Development and validation of a clinical prediction score for poor postoperative pain control following elective spine surgery
Notice bibliographique
Résumé
OBJECTIVE: Thirty percent to sixty-four percent of patients experience poorly controlled pain following spine surgery, leading to patient dissatisfaction and poor outcomes. Identification of at-risk patients before surgery could facilitate patient education and personalized clinical care pathways to improve postoperative pain management. Accordingly, the aim of this study was to develop and internally validate a prediction score for poorly controlled postoperative pain in patients undergoing elective spine surgery. METHODS: A retrospective cohort study was performed in adult patients (≥ 18 years old) consecutively enrolled in the Canadian Spine Outcomes and Research Network registry. All patients underwent elective cervical or thoracolumbar spine surgery and were admitted to the hospital. Poorly controlled postoperative pain was defined as a mean numeric rating scale score for pain at rest of > 4 during the first 24 hours after surgery. Univariable analysis followed by multivariable logistic regression on 25 candidate variables, selected through a systematic review and expert consensus, was used to develop a prediction model using a random 70% sample of the data. The model was transformed into an eight-tier risk-based score that was further simplified into the three-tier Calgary Postoperative Pain After Spine Surgery (CAPPS) score to maximize clinical utility. The CAPPS score was validated using the remaining 30% of the data. RESULTS: Overall, 57% of 1300 spine surgery patients experienced poorly controlled pain during the first 24 hours after surgery. Seven significant variables associated with poor pain control were incorporated into a prediction model: younger age, female sex, preoperative daily use of opioid medication, higher preoperative neck or back pain intensity, higher Patient Health Questionnaire-9 depression score, surgery involving ≥ 3 motion segments, and fusion surgery. Notably, minimally invasive surgery, body mass index, and revision surgery were not associated with poorly controlled pain. The model was discriminative (C-statistic 0.74, 95% CI 0.71-0.77) and calibrated (Hosmer-Lemeshow goodness-of-fit, p = 0.99) at predicting the outcome. Low-, high-, and extreme-risk groups stratified using the CAPPS score had 32%, 63%, and 85% predicted probability of experiencing poorly controlled pain, respectively, which was mirrored closely by the observed incidence of 37%, 62%, and 81% in the validation cohort. CONCLUSIONS: Inadequate pain control is common after spine surgery. The internally validated CAPPS score based on 7 easily acquired variables accurately predicted the probability of experiencing poorly controlled pain after spine surgery.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».