodarone, aprepitant, berotinostat, cimetidine, conivaptan, crizotinib, cyclosporine, diltiazem, duvelithide, dronedarone, erythromycin, flutinib, fluconazole, fosamprenavir, fosaprepitant, fonetopitant-palonosetron, imatinib, isavuconazole, isavuconazonium sulfate, lefamulin, letomovir, netupitant, nilotinib, ribociclib, verapamil, and metronidazole.

[0062] In non-limiting examples, antihypertensive drugs may include: esmolol, metoprolol, propranolol, labetalol, nicardipine, clevidipine, hydralazine, nitroglycerin, nitroglycerin esters, nitroprusside, fenoldopam, verapamil, and diltiazem.

[0063] In non-limiting examples, opioid antagonists may include naloxone, naltrexone, methylnaltrexone, and Alyimopam.

[0064] In non-limiting examples, analgesics and anesthetic adjuncts may include diclofenac, ibuprofen, indomethacin, ketorolac, meloxicam, acetaminophen, lidocaine, ketamine, dexmedetomidine, esmolol, magnesium (sulfate), and dexamethasone.

[0065] In a non-limiting example, Figures 2A-2DAdministration of fentanyl and EEG pharmacodynamic profiles are shown. Subjects (n=25) were administered up to 3 boluses at 2-min intervals after a baseline period, and concentration levels were calculated by pharmacokinetic / pharmacodynamic (Pk / Pd) modeling. Changes in EEG signals and ESCs of fentanyl were shown. Figure 2B Pharmacokinetic / pharmacodynamic (PK / PD) modeling was used to estimate the fentanyl effect site concentration (ESC). Figure 2A ESCs from a representative subject are shown in . EEG can be analyzed using multi-taper spectral analysis. Figure 2B The spectrogram of a representative subject in illustrates how EEG power in the theta (4 to 8 Hz) and slow / delta (0 to 4 Hz) bands increases with increasing fentanyl concentration. These same oscillations can also be understood in the spectrum, as Figure 2C The time points are shown in the figure. Figure 2D A strong association between EEG and fentanyl concentration in all n = 25 subjects [slope: 0.55 (CI: 0.25 to 0.80), marginal R2 = 0.151, conditional R2 = 0.744] was shown in a linear mixed effects model with fentanyl concentration as a function of theta power. Figure 2D As shown in , for fentanyl effect site concentrations ranging between 0 and about 15 ng / mL, the theta power range can be between about -10 and 15 dB.

[0066] Additional details of the systems and methods are described in the following examples. Example

[0067] 1.a. A novel opioid-specific EEG pharmacodynamic profile that tracks patient status and can be detected during general anesthesia (multiple agents). Retrospective EEG data were collected from n=6 patients who received fentanyl-induced general anesthesia during cardiac surgery and a novel fentanyl-induced EEG pharmacodynamic profile was discovered. Figure 3A-3B The spectral characteristics unique to fentanyl sedation and unconsciousness were revealed. More specifically, Figure 3A-3B An EEG spectrogram from a representative subject is shown, which shows an increase in slow band (0.1-1 Hz), delta band (1-4 Hz), and theta band power (4-8 Hz) consistent with an anesthetized state. The theta band signal appears to be a unique oscillation that is unique to fentanyl.

[0068] EEG data were recorded prospectively from n = 26 patients who received fentanyl prior to induction of general anesthesia while performing a structured auditory behavioral response task. After a 5-minute baseline period, patients received two or three boluses of 2 μg / kg ideal body weight fentanyl (eg, Figure 2A ). Figures 4A-4Cis a visualization of a model predicting behavioral response times using fentanyl Pk / Pd concentrations and the theta power model. Figure 4A In the study, the patient's response time to repeated auditory stimuli is used to measure the patient's level of consciousness. Figure 4B It was shown how the EEG theta power efficacy profile tracked the behavioral response times of the patients. It was investigated how the EEG theta efficacy profile might perform as a predictor of behavioral responses when compared and combined with predicted fentanyl Pk / Pd effect site concentrations ( Figure 4C ). Linear regression models were fitted using fentanyl (predicted effect site) concentration, EEGθ concentration, or a combination of both fentanyl and EEGθ, and model mean squared prediction errors (MSEs) were characterized using k-fold cross validation, with one patient omitted from each cross-validation run. The MSEs for the EEGθ model and the combined model were 4% and 14% lower than the fentanyl effect site concentration model, respectively. The combined model using all data had an MSE of P<<0.001 using a generalized likelihood ratio test. These data suggest that EEGθ power, alone or in combination with medication information, is a strong predictor of opioid-induced patient states, superior to predicted Pk / Pd effect site concentrations alone. EEGθ power, again alone or in combination with medication information, can provide information about an individual patient's real-time, personalized drug response to opioid analgesics.

[0069] In a non-limiting example, it was investigated whether this fentanyl-induced EEG theta drug effect profile could be extracted during general anesthesia (multiple agents) when other drugs such as propofol or sevoflurane were administered to maintain unconsciousness. In particular, propofol and sevoflurane induce large slow and alpha oscillations, which may mask nearby fentanyl theta oscillations. A model comparison analysis was performed to determine whether two (slow, alpha) or three (slow, theta, alpha) oscillations were present in the data. A class of "state-space oscillator" models were used, with two or three oscillators and an AIC to compare models. In a non-limiting example, the state-space oscillator models provided by Matsuda and Komaki (2017) and Beck, He, Gutierrez, and Purdon (2022) can be used. Figure 5A The EEG spectra of a representative subject during general anesthesia with sevoflurane and fentanyl are shown using traditional spectral analysis and state-space methods. All methods clearly show peaks in the slow, theta, and alpha bands. Figure 5B The results show that the 2-oscillator state-space model and the 3-oscillator state-space model ( Figure 5B) AIC analysis of n = 30 subjects receiving sevoflurane and fentanyl. This suggests that the fentanyl theta component can be detected and extracted in the context of sevoflurane-induced slow and alpha oscillations. In other retrospective analyses (not shown), similar theta oscillations were found when remifentanil was combined with propofol. These data support the ability to use opioid EEG pharmacodynamic profiles as markers of nociception control and opioid efficacy during general anesthesia or when multiple drugs are administered in addition to opioids.

[0070] 1.b. Electrodermal activity recorded from forehead sensors simultaneously with EEG tracks surgical nociception. EDA tracks autonomic changes induced by noxious or affective stimuli. Typically, EDA is recorded from the palm surface, which has a high density of sweat glands. However, the forehead also has a high density of sweat glands comparable to the palm, and EDA can be measured there at the same time as the EEG. This can be accomplished by any number of methods well known in the art, including, for example, applying a known current to the EEG electrodes, measuring the resulting voltage change, and inferring skin conductance. Figures 6A-6D is the observation of skin conductance responses (SCRs) during nociceptive events during surgery. That is, during general anesthesia, EDA information is measured from a forehead sensor simultaneously with the EEG, such as Figures 6A-6C as shown in . Figure 6B-6C Representative forehead EDA data for a single subject who received general anesthesia during surgery are shown. Figure 6B In the study, after induction of general anesthesia with propofol, the EDA skin conductance response (SCR) decreased significantly and remained below approximately 5 microsiemens thereafter. Figure 6C During the operation, the lidocaine infusion intended to provide analgesia was discontinued; the patient remained unconscious, but the EDASCR increased significantly, rising above the approximately 5 microsiemens maintained during surgery and general anesthesia to levels as high as approximately 35 microsiemens, indicating increased nociception. Fig.6D In the present study, EDA levels were compared before, during, and after surgery in three representative subjects. During surgery, suspected noxious events (e.g., intubation, first incision, active surgery) showed significantly greater SCRs than non-noxious periods (e.g., before the start of active surgery). EDA decreased during general anesthesia but increased after emergence. These preliminary data show that it is possible to simultaneously record EDA and EEG from the forehead and confirm that it is possible to track surgical noxious sensations with changes in EDA using a combined sensor.

[0071] In a non-limiting example, simultaneous monitoring of EEG and EDA during surgery can inform intraoperative fentanyl dose titration. Figure 6B The decrease of SCR after induction of general anesthesia with propofol Figure 6CAn increase in SCR following lidocaine infusion may indicate an upper limit for fentanyl titration. For example, a conductance below a predetermined value, such as below 5 microSiemens, may indicate an optimal intraoperative fentanyl dose to minimize intraoperative nociception and postoperative pain. Alternatively, an optimal fentanyl dose may correspond to a range of conductance values.

[0072] 1.c. Fluctuations in the amplitude of alpha oscillations track arousal and are associated with EDA; a new method to precisely extract alpha fluctuations. Anesthesia-induced changes in the power and amplitude of the frontal alpha band (8-12 Hz) are known to vary with arousal: alpha power increases with anesthetic dose from sedation to unconsciousness and decreases during arousal and / or noxious stimulation.

[0073] Figure 7A-7B The extraction of alpha oscillations and alpha amplitude in a surgical case during general anesthesia is shown. Thus, changes in alpha power can be used to track arousal associated with nociception. Fig. 7A shows a classic EEG spectrogram during general anesthesia, with fluctuations in alpha power, presumably due to underlying changes in arousal or nociception. Several state-space signal processing methods have been developed that can extract transient fluctuations in alpha amplitude ( Figure 7B ) (as described in Beck, He, Gutierrez, and Purdon (2022)), as well as phase and instantaneous frequency information. Alternatively, other standard methods can be used, such as applying a bandpass filter to the alpha frequency, performing a Hilbert transform, and taking the bandpass (real) and Hilbert transformed (imaginary) signals to estimate alpha amplitude, phase, or instantaneous frequency. Fluctuations in alpha power or alpha frequency or alpha amplitude may indicate arousal associated with nociception and can be used to titrate opioids or other non-opioid analgesics, for example by selecting a dose that minimizes fluctuations in alpha power, frequency, or amplitude.

[0074] Figures 8A-8C is the comparison of the skin conductance response (SCR) and the alpha band power. Fig.6D They were highly correlated with the simultaneously recorded EDA in the individual subjects studied and in the three patients on average ( Figures 8A-8B The correlation between alpha amplitude and EDA helps confirm previous literature linking alpha fluctuations to nociception ( Figure 8C ).

[0075] In a non-limiting example, in order to generate an intraoperative nociception management plan using the disclosed systems and methods to optimize postoperative outcomes, a model characterizing the relationship between intraoperative variables, anesthetic information, patient baseline and demographic variables and postoperative outcomes (including but not limited to postoperative pain, opioid requirements, cognitive recovery time, respiratory depression) is used to construct and calibrate monitoring indices or variables. Such models can be constructed using any number of methods (including but not limited to machine learning models, deep learning networks, or regression models). These models can take into account additional confounding variables or covariates that may introduce bias in the prediction of postoperative outcomes (including patient baselines, demographic information, medical history, anesthesia records, and other clinical variables), which can be obtained from an electronic health record system. In a non-limiting example, the data obtained (including all of the above measurements and variables) and / or the data collected from patients in a systematic manner are used to construct and calibrate such models.

[0076] Fig. 9 A schematic diagram depicting the variables measured and their timeline in a clinical observational study designed to collect data for model, algorithm, and index development is shown. Preoperative measurements included a pain numeric rating scale, psychomotor alertness, TabCAT Brain Health Assessment (BHA), and Hospital Anxiety and Depression Scale (HADS). Intraoperative measurements included EEG and EDA, as well as heart rate variability (HRV), blood pressure (BP) waveform data, and recording of all medications and clinical events. Postoperatively in the postoperative care unit (PACU), measurements included EEG and EDA recording continued, as well as pain scores every 15 minutes for the first hour and every hour thereafter. PVT, BHA, and HADS were measured every hour until discharge from the PACU. Opioid-related side effects, modified Aldrete scores (respiratory depression), PACU and inpatient analgesics and opioids administered, and their 90-day and 180-day opioid consumption status were obtained from medical records. Patient baseline, demographics, medical history, anesthesia records, and other clinical variables can be obtained from electronic health record systems (e.g., EPIC).

[0077] See also Fig.10, given that some pathways 1002 to developing operating room monitors focus on physiological variables 1006 and expert rating scales 1008 in the operating room setting, they do not explicitly account for the relationship of these intraoperative variables to post-operative outcomes 1012 in building or calibrating the monitoring system, algorithm, or monitoring index 1010. As a result, an operating room monitor developed with such a typical pathway 1002 may not achieve the desired post-operative outcome 1012 because the monitoring index 1010 value or range of values ​​may have little or no relationship to the desired post-operative outcome 1012. Alternatively, the pathway 1004 described in accordance with aspects of the present disclosure employs data obtained, such as Fig. 9 As described herein, an operational monitoring system, algorithms, and monitoring indices 1016 (derived from physiological variables 1014 and patient records) are operated to achieve a desired post-operative outcome 1018. As described herein, a post-operative plan can be provided that is based on the surgical procedure and reduces post-operative pain, opioid requirements, respiratory depression, and length of post-operative hospital stay compared to a post-operative plan that does not take into account opioids or other medications administered during the surgical procedure.

[0078] In one non-limiting example, the systems and methods of the present disclosure were considered with respect to three female patients, Patient 1, Patient 2, and Patient 3, aged 48, 51, and 52 years, respectively, each undergoing surgery under general anesthesia with varying levels of intraoperative analgesia and varying postoperative outcomes. For each patient, mean fentanyl concentration, median theta EEG power, skin conductance, and fluctuations in alpha EEG amplitude and instantaneous alpha EEG frequency during surgery were calculated. Mean fentanyl concentrations throughout surgery were calculated by extracting dose information from an electronic medical record system and calculating effect site concentrations using the Pk / Pd model described by McClain and Hug (Clin Pharmacol Ther 1980). Theta power throughout the duration of surgery was calculated using a multi-taper spectral estimation method in 4-second windows throughout the duration of surgery. A tapered filter centered at 6 Hz covering the theta band from 4 to 8 Hz was used to estimate the theta power in each window, and the median of these values ​​throughout the duration of surgery was reported. Electrode conductance was estimated by demodulating the 7.5 nA, 78 Hz test current used to measure electrode impedance. The data were filtered using a 6 Hz bandwidth filter and then the Hilbert transform was applied to estimate the amplitude of the 78 Hz signal, which was in turn used to calculate impedance and conductance. The median of these values ​​over the duration of surgery is reported. Alpha amplitude and instantaneous frequency were calculated by applying the Hilbert transform to the EEG data bandpass filtered to 8–12 Hz with a 2 Hz filter transition window. For each patient, postoperative outcomes were also examined, namely, total opioid use within the first 24 h after surgery and maximum pain score within the first 24 h after surgery obtained from the electronic health record.

[0079] Figure 11 shows the mean fentanyl concentration and median theta power for each of patients 1, 2, and 3. Patient 1 received the highest fentanyl concentration, patient 2 received a lower fentanyl concentration, and patient 3 received no fentanyl. The median theta power for each patient shows the corresponding trend for each patient, as can be seen from the Figure 2D As expected, patient 1 had the highest theta power, patient 2 had lower theta power, and patient 3 had the lowest theta power.

[0080] FIG12 shows the postoperative outcomes for these patients. Consistent with the modeling and counterfactual analyses presented above, Patient 1, who had the highest fentanyl concentration and correspondingly the highest theta power, required the lowest amount of postoperative opioids for pain control in the first 24 hours after surgery among the three patients, and also exhibited the lowest maximum pain score for 24 hours after surgery among the three patients. Patient 3, who had the lowest fentanyl concentration and the lowest corresponding theta power, required the highest amount of opioids for pain control in the first 24 hours after surgery among the three patients, and also exhibited the highest maximum pain score for 24 hours after surgery among the three patients. Patient 2, who had a fentanyl concentration and corresponding theta power between Patient 1 and Patient 3, required an amount of opioids for pain control in the first 24 hours after surgery between the amounts required by Patient 1 and Patient 3, and exhibited a maximum pain score for 24 hours after surgery between those exhibited by Patient 1 and Patient 3. As shown in FIG4 , theta power, alone or in combination with drug concentrations estimated using the Pk / Pd model, provides an accurate assessment of individual personalized patient response to opioids. Considering the modeling and counterfactual analyses presented previously, theta power, alone or in combination with estimated drug concentrations, can be used to predict postoperative outcomes or guide analgesia to achieve desired postoperative outcomes.

[0081] The skin conductance of each patient was Fig.13 As shown in . Consistent with the previous description, maintaining skin conductance below certain thresholds can help optimize postoperative pain and opioid requirements. The fluctuations in alpha amplitude and alpha instantaneous frequency for each patient are shown in FIG. 14 . Consistent with the previous description, maintaining these fluctuations in alpha amplitude and alpha instantaneous frequency below certain thresholds can help optimize postoperative pain and opioid requirements.

[0082] Given the multiple related measurements, including opioid-induced EEG theta oscillations, skin conductance, alpha band EEG amplitude and instantaneous frequency fluctuations, as well as multiple complex patient baseline variables, medical history and multiple medications administered, among other variables, it would be advantageous and highly desirable for systems and methods according to aspects of the present disclosure to model the relationships between these variables and summarize the information to guide intraoperative analgesia or postoperative pain management. In contrast, given the complexity of such high-dimensional information that must often be interpreted in real time, it is unlikely that clinicians would be able to consistently achieve optimal results without such systems and methods for processing such multivariate information.

[0083] Those skilled in the art will appreciate that although the disclosed subject matter relates to specific embodiments and examples, the invention is not necessarily limited thereto, and that numerous other embodiments, examples, uses, modifications and deviations from the embodiments, examples and uses are intended to be included in the claims appended hereto.

[0084] Various features and advantages of the invention are set forth in the following claims.

Claims

1. A system for monitoring a patient during surgery, the system comprising: one or more sensors configured to measure electroencephalogram (EEG) and electrical skin signals of a patient undergoing at least one anesthetic and at least one analgesic during a surgical medical procedure; as well as a processor, operatively coupled to the one or more sensors, configured to: Receiving the EEG and skin electrical signals; Using the EEG or skin electrodeion signals to monitor the patient's nociceptive state in real time during a surgical medical procedure; as well as A post-operative pain management plan is generated using at least one nociceptive state of the patient during the surgical medical procedure or the at least one analgesic agent administered to the patient for the surgical medical procedure.

2. The system of claim 1, wherein the processor is further configured to identify a drug effect profile from the EEG signal that is highly correlated with the patient's effect site concentration (ESC) of the at least one analgesic.

3. The system of claim 1 or 2, wherein the at least one analgesic comprises an opioid.

4. The system of claim 3, wherein the opioid comprises fentanyl.

5. The system of claim 1, wherein the post-operative pain management plan comprises a plurality of post-operative outcomes.

6. The system of claim 5, wherein the plurality of outcomes comprises at least one of post-operative pain, opioid requirement, cognitive recovery time, or respiratory depression.

7. The system of claim 1 , wherein the processor is further configured to achieve desired post-operative outcomes based on the patient's real-time nociceptive state during a surgical medical procedure, including at least one of reducing post-operative pain, opioid requirements, cognitive recovery time, and respiratory depression.

8. A system for monitoring a patient during surgery, the system comprising: one or more sensors configured to measure electroencephalogram (EEG) and electrical skin signals of a patient undergoing at least one anesthetic and at least one analgesic during a surgical medical procedure; as well as a processor, operatively coupled to the one or more sensors, configured to: Receiving the EEG and skin electrical signals; Using the EEG or electrodermal signals, a report is generated indicating the patient's real-time nociceptive status during a surgical medical procedure while the patient is undergoing the at least one anesthetic.

9. The system of claim 8, wherein the processor is further configured to identify a pharmacodynamic profile from the EEG signal that is highly correlated with the patient's effect site concentration (ESC) of the at least one analgesic.

10. The system of claim 8 or 9, wherein the at least one analgesic comprises an opioid.

11. The system of claim 10, wherein the opioid comprises fentanyl.

12. The system of claim 9, wherein the report indicative of the patient's real-time nociceptive state during a surgical medical procedure further comprises instructions for titrating at least one dose of the at least one anesthetic or at least one analgesic administered to the patient.

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