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

• Published in: Journal of clinical medicine Vol. 10; no. 21; p. 4910
• Main Authors: Rigoard, Philippe, Ounajim, Amine, Goudman, Lisa, Louis, Pierre-Yves, Slaoui, Yousri, Roulaud, Manuel, Naiditch, Nicolas, Bouche, Bénédicte, Page, Philippe, Lorgeoux, Bertille, Baron, Sandrine, Charrier, Elodie, Poupin, Laure, Rannou, Delphine, de Montgazon, Géraldine Brumauld, Roy-Moreau, Brigitte, Grimaud, Nelly, Adjali, Nihel, Nivole, Kevin, Many, Mathilde, David, Romain, Wood, Chantal, Rigoard, Raphael, Moens, Maarten, Billot, Maxime
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 24.10.2021; MDPI
• Subjects: Anxiety; Artificial intelligence; Back surgery; chronic pain; composite score; failed back surgery syndrome (FBSS); Life Sciences; Machine learning; Mathematics; Pain; pain intensity; Patients; PSPS; Quality of life; Questionnaires; Spinal cord; Statistics; composite score; chronic pain; Anxiety and depression; PSPS; functional capacity; pain surface; failed back surgery (FBSS); pain intensity; machine learning; pain mapping; psychological distress

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...