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

Abstract

The multidimensionality of chronic pain forces us to look beyond isolated pain assessment such as pain intensity, which does not consider multiple key parameters, particularly in patients suffering from post-operative Persistent Spinal Pain Syndrome (PSPS-T2). Our ambition was to provide a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and objective quantitative pain mapping assessments, the objective being to capture patient condition instantaneously, using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in a 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 using exploratory factor analyses to create an optimally accurate MCRI; as a single composite index, using pairwise correlations between measurements, it appeared to better represent all pain dimensions than any other classical score. It appeared to be 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 changing the physician’s perception of patient condition on the basis of objective and holistic metrics, and by providing new insights to therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.

Finite Mixture Models Based on Pain Intensity, Functional Disability and Psychological Distress Composite Assessment Allow Identification of Two Distinct Classes of Persistent Spinal Pain Syndrome after Surgery Patients Related to Their Quality of Life

“…Regarding spinal cord stimulation, as an example, national and international guidelines recommend permanent implantation of neurostimulation devices based on a 30% or a 50% pain decrease during the trial period [37][38][39], but this can only reflect an angle of pain assessment prism [40,41]. Harmful consequences of focusing exclusively on pain intensity have been observed by Ballantyne and Sullivan [30], who contended that the multimodal pain management approaches proposed to chronic pain patients should include behavioral and physical-rehabilitation, which cannot be adequately evaluated using only pain intensity measures [41][42][43]. In a review [32], these authors claimed that it is not necessary to reduce pain intensity systematically in order to achieve adequate chronic pain management.…”

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

“…Based on these assessments, guidelines recommend that a refractory PSPS patient can be eligible to permanent implant, if a 30-50% of pain decrease is observed during the trial [14,16,17,19]. It appears that this unidimensional modality of pain assessment can no longer be considered as the only gold standard to delineate the implanting physician's guidelines and international recommendations, since this would reflect only one dimension of the patient's quality of life, needs and expectations [23]. While composite multidimensional pain indexes, following the application of pain therapy on chronic refractory patients [23], would help to capture the essence of pain substrate and pain potential relief, they are not currently part of the recommended pain assessment toolbox.…”

“…It appears that this unidimensional modality of pain assessment can no longer be considered as the only gold standard to delineate the implanting physician's guidelines and international recommendations, since this would reflect only one dimension of the patient's quality of life, needs and expectations [23]. While composite multidimensional pain indexes, following the application of pain therapy on chronic refractory patients [23], would help to capture the essence of pain substrate and pain potential relief, they are not currently part of the recommended pain assessment toolbox. -Third, it appears that patient selection, final implantation ratio and patient outcomes, WITH OR WITHOUT lead trial, are similar [24].…”

“…Using our experience of multidimensional composite pain assessment and previous predictive research [15,23], we hypothesized that machine learning models, based on homogeneous data pooled from several multicenter studies, could be used as robust screening tools to predict SCS efficacy. To test this hypothesis, we developed, for the purpose of this study, several algorithms designed to confront model predictions and to compare our model performances to lead trial results among 103 implanted patients.…”

The Challenge of Converting “Failed Spinal Cord Stimulation Syndrome” Back to Clinical Success, Using SCS Reprogramming as Salvage Therapy, Through Neurostimulation Adapters Combined with 3D-Computerized Pain Mapping Assessment. A Real-Life Retrospective Cohort Analysis

“…In addition, our team has created a 3D-computerized triple-patented pain mapping tool [22], to be able to document precise paresthesia coverage in cm², pain surface and pain typology changes within time, using objective quantitative metrics, via a tactile interface. These quantitative measurements can be correlated to classical pain assessment tools, such as pain intensity, functional incapacity, quality of life, and patient satisfaction, to build a multi-dimensional composite index [23], aimed at assessing pain, therapy efficacy and patient outcomes in a holistic manner. Finally, a new proposal for a salvage algorithm was presented and will be discussed, hereafter, as a synthesis of the discussion.…”