Tonic Cold Pain Research Articles

Adynamic response to cold pain reflects dysautonomia in type 1 diabetes and polyneuropathy

Cardiac autonomic neuropathy (CAN), widely assessed by heart rate variability (HRV), is a common complication of long-term diabetes. We hypothesized that HRV dynamics during tonic cold pain in individuals with type 1 diabetes mellitus (T1DM) could potentially demask CAN. Forty-eight individuals with long-term T1DM and distal symmetrical polyneuropathy and 21 healthy controls were included. HRV measures were retrieved from 24-h electrocardiograms. Moreover, ultra-short-term HRV recordings were used to assess the dynamic response to the immersion of the hand into 2 °C cold water for 120 s. Compared to healthy, the T1DM group had expectedly lower 24-h HRV measures for most components (p < 0.01), indicating dysautonomia. In the T1DM group, exposure to cold pain caused diminished sympathetic (p < 0.001) and adynamic parasympathetic (p < 0.01) HRV responses. Furthermore, compared to healthy, cold pain exposure caused lower parasympathetic (RMSSD: 4% vs. 20%; p = 0.002) and sympathetic responses (LF: 11% vs. 73%; p = 0.044) in the T1MD group. QRISK3-scores are negatively correlated with HRV measures in 24-h and ultra-short-term recordings. In T1DM, an attenuated sympathovagal response was shown as convincingly adynamic parasympathetic responses and diminished sympathetic adaptability, causing chronometric heart rhythm and rigid neurocardiac regulation threatening homeostasis. The findings associate with an increased risk of cardiovascular disease, emphasizing clinical relevance.

Pain chronification impacts whole-brain functional connectivity in women with hip osteoarthritis during pain stimulation.

Previous neuroimaging studies have shown that patients with chronic pain display altered functional connectivity across distributed brain areas involved in the processing of nociceptive stimuli. The aim of the present study was to investigate how pain chronification modulates whole-brain functional connectivity during evoked clinical and tonic pain. Patients with osteoarthritis of the hip (n = 87) were classified into 3 stages of pain chronification (Grades I-III, Mainz Pain Staging System). Electroencephalograms were recorded during 3 conditions: baseline, evoked clinical hip pain, and tonic cold pain (cold pressor test). The effects of both factors (recording condition and pain chronification stage) on the phase-lag index, as a measure of neuronal connectivity, were examined for different frequency bands. In women, we found increasing functional connectivity in the low-frequency range (delta, 0.5-4 Hz) across pain chronification stages during evoked clinical hip pain and tonic cold pain stimulation. In men, elevated functional connectivity in the delta frequency range was observed in only the tonic cold pain condition. Across pain chronification stages, we found that widespread cortical networks increase their synchronization of delta oscillations in response to clinical and experimental nociceptive stimuli. In view of previous studies relating delta oscillations to salience detection and other basic motivational processes, our results hint at these mechanisms playing an important role in pain chronification, mainly in women.

Do "central sensitization" questionnaires reflect measures of nociceptive sensitization or psychological constructs? A systematic review and meta-analyses.

Central sensitization (CS) is defined as an increased nociceptive responsiveness due to sensitization of neurons in the central nervous system, usually the result of prolonged nociceptive input or a disease state associated with noxious inputs (eg, polyarthritis). The concept of CS has recently been adopted in clinical assessments of chronic pain, but its diagnosis in humans may now include a wide range of hypervigilant responses. The purpose of this review is to ascertain whether self-report questionnaires linked with CS are associated with enhanced nociceptive responses or whether they measure sensitivity in a broader sense (ie, emotional responses). According to our published, PROSPERO-registered review protocol (CRD42021208731), a predefined search of studies that involve the Central Sensitization Inventory (CSI) or Pain Sensitivity Questionnaire (PSQ), correlated with either nociceptive sensory tests or emotional hypervigilance was conducted on MEDLINE, PsycINFO, and Web of Science. Correlations between the CSI or PSQ with our primary outcomes were extracted and meta-analysed. A review of 66 studies totalling 13,284 participants found that the CSI (but not the PSQ) strongly correlated with psychological constructs: depression, anxiety, stress, pain catastrophising, sleep, and kinesiophobia. The CSI and PSQ showed weak or no correlations with experimental measures of nociceptive sensitivity: pain thresholds, temporal summation, or conditioned pain modulation. The PSQ did, however, correlate strongly with phasic heat and tonic cold pain tests. The studies reviewed did not provide sufficient evidence that self-report measures reflect a canonical understanding of CS. The CSI more closely reflects psychological hypervigilance than increased responsiveness of nociceptive neurons.

Central neuronal transmission in response to tonic cold pain is modulated in people with type 1 diabetes and severe polyneuropathy

AimsThis study aimed to investigate cortical source activity and identify source generators in people with type 1 diabetes during rest and tonic cold pain. MethodsForty-eight participants with type 1 diabetes and neuropathy, and 21 healthy controls were investigated with electroencephalography (EEG) during 5-minutes resting and 2-minutes tonic cold pain (immersing the hand into water at 2 °C). EEG power was assessed in eight frequency bands, and EEG source generators were analyzed using standardized low-resolution electromagnetic tomography (sLORETA). ResultsCompared to resting EEG, cold pain EEG power differed in all bands in the diabetes group (all p < 0.001) and six bands in the controls (all p < 0.05). Source generator activity in the diabetes group was increased in delta, beta2, beta3, and gamma bands and decreased in alpha1 (all p < 0.006) with changes mainly seen in the frontal and limbic lobe. Compared to controls, people with diabetes had decreased source generator activity during cold pain in the beta2 and beta3 bands (all p < 0.05), mainly in the frontal lobe. ConclusionsParticipants with type 1 diabetes had altered EEG power and source generator activity predominantly in the frontal and limbic lobe during tonic cold pain. The results may indicate modulated central transmission and neuronal impairment.

Simple Quantitative Sensory Testing Reveals Paradoxical Co-existence of Hypoesthesia and Hyperalgesia in Diabetes.

Background: Diabetic neuropathy is characterized by the paradoxical co-existence of hypo- and hyperalgesia to sensory stimuli. The literature shows consistently sensory differences between healthy and participants with diabetes. We hypothesized that due to differences in pathophysiology, advanced quantitative sensory testing (QST) might reveal sensory discrepancies between type 1 (T1D) and type 2 diabetes (T2D). Furthermore, we investigated whether vibration detection thresholds (VDT) were associated with sensory response.Method: Fifty-six adults with T1D [43 years (28–58)], 99 adults with T2D [65 years (57–71)], and 122 healthy individuals [51 years (34–64)] were included. VDT, pressure pain detection thresholds (pPDT) and tolerance (pPTT), tonic cold pain (hand-immersion in iced water), and central pain mechanisms (temporal summation and conditioned pain modulation) were tested and compared between T1D and T2D. VDT was categorized into normal (< 18 V), intermediary (18–25 V), or high (> 25 V).Results: In comparison to healthy, analysis adjusted for age, BMI, and gender revealed hypoalgesia to tibial (pPDT): p = 0.01, hyperalgesia to tonic cold pain: p < 0.01, and diminished temporal summation (arm: p < 0.01; abdomen: p < 0.01). In comparison to participants with T2D, participants with T1D were hypoalgesic to tibial pPDT: p < 0.01 and pPTT: p < 0.01, and lower VDT: p = 0.02. VDT was not associated with QST responses.Conclusion: Participants with T1D were more hypoalgesic to bone pPDT and pPTT independent of lower VDT, indicating neuronal health toward normalization. Improved understanding of differentiated sensory profiles in T1D and T2D may identify improved clinical endpoints in future trials.

Efficacy of Anodal Suboccipital Direct Current Stimulation for Endogenous Pain Modulation and Tonic Thermal Pain Control in Healthy Participants: A Randomized Controlled Clinical Trial.

The aim of this study was to assess whether anodal DCS applied to the suboccipital (SO) target area could potentiate antinociception assessed primarily with conditioned pain modulation of tonic thermal test stimuli. Randomized double-blinded control trial. Rehabilitation hospital. Healthy participants. Forty healthy participants were randomized to receive either SO-DCS or M1-DCS. The 20-minute 1.5 mA anodal or sham DCS intervention were applied to each participant in randomized order during two test sessions. The primary outcome measure included heterotopic cold-pressor conditioned pain modulation (CPM) of tonic heat pain. Secondary measures included pressure pain threshold and tonic thermal pain intensity. Heterotopic CPM of tonic heat pain intensity was unaffected by either SO-DCS or active M1, including the secondary measures of pressure pain threshold and tonic thermal pain intensity. Although low-power non-significant interactions were identified for DCS intervention (active versus sham) and time (before and after), a significant within-group inhibition of tonic cold pain was identified following SO-DCS (P = .011, mean [SD]: -0.76 ± 0.88 points) and M1-DCS (P < .002: -0.84 ± 0.82 points), without a significant change following sham DCS. Although heterotopic CPM was not facilitated with either SO-DCS or M1-DCS, a general significant inhibition of tonic cold pain intensity was demonstrated following both interventions. The general effects of active DCS compared to sham on tonic cold pain-irrespective of the M1 or SO target-need to be confirmed using standard quantitative sensory testing.

Ultra-high-field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain.

We investigated how the attenuation of pain with cognitive interventions affects brain connectivity using neuroimaging and a whole brain novel analysis approach. While receiving tonic cold pain, 20 healthy participants performed three different pain attenuation strategies during simultaneous collection of functional imaging data at seven tesla. Participants were asked to rate their pain after each trial. We related the trial-by-trial variability of the attenuation performance to the trial-by-trial functional connectivity strength change of brain data. Across all conditions, we found that a higher performance of pain attenuation was predominantly associated with higher functional connectivity. Of note, we observed an association between low pain and high connectivity for regions that belong to brain regions long associated with pain processing, the insular and cingulate cortices. For one of the cognitive strategies (safe place), the performance of pain attenuation was explained by diffusion tensor imaging metrics of increased white matter integrity.

Sustained perturbation in functional connectivity induced by cold pain.

Functional connectivity (FC) perturbations have been reported in multiple chronic pain phenotypes, but the nature of reported changes varies between cohorts and may relate to the consequences of living with chronic-pain related comorbidities, such as anxiety and depression. Healthy volunteer studies provide opportunities to study the effects of tonic noxious stimulation independently of these sequelae. Connectivity changes in task negative and positive networks, for example, the default mode and salience networks (DMN/SN), respectively, have been described, but how these and other connectivity networks, for example, those governing descending pain control are affected by the presence of tonic, noxious stimulation in healthy, pain-free individuals, remains unknown. In 20 healthy volunteers, we assessed FC prior to, during, and following tonic cold painful stimulation in the ventromedial prefrontal cortex (vmPFC), rostral anterior insula (rAI), subgenual anterior cingulate cortex (ACC) and periaqueductal grey (PAG). We also recorded subjectively reported pain using a computerised visual analogue scale. We saw DMN FC changes during painful stimulation and that inter-network connectivity between the rAI with the vmPFC increased during pain, whereas PAG-precuneus FC decreased. Pain-induced FC alterations persisted following noxious stimulation. FC changes related to the magnitude of individuals' subjectively reported pain. We demonstrate FC changes during and following tonic cold-pain in healthy participants. Similarities between our findings and reports of patients with chronic pain suggest that some FC changes observed in these patients may relate to the presence of an ongoing afferent nociceptive drive. How pain-related resting state networks are affected by tonic cold-pain remains unknown. We investigated functional connectivity alterations during and following tonic cold pain in healthy volunteers. Cold pain perturbed the functional connectivity of the ventro-medial prefrontal cortex, anterior insula, and the periacquaductal grey area. These connectivity changes were associated with the magnitude of individuals' reported pain. We suggest that some connectivity changes described in chronic pain patients may be due to an ongoing afferent peripheral drive.

EEG-based tonic cold pain assessment using extreme learning machine

The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pai n states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.

Evaluation of Acute Tonic Cold Pain From Microwave Transcranial Transmission Signals Using Multi-Entropy Machine Learning Approach

This study aims to improve the accuracy of detecting acute tonic cold pain (CP) perception from microwave transcranial transmission (MTT) signals. Two different types of CP and no-pain (NP) MTT signals are obtained from 15 subjects. Four features, namely, power spectral exponential entropy, improved multiscale permutation entropy, refined composite multiscale dispersion entropy, and refined composite multiscale fuzzy entropy, are extracted in the variational modal decomposition domain. The feature datasets are divided into training datasets and test datasets in a 3:1 ratio. Random forest (RF) and support vector machine (SVM) are selected as classifiers. The training datasets are imported into the classifier, and the optimal training dataset is obtained with a 10-fold cross validation strategy. The feature dimension reduction algorithm of the principal component analysis is used to reduce the complexity of the feature datasets and select the most recognizable features. The classification performance of the test datasets is evaluated by the optimal classifiers. Results showed that the RF classifier performs better than the SVM classifier. The RF classifier provides high values of specificity (91.67%), sensitivity (95.83%), positive predictive value (92.00%), accuracy (93.75%), and area under curve (0.867). The combination of the microwave detection approach and machine learning algorithm can effectively detect brain activity induced by nociceptive stimulation. This approach is important in improving the accuracy of pain detection.

Diverse frequency band-based convolutional neural networks for tonic cold pain assessment using EEG

The purpose of this study is to present a novel classification framework, called diverse frequency band-based Convolutional Neural Networks (DFB-based ConvNets), which can objectively identify tonic cold pain states. To achieve this goal, scalp EEG data were recorded from 32 subjects under cold stimuli conditions. The proposed DFB-based ConvNets model is capable of classifying three classes of tonic pain: No pain, Moderate Pain, and Severe Pain. Firstly, the proposed method utilizes diverse frequency band-based inputs to learn temporal representations from different frequency bands of Electroencephalogram (EEG) which are expected to have more discriminative power. Then the derived features are concatenated to form a feature vector, which is fed into a fully-connected network for performing the classification task. Experimental results demonstrate that the proposed method successfully discriminates the tonic cold pain states. To show the superiority of the DFB-based ConvNets classifier, we compare our results with the state-of-the-art classifiers and show it has a competitive classification accuracy (97.37%). Moreover, these promising results may pave the way to use DFB-based ConvNets in clinical pain research.

Tonic Cold Pain Detection Using Choi–Williams Time-Frequency Distribution Analysis of EEG Signals: A Feasibility Study

Detecting pain based on analyzing electroencephalography (EEG) signals can enhance the ability of caregivers to characterize and manage clinical pain. However, the subjective nature of pain and the nonstationarity of EEG signals increase the difficulty of pain detection using EEG signals analysis. In this work, we present an EEG-based pain detection approach that analyzes the EEG signals using a quadratic time-frequency distribution, namely the Choi–Williams distribution (CWD). The use of the CWD enables construction of a time-frequency representation (TFR) of the EEG signals to characterize the time-varying spectral components of the EEG signals. The TFR of the EEG signals is analyzed to extract 12 time-frequency features for pain detection. These features are used to train a support vector machine classifier to distinguish between EEG signals that are associated with the no-pain and pain classes. To evaluate the performance of our proposed approach, we have recorded EEG signals for 24 healthy subjects under tonic cold pain stimulus. Moreover, we have developed two performance evaluation procedures—channel- and feature-based evaluation procedures—to study the effect of the utilized EEG channels and time-frequency features on the accuracy of pain detection. The experimental results show that our proposed approach achieved an average classification accuracy of 89.24% in distinguishing between the no-pain and pain classes. In addition, the classification performance achieved using our proposed approach outperforms the classification results reported in several existing EEG-based pain detection approaches.

Changes of EEG band oscillations to tonic cold pain and the behavioral inhibition and fight-flight-freeze systems.

Using electroencephalography (EEG) power measures within conventional delta, theta, alpha, beta, and gamma bands, the aims of the current study were to highlight cortical correlates of subjective perception of cold pain (CP) and the associations of these measures with behavioral inhibition system (BIS), fight-flight-freeze system (FFFS), and behavioral approach system personality traits. EEG was recorded in 55 healthy right-handed women under (i) a white noise interruption detection condition (Baseline); (ii) enduring CP induced by the cold cup test. CP and Baseline EEG band power scores within conventional frequency bands served for covariance analyses. We found that: (1) higher Pain scorers had higher EEG beta power changes at left frontal, midline central, posterior temporal leads; (2) higher BIS was associated with greater EEG delta activity changes at parietal scalp regions; (3) higher FFFS was associated with higher EEG delta activity changes at temporal and left-parietal regions, and with lower EEG gamma activity changes at right parietal regions. High FFFS, compared to Low FFFS scorers, also showed a lower gamma power across the midline, posterior temporal, and parietal regions. Results suggest a functional role of higher EEG beta activity in the subjective perception of tonic pain. EEG delta activity underpins conflict resolution system responsible for passive avoidance control of pain, while higher EEG delta and lower EEG gamma activity changes, taken together, underpin active avoidance system responsible for pain escape behavior.

Detection of Acute Tonic Cold Pain From Microwave Transcranial Transmission Signals Obtained via the Microwave Scattering Approach

To investigate the feasibility of detecting acute tonic cold pain (CP) perception from recordable microwave transcranial transmission (MTT) signals by using machine learning techniques. CP and no-pain (NP) MTT signals collected from 15 young subjects are analyzed in the wavelet packet transformation (WPT) and variational mode decomposition (VMD) domains. In addition, features such as relative energy change, refined composite multiscale dispersion entropy, refined composite multiscale fuzzy entropy, and autoregressive model coefficients are extracted in the WPD, VMD, VMD-WPD, and WPD-VMD domains. Simultaneously, support vector machine (SVM) is selected as the classifier, and feature indexes are input into the classifier by using the 10-fold cross validation method to obtain the best training and test datasets. Principal component analysis is used to reduce the feature dimensions of the training and test datasets and to improve classification accuracy. Then, the test dataset is imported into the trained classifier for the calculation and evaluation of the model's classification performance. In the validation of the SVM classifier, feature extraction in the WPD-VMD domain is the best pain detection algorithm. It provides high values of sensitivity (91.30%), specificity (90.47%), positive predictive value (91.30%), accuracy (90.90%), and area under curve (0.806). The microwave scattering technique can be used as a direct, objective, and experimentally stable method to detect acute CP perception, this approach has a high application prospect for clinical real-time diagnosis.

Changes of spontaneous EEG-band oscillations to tonic cold pain and the Behavioural Inhibition and Fight-Flight-Freeze Systems

Changes of spontaneous EEG-band oscillations to tonic cold pain and the Behavioural Inhibition and Fight-Flight-Freeze Systems

Offset analgesia is not affected by cold pressor induced analgesia.

Background and aims Offset analgesia (OA) is a pain modulating mechanism described as a disproportionately large decrease in pain intensity evoked by a minor decrease in stimulus intensity. Precise mechanisms of OA are still not elucidated and studies are needed to evaluate factors modulating OA. The aim of this study was to investigate OA before and during tonic cold pain (thought to induce descending inhibition), in a group of healthy volunteers. Methods A randomized, crossover study was performed in 17 healthy participants (8 males and 9 females). The OA paradigm lasted 35 s and was induced by the traditional method using thermal stimulation applied to the forearm. A constant control heat stimulus (CTL) paradigm was used as control to assess adaptation. Pain intensity was assessed continuously. For induction of tonic cold pain, the participants immersed their hand into 2°C water for 2 min. After 1 min and 25 s, the heat stimulation (OA or CTL paradigm) was repeated to assess the modulatory effect of the cold pressor test. Results It was possible to induce OA both before and during the cold pressor test. Tonic cold pain modulated the peak pain reported during both the OA (p=0.015) and CTL paradigms (p=0.001) reflecting endogenous pain modulation. However, the magnitude of OA was not modulated by tonic cold pain (p>0.05). Conclusions The offset analgesia magnitude was not modulated by simultaneously tonic cold pain, thought to reflect another endogenous pain modulation mechanism. Implications Neither offset analgesia magnitude nor adaptation were modulated by cold pressor induced endogenous analgesia. This could be explained by the fact, that offset analgesia was already at maximum in healthy participants. Hence, offset analgesia may not be a suitable assessment tool to investigate modulation induced by experimental methods or pharmacology in healthy participants.

Night-shift work increases cold pain perception

BackgroundAlthough night-shift work (NSW) is associated with a higher risk for several physical and mental disorders, the impact of NSW on pain perception is still unclear. This study investigates the impact of NSW on cold pain perception considering the impact of mood and sleepiness. MethodQuantitative sensory testing (QST) was performed in healthy night-shift workers. Cold pain threshold as well as tonic cold pain was assessed after one habitual night (T1), after a 12-hour NSW (T2) and after one recovery night (T3). Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) before T1, sleepiness with the Stanford Sleepiness Scale (SSS) and mood with a German short-version of the Profile of Mood States (ASTS) at T1, T2 and T3. Depending on the distribution of the data, ANOVAs or Friedman tests as well as t- or Wilcoxon tests were performed. ResultsNineteen healthy shift-workers (13 females; 29.7 ± 7.5 years old; 8.1 ± 6.6 years in shift work, PSQI: 4.7 ± 2.2) were included. Tonic cold pain showed a significant difference between T1 (48.2 ± 27.5 mm), T2 (61.7 ± 26.6 mm; effect size: Cohen's d=.49; percent change 28%), and T3 (52.1 ± 28.7 mm) on a 0–100 mm Visual Analog Scale (p = 0.007). Cold pain threshold changed from 11.0 ± 7.9 °C (T1) to 14.5 ± 8.8 °C (T2) (p = 0.04), however, an ANOVA comparing T1, T2, and T3 was not significant (p = 0.095). Sleepiness (SSS) and mood (ASTS) changed significantly between T1, T2 and T3 (p-values < 0.01). The change of mood but not of sleepiness correlated with the difference in tonic cold pain from T1 to T2 (R: 0.53; R2: 0.29; p = 0.022). DiscussionNSW increases cold pain perception. The same tonic cold pain stimulus is rated 28% more painful after NSW and normalizes after a recovery night. Increases in cold pain perception due to NSW appear to be more strongly related to changes in mood as compared to changes in sleepiness.

EEG-based tonic cold pain recognition system using wavelet transform

Developing an objective pain identification system can provide caregivers with a second opinion to improve the treatment of patients who are unable to verbally communicate their pain. In this study, we present a new EEG-based approach for pain recognition. The proposed approach is employed to identify four different states that a human can feel during tonic cold pain stimulation. These states are the relax state, relax-to-pain state (RPS), pain state (PS), and pain-to-relax state (PRS). A sliding window has been used to decompose the EEG signals into overlapping segments. Each EEG segment is analyzed using the discrete wavelet transform to construct a time–frequency representation of the EEG signals and extract a set of nonlinear features. These features are used to construct a two-layer hierarchical classification framework that can identify the aforementioned four pain states. The first layer identifies whether an EEG segment is relax or pain segment. In the second layer, the pain segments are classified into one of the three pain states (i.e., RPS, PS, and PRS). To evaluate the performance of the proposed approach, we recorded EEG data for 24 healthy subjects who were exposed to tonic cold pain stimulation. Three procedures were employed to evaluate the capability of the approach to detect the four states associated with tonic cold pain stimulation. The experimental results demonstrate the efficacy of our approach for accurate tonic cold pain identification. Moreover, these promising results suggest the feasibility of expanding the proposed approach to characterize clinical pain, such as cancer-related pain.

Prediction of persistent post-surgery pain by preoperative cold pain sensitivity: biomarker development with machine-learning-derived analysis

BackgroundTo prevent persistent post-surgery pain, early identification of patients at high risk is a clinical need. Supervised machine-learning techniques were used to test how accurately the patients’ performance in a preoperatively performed tonic cold pain test could predict persistent post-surgery pain. MethodsWe analysed 763 patients from a cohort of 900 women who were treated for breast cancer, of whom 61 patients had developed signs of persistent pain during three yr of follow-up. Preoperatively, all patients underwent a cold pain test (immersion of the hand into a water bath at 2–4 °C). The patients rated the pain intensity using a numerical ratings scale (NRS) from 0 to 10. Supervised machine-learning techniques were used to construct a classifier that could predict patients at risk of persistent pain. ResultsWhether or not a patient rated the pain intensity at NRS=10 within less than 45 s during the cold water immersion test provided a negative predictive value of 94.4% to assign a patient to the “persistent pain” group. If NRS=10 was never reached during the cold test, the predictive value for not developing persistent pain was almost 97%. However, a low negative predictive value of 10% implied a high false positive rate. ConclusionsResults provide a robust exclusion of persistent pain in women with an accuracy of 94.4%. Moreover, results provide further support for the hypothesis that the endogenous pain inhibitory system may play an important role in the process of pain becoming persistent.

Lack of genetic association between OCT1, ABCB1, and UGT2B7 variants and morphine pharmacokinetics

AimA high inter-individual variation in the pharmacokinetics and pharmacodynamics of morphine has been observed. Genetic polymorphisms in genes encoding the organic cation transporter isoform 1 (OCT1), the efflux transporter p-glycoprotein (ABCB1), and the UDP-glucuronosyltransferase-2B7 (UGT2B7) may influence morphine pharmacokinetics and thus, also pharmacodynamics. The aim of this study was to evaluate the association between OCT1, ABCB1, and UGT2B7 variants, and morphine pharmacokinetics and -dynamics in healthy volunteers. MethodsPharmacokinetic and pharmacodynamic data were collected from a double-blinded, randomized, crossover trial in 37 healthy subjects. Pharmacokinetic data were analyzed in NONMEM®, and the time-concentration relationship of morphine, morphine-3-glucuronide, and morphine-6-glucuronide was parameterized as the transit compartment rate constant (ktr), clearance (CL), and volume of distribution (VD). The area under the plasma concentration-time curve (AUC0–150min) and the maximum plasma concentration (Cmax) were also calculated. Pharmacodynamic data were measured as pain tolerance thresholds to mechanical stimulation of the rectum and muscle, as well as tonic cold pain stimulation (“the cold pressor test” where hand was immersed in cold water). Six different single nucleotide polymorphisms in three different genes (OCT1 (n=22), ABCB1 (n=37), and UGT2B (n=22)) were examined. ResultsNeither AUC0–150min, ktr, CL, nor VD were associated with genetic variants in OCT1, ABCB1, and UGT2B7 (all P>0.05). Similarly, the antinociceptive effects of morphine on rectal, muscle, and cold pressor tests were not associated with these genetic variants (all P>0.05). ConclusionsIn this experimental study in healthy volunteers, we found no association between different genotypes of OCT1, ABCB1, and UGT2B7, and morphine pharmacokinetics and pharmacodynamics. Nonetheless, due to methodological limitations we cannot exclude that associations exist.