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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2021
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oai:doaj.org-article:7b9d4f30a3394165981db74c74e948e22021-11-11T17:33:15ZA 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 Techniques10.3390/jcm102149102077-0383https://doaj.org/article/7b9d4f30a3394165981db74c74e948e22021-10-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/21/4910https://doaj.org/toc/2077-0383The 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.Philippe RigoardAmine OunajimLisa GoudmanPierre-Yves LouisYousri SlaouiManuel RoulaudNicolas NaiditchBénédicte BouchePhilippe PageBertille LorgeouxSandrine BaronElodie CharrierLaure PoupinDelphine RannouGéraldine Brumauld de MontgazonBrigitte Roy-MoreauNelly GrimaudNihel AdjaliKevin NivoleMathilde ManyRomain DavidChantal WoodRaphael RigoardMaarten MoensMaxime BillotMDPI AGarticlecomposite scoremachine learningPSPSfailed back surgery syndrome (FBSS)chronic painpain intensityMedicineRENJournal of Clinical Medicine, Vol 10, Iss 4910, p 4910 (2021) |
| institution |
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| collection |
DOAJ |
| language |
EN |
| topic |
composite score machine learning PSPS failed back surgery syndrome (FBSS) chronic pain pain intensity Medicine R |
| spellingShingle |
composite score machine learning PSPS failed back surgery syndrome (FBSS) chronic pain pain intensity Medicine R 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 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 |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Philippe Rigoard |
| title |
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 |
| title_short |
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 |
| title_full |
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 |
| title_fullStr |
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 |
| title_full_unstemmed |
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 |
| title_sort |
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 |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/7b9d4f30a3394165981db74c74e948e2 |
| work_keys_str_mv |
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