VARIATION IN EARLY MAJOR COMPLICATION RATES FOLLOWING TOTAL HIP ARTHROPLASTY BETWEEN HOSPITALS IN ONTARIO: A POPULATION-BASED STUDY
Notice bibliographique
Résumé
Although early major surgical complications following total hip arthroplasty (THA) occur rarely, complications are significant for those that experience them. As discussed in the 2020 Report on Early Revisions of Hip and Knee Replacements in Canada , early major surgical complications “are more likely to be due to conditions surrounding the surgery or to the surgery itself…can be viewed as largely avoidable, and represent actionable opportunities to improve quality of care [and] increase health system productivity and capacity.” The purpose of this study was to determine the influence of hospital-level surgical practices on early major surgical complications across Ontario. We conducted a population-based retrospective cohort study of all adults in Ontario, Canada who had undergone primary THA for osteoarthritis between April 1, 2008 and March 31, 2019. Patients treated at hospitals completing fewer than 200 THAs during the study period were excluded. All patients were followed for one year (study end date March 31, 2020 i.e. prior to the COVID-19 pandemic). The primary outcome was early major surgical complications defined as a composite of deep infection requiring surgery, dislocation requiring closed or open reduction, or revision surgery occurring within 1 year of surgery. Medical complications occurring within 30 days of surgery (PE/DVT, MI, pneumonia) were also assessed. The random effects output from two-level hierarchical logistic regression models adjusted for age, sex and Charlson score were used to calculate each hospital's unique adjusted complication rate and 95% CI. Hospitals that had significantly different adjusted complication rates from the average were defined as statistical ‘outliers’ as follows: 1) ‘Low outliers’ as those with the upper limits of their 95% CI less than the mean cohort rate and 2) ‘High outliers’ as those with a lower limit of their 95% CI greater than the mean cohort rate. During the study period, 95,912 patients (mean [SD] age 67 [11.0] years; 51,216 (53.4%) women) underwent THA at 56 hospitals across Ontario. 1,656 (1.7%) patients had a major surgical complication within 1 year. Major surgical complication rates varied 7-fold between hospitals from 0.6% to 4.1%. After adjustment, 4 of 56 hospitals were low outliers (adjusted complication rate significantly lower than the average) and 5 of 56 were high outliers (adjusted complication rate significantly higher than the average). In contrast, there were no hospital outliers for medical complications (i.e. medical complications were not significantly different between hospitals). There was significant variation in early major surgical complication rates between Ontario hospitals that persisted after adjustment for patient age, sex and medical comorbidity. That we observed significant hospital-level variation in surgical but not medical complications suggests differences in surgical practices at different hospitals contributes to outcomes in addition to case mix alone. Feeding back adjusted outcomes in benchmarking reports may enable individual hospitals and surgeons better consider their own performance and scale up best practices. Future research should try understand causes of variable complication rates between hospitals and whether variability extends to PROMs.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».