Predicting Recovery Following Total Hip and Knee Arthroplasty Using a Clustering Algorithm
Notice bibliographique
Résumé
BackgroundRecovery following total joint arthroplasty is patient-specific, yet groups of patients tend to fall into certain similar patterns of recovery. The purpose of this study was to identify and characterize recovery patterns following total hip arthroplasty (THA) and total knee arthroplasty (TKA) using patient-reported outcomes that represent distinct health domains. We hypothesized that recovery patterns could be defined and predicted using preoperative data.MethodsAdult patients were recruited from a large, urban academic center. To model postoperative responses to THA and TKA across domains such as physical health, mental health, and joint-specific measures, we employed a longitudinal clustering algorithm that incorporates each of these health domains. The clustering algorithm from multiple health domains allows the ability to define distinct recovery trajectories, which could then be predicted from preoperative and perioperative factors using a multinomial regression.ResultsFour hundred forty-one of 1134 patients undergoing THA and 346 of 921 undergoing TKA met eligibility criteria and were used to define distinct patterns of recovery. The clustering algorithm was optimized for 3 distinct patterns of recovery that were observed in THA and TKA patients. Patients recovering from THA were divided into 3 groups: standard responders (50.8%), late mental responders (13.2%), and substandard responders (36.1%). Multivariable, multinomial regression suggested that these 3 groups had defined characteristics. Late mental responders tended to be obese (P = .05) and use more opioids (P = .01). Substandard responders had a larger number of comorbidities (P = .02) and used more opioids (P = .001). Patients recovering from TKA were divided among standard responders (55.8%), poor mental responders (24%), and poor physical responders (20.2%). Poor mental responders were more likely to be female (P = .04) and American Society of Anesthesiologists class III/IV (P = .004). Poor physical responders were more likely to be female (P = .03), younger (P = .04), American Society of Anesthesiologists III/IV (P = .04), use more opioids (P = .02), and be discharged to a nursing facility (P = .001). The THA and TKA models demonstrated areas under the curve of 0.67 and 0.72.ConclusionsThis multidomain, longitudinal clustering analysis defines 3 distinct patterns in the recovery of THA and TKA patients, with most patients in both cohorts experiencing robust improvement, while others had equally well defined yet less optimal recovery trajectories that were either delayed in recovery or failed to achieve a desired outcome. Patients in the delayed recovery and poor outcome groups were slightly different between THA and TKA. These groups of patients with similar recovery patterns were defined by patient characteristics that include potentially modifiable comorbid factors. This research suggests that there are multiple defined recovery trajectories after THA and TKA, which provides a new perspective on THA and TKA recovery.Level of EvidenceIII.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| 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 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».