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 - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Journal of Clinical Medicine Année : 2021

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

Manuel Roulaud
  • Fonction : Auteur
Nicolas Naiditch
Bénédicte Bouche
  • Fonction : Auteur
Bertille Lorgeoux
  • Fonction : Auteur
Sandrine Baron
  • Fonction : Auteur
Brigitte Roy-Moreau
  • Fonction : Auteur
Nelly Grimaud
  • Fonction : Auteur
Nihel Adjali
  • Fonction : Auteur
Kevin Nivole
  • Fonction : Auteur
Mathilde Many
  • Fonction : Auteur
Romain David
  • Fonction : Auteur
Chantal Wood
  • Fonction : Auteur
Maarten Moens
  • Fonction : Auteur
Maxime Billot

Résumé

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.
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Dates et versions

hal-03343619 , version 1 (27-09-2023)

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Philippe Rigoard, Amine Ounajim, Lisa Goudman, Pierre-Yves Louis, Yousri Slaoui, et al.. 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. Journal of Clinical Medicine, 2021, 10 (21), pp.4910. ⟨10.3390/jcm10214910⟩. ⟨hal-03343619⟩
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