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

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review

BACKGROUND/OBJECTIVES

Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

METHODS

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

RESULTS

Forty four studies from 2006 to 2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%).

CONCLUSION

Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Background/objective

Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods

A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.

Results

This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).

Conclusion

Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Prognostic models for chronic postsurgical pain-Current developments, trends, and challenges

PURPOSE OF REVIEW

Prognostic models for chronic postsurgical pain (CPSP) aim to predict the likelihood for development and severity of CPSP in individual patients undergoing surgical procedures. Such models might provide valuable information for healthcare providers, allowing them to identify patients at higher risk and implement targeted interventions to prevent or manage CPSP effectively. This review discusses the latest developments of prognostic models for CPSP, their challenges, limitations, and future directions.

RECENT FINDINGS

Numerous studies have been conducted aiming to develop prognostic models for CPSP using various perioperative factors. These include patient-related factors like demographic variables, preexisting pain conditions, psychosocial aspects, procedure-specific characteristics, perioperative analgesic strategies, postoperative complications and, as indicated most recently, biomarkers. Model generation, however, varies and performance and accuracy differ between prognostic models for several reasons and validation of models is rather scarce.

SUMMARY

Precise methodology of prognostic model development needs advancements in the field of CPSP. Development of more accurate, validated and refined models in large-scale cohorts is needed to improve reliability and applicability in clinical practice and validation studies are necessary to further refine and improve the performance of prognostic models for CPSP.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

Development and validation of a nomogram for blood transfusion during intracranial aneurysm clamping surgery: a retrospective analysis

PURPOSE

Intraoperative blood transfusion is associated with adverse events. We aimed to establish a machine learning model to predict the probability of intraoperative blood transfusion during intracranial aneurysm surgery.

METHODS

Patients, who underwent intracranial aneurysm surgery in our hospital between January 2019 and December 2021 were enrolled. Four machine learning models were benchmarked and the best learning model was used to establish the nomogram, before conducting a discriminative assessment.

RESULTS

A total of 375 patients were included for analysis in this model, among whom 108 received an intraoperative blood transfusion during the intracranial aneurysm surgery. The least absolute shrinkage selection operator identified six preoperative relative factors: hemoglobin, platelet, D-dimer, sex, white blood cell, and aneurysm rupture before surgery. Performance evaluation of the classification error demonstrated the following: K-nearest neighbor, 0.2903; logistic regression, 0.2290; ranger, 0.2518; and extremely gradient boosting model, 0.2632. A nomogram based on a logistic regression algorithm was established using the above six parameters. The AUC values of the nomogram were 0.828 (0.775, 0.881) and 0.796 (0.710, 0.882) in the development and validation groups, respectively.

CONCLUSIONS

Machine learning algorithms present a good performance evaluation of intraoperative blood transfusion. The nomogram established using a logistic regression algorithm showed a good discriminative ability to predict intraoperative blood transfusion during aneurysm surgery.

Machine Learning and Artificial Intelligence in Surgical Research

Machine learning, a subtype of artificial intelligence, is an emerging field of surgical research dedicated to predictive modeling. From its inception, machine learning has been of interest in medical and surgical research. Built on traditional research metrics for optimal success, avenues of research include diagnostics, prognosis, operative timing, and surgical education, in a variety of surgical subspecialties. Machine learning represents an exciting and developing future in the world of surgical research that will not only allow for more personalized and comprehensive medical care.

Association between systemic inflammation and experimental pain sensitivity in subjects with pain and painless neuropathy after traumatic nerve injuries

OBJECTIVES

Peripheral neuropathies that occur secondary to nerve injuries may be painful or painless, and including a low-grade inflammation and pro-inflammatory cytokines associated with both regeneration and damage of peripheral nerve cells and fibers. Currently, there are no validated methods that can distinguished between neuropathic pain and painless neuropathy. The aim of this study was to search for proinflammatory and anti-inflammatory proteins associated with pain and experimental pain sensitivity in subjects with surgeon-verified nerve injuries in the upper extremities.

METHODS

One hundred and thirty-one subjects [69 with neuropathic pain, NP; 62 with painless neuropathy, nP] underwent a conditioned pain modulation (CPM) test that included a cold pressor task (CPT) conducted with the non-injured hand submerged in cold water (4 °C) until pain was intolerable. CPM was assessed by pain ratings to pressure stimuli before and after applying the CPT. Efficient CPM effect was defined as the ability of the individual's CS to inhibit at least 29% of pain (eCPM). The subjects were assigned to one of two subgroups: pain sensitive (PS) and pain tolerant (PT) after the time they could tolerate their hand in cold water (PS<40 s and PT=60 s) . Plasma samples were analyzed for 92 proteins incorporated in the inflammation panel using multiplex Protein Extension Array Technology (PEA). Differentially expressed proteins were investigated using both univariate and multivariate analysis (principal component analysis-PCA and orthogonal partial least-squares discriminant analysis-OPLS-DA).

RESULTS

Significant differences in all protein levels were found between PS and PT subgroups (CV-ANOVA p<0.001), but not between NP and nP groups (p=0.09) or between inefficient CPM (iCPM) and eCPM (p=0.53) subgroups. Several top proteins associated with NP could be detected using multivariate regression analysis such as stromelysin 2 (MMPs), interleukin-2 receptor subunit beta (IL2RB), chemokine (C-X-C motif) ligand 3 (CXCL3), fibroblast growth factor 5 (FGF5), chemokine (C-C motif) ligand 28 (CCL28), CCL25, CCL11, hepatocyte growth factor (HGF), interleukin 4 (IL4), IL13. After adjusting for multiple testing, none of these proteins correlated significantly with pain. Higher levels of CCL20 (p=0.049) and CUB domain-containing protein (CDCP-1; p=0.047) were found to correlate significantly with cold pain sensitivity. CDCP-1 was highly associated with both PS and iCPM (p=0.042).

CONCLUSIONS

No significant alterations in systemic proteins were found comparing subjects with neuropathic pain and painless neuropathy. An expression of predominant proinflammatory proteins was associated with experimental cold pain sensitivity in both subjects with pain and painless neuropathy. One these proteins, CDC-1 acted as "molecular fingerprint" overlapping both CPM and CPT. This observation might have implications for the study of pain in general and should be addressed in more detail in future experiments.

Development and validation of a multivariable prediction model for early prediction of chronic postsurgical pain in adults: a prospective cohort study

BACKGROUND

Early identification of patients at risk of developing chronic postsurgical pain (CPSP) is an essential step in reducing pain chronification in postsurgical patients. We aimed to develop and validate a prognostic model for the early prediction of CPSP including pain characteristics indicating altered pain processing within 2 weeks after surgery.

METHODS

A prospective cohort study was conducted in adult patients undergoing orthopaedic, vascular, trauma, or general surgery between 2018 and 2019. Multivariable logistic regression models for CPSP were developed using data from the University Medical Centre (UMC) Utrecht and validated in data from the Erasmus UMC Rotterdam, The Netherlands.

RESULTS

In the development (n=344) and the validation (n=150) cohorts, 28.8% and 21.3% of patients reported CPSP. The best performing model (area under the curve=0.82; 95% confidence interval [CI], 0.76-0.87) included preoperative treatment with opioids (odds ratio [OR]=4.04; 95% CI, 2.13-7.70), bone surgery (OR=2.01; 95% CI, 1.10-3.67), numerical rating scale pain score on postoperative day 14 (OR=1.57; 95% CI, 1.34-1.83), and the presence of painful cold within the painful area 2 weeks after surgery (OR=4.85; 95% CI, 1.85-12.68). Predictive performance was confirmed by external validation.

CONCLUSIONS

As only four easily obtainable predictors are necessary for reliable CPSP prediction, the models are useful for the clinician to be alerted to further assess and treat individual patients at risk. Identification of the presence of painful cold within 2 weeks after surgery as a strong predictor supports altered pain processing as an important contributor to CPSP development.

The Diurnal Profile of Human Basal Pain Sensitivity and Skin Sympathetic Nerve Activity: A Healthy Volunteer Study

Objective

The diurnal rhythm profile of human basal pain sensitivity and its association with sympathetic nerve activity are not fully understood. This study aimed to examine rhythmic changes in experimental pain sensitivity and skin sympathetic nerve activity in healthy volunteers.

Methods

Thirty healthy volunteers were included in the study. Experimental pain sensitivity, including pressure pain threshold and tolerance, cold pain threshold (CPT) and tolerance, skin sympathetic nerve activity, and cardiovascular parameters (including heart rate, cardiac output, and peripheral vascular resistance) at six time points throughout the day (08:00, 12:00, 16:00, 20:00, 00:00, and 04:00) were sequentially measured. Circadian rhythm analysis was performed on the mean values of the different measurements and individual subjects.

Results

Significant differences were found in experimental pain sensitivity, skin sympathetic nerve activity, and non-invasive cardiovascular parameters at different time points (P < 0.05). The minimum measured values of all four types of experimental pain sensitivity were consistently observed at 04:00. Rhythmical analysis showed that the mean values of pressure pain threshold (meta2d P = 0.016) and skin sympathetic nerve activity (meta2d P = 0.039) were significant. Significant diurnal rhythms in pain sensitivity and skin sympathetic nerve activity existed in some individuals but not in others. No significant correlation between experimental pain sensitivity and skin sympathetic nerve activity was found at any time point (P > 0.05).

Conclusion

Significant diurnal fluctuations were observed in different pain sensitivities and skin sympathetic nerve activity. No significant correlation between experimental pain sensitivity and sympathetic excitability at different times was found; the reasons for these phenomena remain to be further studied.

Clinical Trial Registration

[www.ClinicalTrials.gov], identifier [ChiCTR2000039709].

Predicting pain among female survivors of recent interpersonal violence: A proof-of-concept machine-learning approach

Interpersonal violence (IPV) is highly prevalent in the United States and is a major public health problem. The emergence and/or worsening of chronic pain are known sequelae of IPV; however, not all those who experience IPV develop chronic pain. To mitigate its development, it is critical to identify the factors that are associated with increased risk of pain after IPV. This proof-of-concept study used machine-learning strategies to predict pain severity and interference in 47 young women, ages 18 to 30, who experienced an incident of IPV (i.e., physical and/or sexual assault) within three months of their baseline assessment. Young women are more likely than men to experience IPV and to subsequently develop posttraumatic stress disorder (PTSD) and chronic pain. Women completed a comprehensive assessment of theory-driven cognitive and neurobiological predictors of pain severity and pain-related interference (e.g., pain, coping, disability, psychiatric diagnosis/symptoms, PTSD/trauma, executive function, neuroendocrine, and physiological stress response). Gradient boosting machine models were used to predict symptoms of pain severity and pain-related interference across time (Baseline, 1-,3-,6- follow-up assessments). Models showed excellent predictive performance for pain severity and adequate predictive performance for pain-related interference. This proof-of-concept study suggests that machine-learning approaches are a useful tool for identifying predictors of pain development in survivors of recent IPV. Baseline measures of pain, family life impairment, neuropsychological function, and trauma history were of greatest importance in predicting pain and pain-related interference across a 6-month follow-up period. Present findings support the use of machine-learning techniques in larger studies of post-IPV pain development and highlight theory-driven predictors that could inform the development of targeted early intervention programs. However, these results should be replicated in a larger dataset with lower levels of missing data.

Cold pain hypersensitivity predicts trajectories of pain and disability after low back surgery: a prospective cohort study

Improving the ability to predict persistent pain after spine surgery would allow identification of patients at risk and guide treatment decisions. Quantitative sensory tests (QST) are measures of altered pain processes, but in our previous study, preoperative QST did not predict pain and disability at single time-points. Trajectory analysis accounts for time-dependent patterns. We hypothesized that QST predict trajectories of pain and disability during 1 year after low back surgery. We performed a trajectory analysis on the cohort of our previous study (n = 141). Baseline QST included electrical, pressure, heat, and cold stimulation of the low back and lower extremity, temporal summation, and conditioned pain modulation. Pain intensity and Oswestry Disability Index were measured before, and 2, 6, and 12 months after surgery. Bivariate trajectories for pain and disability were computed using group-based trajectory models. Multivariable regressions were used to identify QST as predictors of trajectory groups, with sociodemographic, psychological, and clinical characteristics as covariates. Cold pain hypersensitivity at the leg, not being married, and long pain duration independently predicted worse recovery (complete-to-incomplete, incomplete-to-no recovery). Cold pain hypersensitivity increased the odds for worse recovery by 3.8 (95% confidence intervals 1.8-8.0, P < 0.001) and 3.0 (1.3-7.0, P = 0.012) in the univariable and multivariable analyses, respectively. Trajectory analysis, but not analysis at single time-points, identified cold pain hypersensitivity as strong predictor of worse recovery, supporting altered pain processes as predisposing factor for persisting pain and disability, and a broader use of trajectory analysis. Assessment of cold pain sensitivity may be a clinically applicable, prognostic test.

Data-science-based subgroup analysis of persistent pain during 3 years after breast cancer surgery: A prospective cohort study

BACKGROUND

Persistent pain extending beyond 6 months after breast cancer surgery when adjuvant therapies have ended is a recognised phenomenon. The evolution of postsurgery pain is therefore of interest for future patient management in terms of possible prognoses for distinct groups of patients to enable better patient information.

OBJECTIVE(S)

An analysis aimed to identify subgroups of patients who share similar time courses of postoperative persistent pain.

DESIGN

Prospective cohort study.

SETTING

Helsinki University Hospital, Finland, between 2006 and 2010.

PATIENTS

A total of 763 women treated for breast cancer at the Helsinki University Hospital.

INTERVENTIONS

Employing a data science approach in a nonredundant reanalysis of data published previously, pain ratings acquired at 6, 12, 24 and 36 months after breast cancer surgery, were analysed for a group structure of the temporal courses of pain. Unsupervised automated evolutionary (genetic) algorithms were used for patient cluster detection in the pain ratings and for Gaussian mixture modelling of the slopes of the linear relationship between pain ratings and acquisition times.

MAIN OUTCOME MEASURES

Clusters or groups of patients sharing patterns in the time courses of pain between 6 and 36 months after breast cancer surgery.

RESULTS

Three groups of patients with distinct time courses of pain were identified as the best solutions for both clustering of the pain ratings and multimodal modelling of the slopes of their temporal trends. In two clusters/groups, pain decreased or remained stable and the two approaches suggested/identified similar subgroups representing 80/763 and 86/763 of the patients, respectively, in whom rather high pain levels tended to further increase over time.

CONCLUSION

In the majority of patients, pain after breast cancer surgery decreased rapidly and disappeared or the intensity decreased over 3 years. However, in about a tenth of patients, moderate-to-severe pain tended to increase during the 3-year follow-up.

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Background

Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients.

Objective

This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients.

Methods

PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices.

Results

PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%.

Conclusions

Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia.

Trial Registration

Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638

Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain

The genetic background of pain is becoming increasingly well understood, which opens up possibilities for predicting the individual risk of persistent pain and the use of tailored therapies adapted to the variant pattern of the patient's pain-relevant genes. The individual variant pattern of pain-relevant genes is accessible via next-generation sequencing, although the analysis of all "pain genes" would be expensive. Here, we report on the development of a cost-effective next generation sequencing-based pain-genotyping assay comprising the development of a customized AmpliSeq™ panel and bioinformatics approaches that condensate the genetic information of pain by identifying the most representative genes. The panel includes 29 key genes that have been shown to cover 70% of the biological functions exerted by a list of 540 so-called "pain genes" derived from transgenic mice experiments. These were supplemented by 43 additional genes that had been independently proposed as relevant for persistent pain. The functional genomics covered by the resulting 72 genes is particularly represented by mitogen-activated protein kinase of extracellular signal-regulated kinase and cytokine production and secretion. The present genotyping assay was established in 61 subjects of Caucasian ethnicity and investigates the functional role of the selected genes in the context of the known genetic architecture of pain without seeking functional associations for pain. The assay identified a total of 691 genetic variants, of which many have reports for a clinical relevance for pain or in another context. The assay is applicable for small to large-scale experimental setups at contemporary genotyping costs.

Machine-learned analysis of the association of next-generation sequencing-based genotypes with persistent pain after breast cancer surgery

Cancer and its surgical treatment are among the most important triggering events for persistent pain, but additional factors need to be present for the clinical manifestation, such as variants in pain-relevant genes. In a cohort of 140 women undergoing breast cancer surgery, assigned based on a 3-year follow-up to either a persistent or nonpersistent pain phenotype, next-generation sequencing was performed for 77 genes selected for known functional involvement in persistent pain. Applying machine-learning and item categorization techniques, 21 variants in 13 different genes were found to be relevant to the assignment of a patient to either the persistent pain or the nonpersistent pain phenotype group. In descending order of importance for correct group assignment, the relevant genes comprised DRD1, FAAH, GCH1, GPR132, OPRM1, DRD3, RELN, GABRA5, NF1, COMT, TRPA1, ABHD6, and DRD4, of which one in the DRD4 gene was a novel discovery. Particularly relevant variants were found in the DRD1 and GPR132 genes, or in a cis-eCTL position of the OPRM1 gene. Supervised machine-learning-based classifiers, trained with 2/3 of the data, identified the correct pain phenotype group in the remaining 1/3 of the patients at accuracies and areas under the receiver operator characteristic curves of 65% to 72%. When using conservative classical statistical approaches, none of the variants passed α-corrected testing. The present data analysis approach, using machine learning and training artificial intelligences, provided biologically plausible results and outperformed classical approaches to genotype-phenotype association.

Chemotherapy-induced peripheral neuropathy: where are we now?

Chemotherapy-induced peripheral neuropathy (CIPN) is a major challenge, with increasing impact as oncological treatments, using potentially neurotoxic chemotherapy, improve cancer cure and survival. Acute CIPN occurs during chemotherapy, sometimes requiring dose reduction or cessation, impacting on survival. Around 30% of patients will still have CIPN a year, or more, after finishing chemotherapy. Accurate assessment is essential to improve knowledge around prevalence and incidence of CIPN. Consensus is needed to standardize assessment and diagnosis, with use of well-validated tools, such as the EORTC-CIPN 20. Detailed phenotyping of the clinical syndrome moves toward a precision medicine approach, to individualize treatment. Understanding significant risk factors and pre-existing vulnerability may be used to improve strategies for CIPN prevention, or to use targeted treatment for established CIPN. No preventive therapies have shown significant clinical efficacy, although there are promising novel agents such as histone deacetylase 6 (HDAC6) inhibitors, currently in early phase clinical trials for cancer treatment. Drug repurposing, eg, metformin, may offer an alternative therapeutic avenue. Established treatment for painful CIPN is limited. Following recommendations for general neuropathic pain is logical, but evidence for agents such as gabapentinoids and amitriptyline is weak. The only agent currently recommended by the American Society of Clinical Oncology is duloxetine. Mechanisms are complex with changes in ion channels (sodium, potassium, and calcium), transient receptor potential channels, mitochondrial dysfunction, and immune cell interactions. Improved understanding is essential to advance CIPN management. On a positive note, there are many potential sites for modulation, with novel analgesic approaches.

Impaired hemodynamic activity in the right dorsolateral prefrontal cortex is associated with impairment of placebo analgesia and clinical symptoms in postherpetic neuralgia

The dorsolateral prefrontal cortex (dlPFC) is functionally linked to the descending pain modulation system and has been implicated in top down pain inhibition, including placebo analgesia. Therefore, functions of the dlPFC may be impaired in patients with chronic pain. Postherpetic neuralgia (PHN) is one of several syndromes with chronic neuropathic pain. In the present study, we investigated possible dysfunction of the dlPFC in chronic pain using patients with PHN. In a conditioning phase, heathy controls (n = 15) and patients with PHN (n = 7) were exposed to low (LF) and high (HF) frequency tones associated with noxious stimuli: weak (WS) and strong (SS) electrical stimulation, respectively. After the conditioning, cerebral hemodynamic activity was recorded from the bilateral dlPFC while the subjects were subjected to the cue tone-noxious electrical stimulation paradigm, in which incorrectly cued noxious stimuli were sometimes delivered to induce placebo and nocebo effects. The results indicated that hemodynamic responses to the LF tone in the right dlPFC was significantly lower in patients with PHN compared to the healthy controls. Furthermore, the same hemodynamic responses in the right dlPFC were correlated with placebo effects. In addition, clinical symptoms of PHN were negatively correlated to cerebral hemodynamic responses in the right dlPFC and magnitudes of the placebo effects. The results suggest that the right dlPFC, which is closely associated with the descending pain modulation system, is disturbed in PHN.

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

Machine-learned analysis of global and glial/opioid intersection-related DNA methylation in patients with persistent pain after breast cancer surgery

BACKGROUND

Glial cells in the central nervous system play a key role in neuroinflammation and subsequent central sensitization to pain. They are therefore involved in the development of persistent pain. One of the main sites of interaction of the immune system with persistent pain has been identified as neuro-immune crosstalk at the glial-opioid interface. The present study examined a potential association between the DNA methylation of two key players of glial/opioid intersection and persistent postoperative pain.

METHODS

In a cohort of 140 women who had undergone breast cancer surgery, and were assigned based on a 3-year follow-up to either a persistent or non-persistent pain phenotype, the role of epigenetic regulation of key players in the glial-opioid interface was assessed. The methylation of genes coding for the Toll-like receptor 4 (TLR4) as a major mediator of glial contributions to persistent pain or for the μ-opioid receptor (OPRM1) was analyzed and its association with the pain phenotype was compared with that conferred by global genome-wide DNA methylation assessed via quantification of the methylation in the retrotransposon LINE1.

RESULTS

Training of machine learning algorithms indicated that the global DNA methylation provided a similar diagnostic accuracy for persistent pain as previously established non-genetic predictors. However, the diagnosis can be based on a single DNA based marker. By contrast, the methylation of TLR4 or OPRM1 genes could not contribute further to the allocation of the patients to the pain-related phenotype groups.

CONCLUSIONS

While clearly supporting a predictive utility of epigenetic testing, the present analysis cannot provide support for specific epigenetic modulation of persistent postoperative pain via methylation of two key genes of the glial-opioid interface.

Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis

This study aims to develop a new postoperative pain assessment model based on pulse contour analysis and to evaluate its effectiveness in postoperative pain assessment. We derived candidate features from photoplethysmography (PPG) and developed an assessment model based on multiple logistic regressions with a combination of features. This study also includes investigations into the optimal unit of analysis and number of features. For model development, PPGs obtained from 78 surgical patients with a six-min duration in pre- and post-operation conditions, including a training set of 56 pairs and a test set of 22 pairs, were used. We tested models with 5, 10, 20, 30, 40, 50, 60, 70, and 80 beats as an analysis unit, and with 1 to 8 of features for optimization, then determined 20 beats and three features to be the simplest optimal unit of analysis and number of features, respectively. The selected features were RMSSD-ACV_onset/ACA_bl, AV-A_sys/A_total, and SD-RS, where RMSSD-ACV_onset/ACA_bl is the root mean square of the successive difference of the ratio of pulse onset amplitude to the pulse onset-to-peak amplitude, AV-A_sys/A_total is the average value of a normalized systolic area of a pulse with a total pulse area, and SD-RS is the standard deviation of a rising slope of a pulse. The accuracy (AC) and the area under the curve (AUC) of the proposed model were 0.793 and 0.872 in the development set (N = 56), respectively, which were superior to those of SPI (AC: 0.643, AUC: 0.716) and ANI (AC: 0.633 AUC: 0.671). In the test set (N = 22), the AC and AUC of the proposed model were 0.712 and 0.808, respectively, which were superior to those of SPI (AC: 0.640, AUC: 0.709) and ANI (AC: 0.640, AUC: 0.680).

Machine-learning-derived classifier predicts absence of persistent pain after breast cancer surgery with high accuracy

Background

Prevention of persistent pain following breast cancer surgery, via early identification of patients at high risk, is a clinical need. Supervised machine-learning was used to identify parameters that predict persistence of significant pain.

Methods

Over 500 demographic, clinical and psychological parameters were acquired up to 6 months after surgery from 1,000 women (aged 28–75 years) who were treated for breast cancer. Pain was assessed using an 11-point numerical rating scale before surgery and at months 1, 6, 12, 24, and 36. The ratings at months 12, 24, and 36 were used to allocate patents to either “persisting pain” or “non-persisting pain” groups. Unsupervised machine learning was applied to map the parameters to these diagnoses.

Results

A symbolic rule-based classifier tool was created that comprised 21 single or aggregated parameters, including demographic features, psychological and pain-related parameters, forming a questionnaire with “yes/no” items (decision rules). If at least 10 of the 21 rules applied, persisting pain was predicted at a cross-validated accuracy of 86% and a negative predictive value of approximately 95%.

Conclusions

The present machine-learned analysis showed that, even with a large set of parameters acquired from a large cohort, early identification of these patients is only partly successful. This indicates that more parameters are needed for accurate prediction of persisting pain. However, with the current parameters it is possible, with a certainty of almost 95%, to exclude the possibility of persistent pain developing in a woman being treated for breast cancer.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-4841-8) contains supplementary material, which is available to authorized users.