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Enregistrement W3210668104 · doi:10.1097/corr.0000000000002024

How Is the Probability of Reporting Various Levels of Pain 12 Months After Noncatastrophic Injuries Associated with the Level of Peritraumatic Distress?

2021· article· en· W3210668104 sur OpenAlexaffabout
Shirin Modarresi, Joy C. MacDermid, Nina Suh, James M. Elliott, David M. Walton

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2021
Typearticle
Langueen
DomainePsychology
ThématiquePosttraumatic Stress Disorder Research
Établissements canadiensMcMaster UniversitySt Joseph's Health CareWestern University
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human Development
Mots-clésMedicineDistressPhysical therapyEtiologyProspective cohort studyInjury preventionCohort studyPoison controlOccupational safety and healthCohortPsychiatryEmergency medicineInternal medicineClinical psychology

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Psychological factors have been shown to be consistent predictors of chronic pain in people with musculoskeletal injuries. However, few prognostic studies have considered multiple risk factors including peritraumatic distress. In addition, previous research has not considered that the associations between peritraumatic distress and pain levels can vary across pain outcomes. QUESTION/PURPOSE: To determine whether an easily measurable level of baseline distress is associated with pain levels 1 year after noncatastrophic traumatic injuries when the outcome to be assessed is not normally distributed. METHODS: This was a prospective cohort study. The data were captured from two cohorts in London, Ontario, Canada, and Chicago, IL, USA. Participants were adults with acute noncatastrophic (that is, not treated with surgery or hospitalization) musculoskeletal injuries of any etiology with various injury locations (60% [145 of 241] spinal and 40% [96 of 241] peripheral) that presented to local urgent care centers. Other inclusion criteria included English/French speakers and having no other disorder that would affect their pain levels. In total, between the years 2015 and 2018, 241 participants were recruited based on the inclusion criteria. Ninety-three percent (225 of 241) of participants provided baseline data, and of these, 48% (109 of 225) were lost to follow-up in 1 year. Based on a complete case approach, this study included 116 participants who ranged in age from 18 to 66 years and 69% (80) of whom were women. The Traumatic Injuries Distress Scale (TIDS) was used to evaluate distress at baseline (within 4 weeks of injury). The TIDS is a validated, reliable 12-item risk prognosis screening tool that takes less than 3 minutes to complete with questions regarding uncontrolled pain, negative affect, and intrusion/hyperarousal. The minimum and maximum possible scores are 0 and 24, with higher scores indicating higher levels of distress. The Numeric Pain Rating Scale (NPRS) was used to assess the pain level at baseline and again 12 months postinjury. To complete the NPRS, participants rate the severity of their pain on a scale of 0 to 10, with 0 indicating no pain and 10 indicating the worst pain imaginable. NPRS scores of 1 to 3 indicate mild pain, 4 to 6 indicate moderate pain and 7 or higher indicate severe pain. As a preliminary analysis, to assess whether the participants as a group experienced recovery, a paired samples t-test was used to compare NPRS scores at baseline and 12 months. In all, 52% (60 of 116) of participants reported no pain (NPRS = 0), and mean pain intensity scores improved from 4.8 ± 2.1 at baseline to 1.6 ± 1.6 at 12-month follow-up (p < 0.001). Quantile regression was used to describe the association between baseline distress and pain levels 1 year after the injury. This technique was used because it reveals the relationships at different quantiles of the pain outcome distribution. RESULTS: The results indicate some people (52% [60 of 116]) recovered regardless of their baseline level of distress (30th quantile of the NPRS: β = 0). However, in those with persisting pain, higher levels of baseline distress are associated with greater levels of pain 12 months after the injury (50th quantile of the NPRS: β = 0.11; p = 0.01; 70th quantile of the NPRS: β = 0.27; p < 0.001; 90th quantile of the NPRS: β = 0.31; p = 0.01). According to this model, with a baseline TIDS score of 5, there is 10% probability that patients will report moderate or greater levels of pain (4 or higher of 10) 12 months later. This probability then increases as the TIDS score increases. For instance, at a score of 13 on the TIDS, the probability of a patient reporting moderate or higher levels of pain increases to 30%, and the probability of reporting severe pain (higher than 6 of 10) is 10%. CONCLUSION: Clinicians and surgeons in orthopaedic settings can screen for the presence of peritraumatic distress using the TIDS, which is an easily administered tool that does not require extensive knowledge of psychology, and by using it they can identify those with higher levels of distress who are more likely to have persistent, long-term pain. A score of 4 or less indicates a low risk of persistent pain, a score between 5 and 12 (endpoints included) indicates moderate risk, and a score of 13 or higher indicates high risk. Future studies should investigate whether certain immediate interventions for peritraumatic distress in the aftermath of trauma can decrease the likelihood that a patient will develop chronic pain after injury. As an analysis technique, quantile regression is useful to assess complex associations in many orthopaedic settings in which a certain outcome is expected to occur in most patients leading to non-normally distributed data. LEVEL OF EVIDENCE: Level II, therapeutic study.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,020
score de la tête « metaresearch » (Gemma)0,026
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,087
Score d'incertitude au seuil0,998

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0200,026
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,005
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,290
Tête enseignante GPT0,450
Écart entre enseignants0,160 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations16
Publié2021
Routes d'admission2
Résumé présentoui

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