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

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 Clini...

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Autores principales: Philippe Rigoard, Amine Ounajim, Lisa Goudman, Pierre-Yves Louis, Yousri Slaoui, Manuel Roulaud, Nicolas Naiditch, Bénédicte Bouche, Philippe Page, Bertille Lorgeoux, Sandrine Baron, Elodie Charrier, Laure Poupin, Delphine Rannou, Géraldine Brumauld de Montgazon, Brigitte Roy-Moreau, Nelly Grimaud, Nihel Adjali, Kevin Nivole, Mathilde Many, Romain David, Chantal Wood, Raphael Rigoard, Maarten Moens, Maxime Billot
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
composite score
machine learning
PSPS
failed back surgery syndrome (FBSS)
chronic pain
pain intensity
Medicine
R
Acceso en línea:https://doaj.org/article/7b9d4f30a3394165981db74c74e948e2
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Sumario: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.

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