Machine Learning Algorithms Provide Greater Prediction of Response to SCS Than Lead Screening Trial: A Predictive AI-Based Multicenter Study

The Role and Applications of Artificial Intelligence in the Treatment of Chronic Pain

PURPOSE OF REVIEW

This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.

RECENT FINDINGS

In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Neurostimulation in the patient with chronic pain: forecasting the future with data from the present - data-driven analysis or just dreams?

Chronic pain involves a structured and individualized development of neurophysiological and biological responses. The final expression in each patient correlates with diverse expressions of mediators and activations of different transmission and modulation pathways, as well as alterations in the structure and function of the brain, all of which develop according to the pain phenotype. Still today, the selection process for the ideal candidate for spinal cord stimulation (SCS) is based on results from test and functional variables analysis as well as pain evaluation. In addition to the difficulties in the initial selection of patients and the predictive analysis of the test phase, which undoubtedly impact on the results in the middle and long term, the rate of explants is one of the most important concerns, in the analysis of suitability of implanted candidates. A potential for useful integration of genome analysis and lymphocyte expression in the daily practice of neurostimulation, for pain management is presented. Structural and functional quantitative information provided by imaging biomarkers will allow establishing a clinical decision support system that improve the effectiveness of the SCS implantation, optimizing human, economic and psychological resources. A correct programming of the neurostimulator, as well as other factors associated with the choice of leads and their position in the epidural space, are the critical factors for the effectiveness of the therapy. Using a model of SCS based on mathematical methods and computational simulation, the effect of different factors of influence on clinical practice studied, as several configurations of electrodes, position of these, and programming of polarities, in order to draw conclusions of clinical utility in neuroestimulation therapy.

Machine Learning in Spinal Cord Stimulation for Chronic Pain

Spinal cord stimulation (SCS) is an effective treatment for chronic neuropathic pain. The success of SCS is dependent on candidate selection, response to trialing, and programming optimization. Owing to the subjective nature of these variables, machine learning (ML) offers a powerful tool to augment these processes. Here we explore what work has been done using data analytics and applications of ML in SCS. In addition, we discuss aspects of SCS which have narrowly been influenced by ML and propose the need for further exploration. ML has demonstrated a potential to complement SCS to an extent ranging from assistance with candidate selection to replacing invasive and costly aspects of the surgery. The clinical application of ML in SCS shows promise for improving patient outcomes, reducing costs of treatment, limiting invasiveness, and resulting in a better quality of life for the patient.

Research hotspots and trends on spinal cord stimulation for pain treatment: a two-decade bibliometric analysis

Background

Chronic pain poses a significant social burden. Spinal cord stimulation (SCS) is considered to be the most promising treatment for refractory pain. The aim of this study was to summarize the current research hotspots on SCS for pain treatment during the past two decades and to predict the future research trends by bibliometric analysis.

Methods

The literature over the last two decades (2002-2022) which was related to SCS in pain treatment was obtained from the Web of Science Core Collection. Bibliometric analyses were conducted based on the following aspects: (1) Annual publication and citation trends; (2) Annual publication changes of different publication types; (3) Publications and citations/co-citations of different country/institution/journal/author; (4) Citations/co-citation and citation burst analysis of different literature; and (5) Co-occurrence, cluster, thematic map, trend topics, and citation burst analysis of different keywords. (6) Comparison between the United States and Europe. All analyses were performed on CiteSpace, VOSviewer, and R bibliometrix package.

Results

A total of 1,392 articles were included in this study, with an increasing number of publications and citations year by year. The most highly published type of literature was clinical trial. United States was the country with the most publications and citations; Johns Hopkins University was the institution with the most publications; NEUROMODULATION published the most papers; the most published author was Linderoth B; and the most cited paper was published in the PAIN by Kumar K in 2007. The most frequently occurring keywords were "spinal cord stimulation," "neuropathic pain," and "chronic pain," etc.

Conclusion

The positive effect of SCS on pain treatment has continued to arouse the enthusiasm of researchers in this field. Future research should focus on the development of new technologies, innovative applications, and clinical trials for SCS. This study might facilitate researchers to comprehensively understand the overall perspective, research hotspots, and future development trends in this field, as well as seek collaboration with other researchers.

Combining Awake Anesthesia with Minimal Invasive Surgery Optimizes Intraoperative Surgical Spinal Cord Stimulation Lead Placement

Spinal cord stimulation (SCS) is an effective and validated treatment to address chronic refractory neuropathic pain in persistent spinal pain syndrome-type 2 (PSPS-T2) patients. Surgical SCS lead placement is traditionally performed under general anesthesia due to its invasiveness. In parallel, recent works have suggested that awake anesthesia (AA), consisting of target controlled intra-venous anesthesia (TCIVA), could be an interesting tool to optimize lead anatomical placement using patient intra-operative feedback. We hypothesized that combining AA with minimal invasive surgery (MIS) could improve SCS outcomes. The goal of this study was to evaluate SCS lead performance (defined by the area of pain adequately covered by paraesthesia generated via SCS), using an intraoperative objective quantitative mapping tool, and secondarily, to assess pain relief, functional improvement and change in quality of life with a composite score. We analyzed data from a prospective multicenter study (ESTIMET) to compare the outcomes of 115 patients implanted with MIS under AA (MISAA group) or general anesthesia (MISGA group), or by laminectomy under general anesthesia (LGA group). All in all, awake surgery appears to show significantly better performance than general anesthesia in terms of patient pain coverage (65% vs. 34–62%), pain surface (50–76% vs. 50–61%) and pain intensity (65% vs. 35–40%), as well as improved secondary outcomes (quality of life, functional disability and depression). One step further, our results suggest that MISAA combined with intra-operative hypnosis could potentialize patient intraoperative cooperation and could be proposed as a personalized package offered to PSPS-T2 patients eligible for SCS implantation in highly dedicated neuromodulation centers.

Study on Automatic Multi-Classification of Spine Based on Deep Learning and Postoperative Infection Screening

The preoperative qualitative and hierarchical diagnosis of intervertebral foramen stenosis is very important for clinicians to explore the effect of multimodal analgesia nursing on pain control after spinal fusion and to formulate treatment strategies and patients' health recovery. However, there are still many problems in this aspect, and there is a lack of relevant research and effective methods to assist clinicians in diagnosis. Therefore, to improve the accuracy of computer-aided diagnosis of intervertebral foramen stenosis and the work efficiency of doctors, a deep learning automatic grading algorithm of intervertebral foramen stenosis image is proposed in this study. The image of intervertebral foramen was extracted from the MRI image of sagittal spine, and the image was preprocessed. 86 patients with spinal fusion treated in our hospital, specifically from May 2018 to May 2020, were randomly divided into the control group (routine analgesic nursing) and the multimodal group (multimodal analgesic nursing), with 43 cases in each group. The pain control effect and satisfaction of the two groups were observed. The results after multimodal analgesia nursing show that the VASs of the multimodal group at different time points were significantly lower than those of the control group (P < 0.05); the satisfaction score of pain control in the multimodal group was significantly higher than that in the control group (P < 0.05). Multimodal analgesia nursing for patients undergoing spinal fusion can effectively reduce the degree of postoperative pain and improve the effect of pain control and satisfaction with pain control, which is worthy of promotion.

PRESENTATION OF THE MULTICENTRIC COLLECTION SPINE SURGERY DATABASE

ABSTRACT Objectives: To present the Spine Surgery Database developed by the Brazilian Spine Study Group and the methodology involved in its creation, in addition to presenting initial informationabout the use of the database. Methods: Description of the steps for selecting the questionnaires and variables to be included in the database, initial expansion of the use of the database tophysicians close to the BSSG, a brief exploration of quality control and methods for the inclusion ofnew centers, and training on the database, in addition to a brief descriptionof some of the data included in the database. Results: Currently, the database includes 428 patients who already underwent spine surgery and 9 collection centers with at least one patient collected. Conclusion: The Brazilian Spine Study Group’s Multicentric Collection Database is a viable tool that allows patients from different sourcesto be included within a common flow. Level of Evidence V; Expert opinion.

A Novel Multi-Dimensional Clinical Response Index Dedicated to Improving Global Assessment of Pain in Patients with Persistent Spinal Pain Syndrome after Spinal Surgery, Based on a Real-Life Prospective Multicentric Study (PREDIBACK) and Machine Learning Techniques

The multidimensionality of chronic pain forces us to look beyond isolated assessment such as pain intensity, which does not consider multiple key parameters, particularly in post-operative Persistent Spinal Pain Syndrome (PSPS-T2) patients. Our ambition was to produce a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and quantitative pain mapping, the objective being to achieve instantaneous assessment using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in the real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined, as exploratory factor analyses helped to create a single composite MCRI; using pairwise correlations between measurements, it appeared to more accurately represent all pain dimensions than any previous classical score. It represented the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by informing the physician's perception of patient condition on the basis of objective and holistic metrics, and also by providing new insights regarding therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.