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Enregistrement W6977478193 · doi:10.7939/r3-yam5-2370

Perioperative opioid demand and risk factors for long-term opioid use among anterior cruciate ligament reconstruction and repair patients in Alberta

2023· dissertation· en· W6977478193 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2023
Typedissertation
Langueen
DomaineMedicine
ThématiqueOpioid Use Disorder Treatment
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOpioidPerioperativeAnterior cruciate ligamentTramadolMorphineAnterior cruciate ligament reconstructionKetorolac

Résumé

récupéré en direct d'OpenAlex

Chapter 2 (Study 1): Background: Opioid use beyond typical postoperative pain timelines remains an adverse surgical outcome. Anterior cruciate ligament reconstruction and repair (ACLRR) are common surgeries whose perioperative opioid demands have not been characterized in a Canadian setting. Methods: In this retrospective cohort study, we identified ACLRRs performed between 2009–2017 in Alberta among patients aged 10–65. Using linked community pharmacy dispensation data, we evaluated time trends in the percentage of patients with preoperative opioid exposure and in initial postoperative opioid dispensation characteristics for opioid-naïve patients. We described typical month-to-month opioid demand for one year following ACLRR, wherein we distinguished patients exhibiting >90 days of opioid supply (LTOT). Results: Across 15,675 ACLRRs, preoperative opioid exposure increased from 2009 (6.6%) to 2016-17 (9.9%). Opioid-naïve patients more frequently received postoperative opioids in 2016–17 (89.2%) than in 2009 (66.7%). By 2016–17, initiating dispensations among opioid-naïve patients became more likely to contain tramadol (49.6%), involve ≥50 morphine milligram equivalent daily dosages (43.6%), and be indicated for use over 5–7 days (57.8%). 304 patients (1.9%) exhibited LTOT during their first postoperative year. LTOT rate was stratified by patient preoperative opioid exposure, ACLRR surgical type, and patient age, but did not significantly change over the study period. Conclusion: Perioperative opioid dispensations in ACLRR increased in frequency and dosage from 2009–2017 in Alberta, especially among patients without preoperative opioid exposure, alongside no significant change to overall postoperative LTOT rate. ACLRR-specific clinical guidance may be necessary for future widespread adoption of opioid-sparing and multimodal postoperative analgesia. Chapter 3 (Study 2): Background: Postoperative long-term opioid therapy (LTOT) provides minimal patient benefit while conferring substantial potential for harm. Among anterior cruciate ligament (ACL) reconstruction and repair (ACLRR) patients, the roles of preoperative non-opioid drug exposure and initial postoperative opioid dispensation characteristics on LTOT have not been elucidated. Hypothesis/Purpose: To identify preoperative, intraoperative and postoperative patient-level characteristics associated with changes in LTOT likelihood among patients undergoing ACLRR, while following recommendations to robustly define LTOT and to broadly include initiating opioid dispensation characteristics. Study Design: Cohort study. Methods: Physician billing codes were used to index ACLRRs performed between 2009–2017 in Alberta, Canada. Patient demographics, comorbidity history, preoperative opioid exposure and preoperative non-opioid drug exposure were determined for all ACLRR following linkage. Initial postoperative opioid dispensations were identified and categorized by dosage and duration for all preoperatively opioid-naïve patients. Associations between patient-level characteristics and postoperative LTOT were described via multivariable logistic regression models using three LTOT outcome constructs of varying stringency. Models were generated for the whole ACLRR cohort, as well as for the subset of patients undergoing ACLRR who were both opioid-naïve and who received opioids within their first 30 postoperative days. Results: 15,675 ACLRRs were included for analysis. Complete-cohort LTOT prevalence ranged from 304 (1.9%; Primary LTOT) patients to 1,701 (10.9%; Prior studies’ LTOT) patients. Preoperative opioid dispensation showed the strongest association with all LTOT outcome constructs. Other patient-level risk factors associated with increased LTOT included patient age >29, preoperative exposure to antidepressants, antipsychotics, and benzodiazepines; histories of substance use disorder and uncomplicated diabetes; and ACL repair <14 days from injury versus ACL reconstruction. Among patients without preoperative opioid exposure, initiating opioid dispensations of ≥50 morphine milligram equivalent daily dosage and of 15+ day duration were associated with increased LTOT. Patterns of association differed based on LTOT outcome choice. Conclusion: Numerous patient-level associations with increased LTOT are present among patients undergoing ACLRR, although preoperative opioid exposure remains a chiefly important predictor. Substantial differences in patterns of association between LTOT outcome constructs indicate a need for use of robust LTOT outcome measures in future research.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,060
Score d'incertitude au seuil0,120

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 Opus0,007
Tête enseignante GPT0,212
Écart entre enseignants0,204 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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