Multivariate Prognostic Modeling of PersistentPain Following Lumbar Discectomy
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Résumé
BACKGROUND: Persistent postsurgical pain (PPSP) affects between 10% and 50% of surgical patients, the development of which is a complex and poorly understood process. To date, most studies on PPSP have focused on specific surgical procedures where individuals do not suffer from chronic pain before the surgical intervention. Individuals who have a chronic nerve injury are likely to have established peripheral and central sensitization which may increase the risk of developing PPSP. Concurrent analyses of the possible factors contributing to the development of PPSP following lumbar discectomy have not been examined. OBJECTIVE: The aim of this study is to identify risk and protective factors that predict the course of recovery following lumbar discectomy and to develop an easily applicable preoperative multivariate prognostic model for the occurrence of PPSP in this patient cohort. STUDY DESIGN: A prospective study of elective lumbar discectomy with a 3 month follow-up. SETTING: University setting in Ireland. METHODS: All ASA I-II patients, (n = 53, 18-65 years old), undergoing elective lumbar discectomy at a single institute were included and followed for a 3 month period postsurgery. Preoperative potential predictors were collected: age, gender, pain intensity (McGill score, visual analog scale [VAS], Present Pain Intensity), degree of dysfunction (Roland-Morris Function score), psychological status (pain catastrophizing, anxiety, and depression scores), health-related quality of life (SF-36), quantitative sensory testing (QST), inflammatory biomarkers, and a genetic pain profile. The proposed primary outcome was significant pain reduction (VAS > 70%) 3 months following surgery compared to the preoperative pain intensity. RESULTS: A final prediction model was obtained using a multivariate logistic regression in combination with bootstrapping techniques for internal validation. Twenty (37.7%) patients developed PPSP. Independent predictor factors included age (odds ratio [OR] = 1.0 per year), present pain intensity (OR = 0.6), and degree of dysfunction (OR = 1.2). The concordance index C (.658) supports a good monotonic association (where perfect prediction is 1) and the Akaike's information criteria indicated a good fit of the model. Inclusion of additional measured parameters (QST, biomarker, or genotyping) did not improve the model. LIMITATIONS: Before this internally validated model can be integrated into clinical practice, and used for patient counselling and quality assurance purposes, external validation studies are necessary. CONCLUSIONS: We demonstrated that the occurrence of PPSP can be predicted using a small set of variables easily obtained at the preoperative visit. This a prediction rule that could further optimize perioperative pain treatment and reduce attendant complications by allowing the preoperative classification of surgical patients according to their risk of developing PPSP.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle