PREDICTORS OF SUCCESSFUL SAME-DAY DISCHARGE FOLLOWING PRIMARY HIP AND KNEE ARTHROPLASTY
Notice bibliographique
Résumé
Advances in anaesthetic technology and orthopaedic practice are enabling same day discharge (SDD) following total hip and knee arthroplasty (THA and TKA). SDD is also an appealing strategy for resource constrained health systems to facilitate THA and TKA for increasing numbers of patients disabled by endstage hip and knee arthritis. Previous research of SDD has aimed to identify suitable patients for this endeavour, but no consensus is available regarding demographics, comorbidities or preoperative scoring systems. One limitation of prior research is that data derives from single ‘centres of excellence’ which may not be generalisable, particularly to smaller hospitals across a large health care system. In this context our goals were two-fold: 1) Assess the safety of SDD across all centres performing THA and TKA in Ontario and 2) Identify patient, surgeon and institutional variables that were significantly associated with failure of SDD. We conducted a population-based, retrospective cohort study of all patients undergoing primary THA and TKA in Ontario between 2016 to 2021. Data was extracted from the ICES database and previously validated algorithms were utilised to identify patients, covariates and outcomes. Inclusion criteria included patients undergoing primary total hip or knee arthroplasty treated by all surgeons and hospitals in Ontario. We excluded revision arthroplasties and other arthroplasty operations (ex. partial knee replacements). “Failure” of SDD was defined as the inability to discharge the patient on the same day / admission to hospital following originally planned SDD. Of 58,120 THAs completed between 2016 and 2021, 3,380 patients were planned for SDD. There were no differences in medical complications (DVT/PE, MI, pneumonia) between those planned SDD versus inpatients. The proportion of patients planned for SDD increased from 0.5% in 2016 to 33% in 2021. Of those planned for SDD, 2981 (88.4%) were successful and 393 (11.6%) failed/could not be discharged the same day. Predictors for failure were Charlson index (p=0.042), obesity (p=0.002), female gender (p<0.001), PUD (p=0.011), frailty (p<0.002), hypertension (p=0.022), use of general anaesthetic (p<0.001) and surgical complications (p=0.006). Of 82,646 TKAs completed between 2016 and 2021, 2,776 patients were planned for SDD. Similar to THA, there were no differences in medical complications (DVT/PE, MI, pneumonia) or mortality within 30 days between those planned SDD versus inpatients following TKA. The proportion of patients planned for SDD increased from 0% in 2016 to 27% in 2021. Of those planned for SDD, 2462 (88.7%) were successful and 314 (11.3%) failed. Once again predictors of failure included Charlson index (p=0.037), frailty (p<0.01), female gender (p=0.019) and use of general anaesthetic (p<0.001). Interestingly for both THAs and TKAs, those with successful SDD were more likely to have an unplanned ED visit within 30 days of surgery (p<0.001). SDD following THA and TKA in Ontario increased from <1% in 2016 to nearly a third of patients in 2021. The early experience of SDD following these procedures in Ontario appears to be safe without and increased risk of medical complications and mortality compared to inpatients. Failure of SDD only occurred in about 10% of patients. Our findings that female gender, obesity, medical comorbidity and general anaesthetic delaying discharge can be helpful in planning for SDD success. Interestingly, the increased ED admission in those with successful SDD suggests a failure to detect early complications, negate patient anxieties or deal with conditions suitable for primary care and is an area for improvement going forward. Future research should assess low fidelity testing including clinical examination findings to help predict SDD success.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».