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Enregistrement W4415929182 · doi:10.2106/jbjs.25.01006

Weight Loss After Joint Replacement: Fact or Fiction?

2025· article· en· W4415929182 sur OpenAlexaff
Lisa C. Howard

Notice bibliographique

RevueJournal of Bone and Joint Surgery · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueTotal Knee Arthroplasty Outcomes
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésPeriprostheticOverweightBody mass indexArthroplastyJoint arthroplastyWeight lossProsthesisAmputation

Résumé

récupéré en direct d'OpenAlex

Commentary As the debate continues among surgeons regarding the importance of body mass index (BMI) in patients undergoing primary total hip arthroplasty (THA) or total knee arthroplasty (TKA), the article by Karczewski et al. is well-timed. A staggering one-half of patients undergoing primary TKA and one-third of patients undergoing THA1,2 have a BMI of ≥30 kg/m2. It is known that obesity increases surgical risks, including instability, thromboembolic events, and periprosthetic joint infection3,4, although the periprosthetic joint infection is often debated. Overweight patients often view joint replacement as a means to facilitate weight loss. However, weight loss is a multifactorial process, with increased physical activity representing only 1 of several contributing factors. Performing a joint replacement in a patient with high BMI can pose considerable technical surgical challenges. Achieving enough exposure for an adequate debridement is challenging on its own, and the excess adipose tissue often necroses, making a watertight closure difficult. In the most drastic of scenarios, if an excision arthroplasty or amputation is required, these patients are often not candidates for a prosthesis; the stakes are higher. In their retrospective study over 11 years, Karczewski et al. included 763 patients who underwent primary THA or TKA and had recorded BMI measurements at the time of surgery as well as at follow-up points of 2, 5, and 10 years. They also included additional cohorts of patients who had BMI measurements recorded at surgery and at either 2, 5, or 10 years postoperatively. Although the mean BMI changes were significant at 2 and 5 years for both patients who underwent THA and those who underwent TKA, these changes were small. The authors concluded that, overall, there was no meaningful change in mean BMI, defined in accordance with the U.S. Food and Drug Administration as >5% total body weight loss. However, the distribution of the change tells a more interesting story. At 10 years, a >5% change in BMI after THA occurred in 57% of patients, with 30% increasing and 27% decreasing by >5% relative to their BMI at surgery. The TKA group was similar, with 32% increasing and 30% decreasing in BMI by >5%. Also, although almost one-third of patients did lose meaningful weight at 10 years, another almost one-third of patients gained meaningful weight. Female patients were found to have twice the risk of gaining >5% of their BMI at 10 years after THA (odds ratio, 2.14; p = 0.006), whereas older patients were less likely to gain at 10 years after TKA (odds ratio, 0.95; p < 0.001). Retrospective studies such as this will always face criticism, given the inherent bias in their design. Furthermore, the impact of BMI as it relates to joint replacement is heterogenous and a source of great debate. The authors should be commended for their large numbers, long study duration, and robust methodological attempts (well-executed analysis of variance and multinomial logistical regression) to circumvent the limitations of retrospective studies. However, the lack of generalizability outside of a predominantly White population at a single institution is an important limitation. Nevertheless, this study will hopefully jumpstart needed studies on other demographic and racial distributions. In addition, patients lost to follow-up, who may be systematically different, were excluded from this study. Although the BMI did not differ at surgery between the included patients and patients lost to follow-up, the impact of these missing data cannot be concretely determined. Finally, this article further illuminates the utility of BMI as a tool for weight assessment. Despite its ease of use, BMI does not take into account weight distribution, body composition, and other metabolic risk factors. Despite these limitations, Karczewski et al. produced meaningful research that contributes to and improves upon the current literature, as previous studies examining BMI changes after arthroplasty have been limited to a single short-term postoperative measurement5–8. The authors have given us evidence-based research to inform patients that joint replacement is not a solitary means for weight loss and does not replace adequate workup and management of risk factors that contribute to their overall risk. Put plainly, patients angling for a joint replacement as a motivation for weight loss are likely misinformed and require their expectations to be adjusted. The weight loss journey involves optimization of factors such as metabolic syndrome, insulin resistance, and diet, all of which are arguably more impactful than exercise alone. This study also suggests that, after arthroplasty, women gain weight more than men as they age, which should be factored into individual patient expectation discussions. Physicians should carefully consider the results of this study as a springboard for further research as well as to inform their practice, particularly as it relates to adjusting patient expectations.

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)0,006
score de la tête « metaresearch » (Gemma)0,051
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: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,051
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,002
Études des sciences et des technologies0,0020,005
Communication savante0,0020,006
Science ouverte0,0050,001
Intégrité de la recherche0,0180,027
Charge utile insuffisante (le modèle a refusé de juger)0,0060,004

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 Opus0,019
Tête enseignante GPT0,259
Écart entre enseignants0,240 · 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
Publié2025
Routes d'admission1
Résumé présentoui

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