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

Rigoard, P.; Ounajim, A.; Goudman, L.; Louis, P.-Y.; Slaoui, Y.; Roulaud, M.; Naiditch, N.; Bouche, B.; Page, P.; Lorgeoux, B.; Baron, S.; Charrier, E.; Poupin, L.; Rannou, D.; de Montgazon, G.B.; Roy-Moreau, B.; Grimaud, N.; Adjali, N.; Nivole, K.; Many, M.; David, R.; Wood, C.; Rigoard, R.; Moens, M.; Billot, M. 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. J. Clin. Med. 2021, 10, 4910. Rigoard, P.; Ounajim, A.; Goudman, L.; Louis, P.-Y.; Slaoui, Y.; Roulaud, M.; Naiditch, N.; Bouche, B.; Page, P.; Lorgeoux, B.; Baron, S.; Charrier, E.; Poupin, L.; Rannou, D.; de Montgazon, G.B.; Roy-Moreau, B.; Grimaud, N.; Adjali, N.; Nivole, K.; Many, M.; David, R.; Wood, C.; Rigoard, R.; Moens, M.; Billot, M. 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. J. Clin. Med. 2021, 10, 4910.

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.

Keywords

Composite score; Machine learning; PSPS; Failed Back Surgery Syndrome (FBSS); Chronic pain; Pain Intensity; Quality of Life; Pain Mapping; Pain Surface; Functional Capacity; Psychological Distress; Anxiety and Depression

Subject

Medicine and Pharmacology, Anesthesiology and Pain Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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