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Enregistrement W4284974619 · doi:10.1097/corr.0000000000002306

How do Patient-reported Outcome Scores in International Hip and Knee Arthroplasty Registries Compare?

2022· article· en· W4284974619 sur OpenAlexaff
Lina Holm Ingelsrud, J. Mark Wilkinson, Søren Overgaard, Ola Rolfson, Brian R. Hallstrom, Ronald A. Navarro, Michael Terner, Sunita Karmakar-Hore, Greg Webster, Luke Slawomirski, Adrian Sayers, Candan Kendir, Katherine De Bienassis, Niek Klazinga, Annette W. Dahl, Éric Bohm

Notice bibliographique

RevueClinical Orthopaedics and Related Research · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueOrthopaedic implants and arthroplasty
Établissements canadiensUniversity of ManitobaCanadian Institute for Health Information
Organismes subventionnairesnon disponible
Mots-clésMedicineArthroplastyPhysical therapyOrthopedic surgerySports medicineMEDLINEOutcome (game theory)Patient-reported outcomeHip arthroplastySurgeryQuality of life (healthcare)

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Patient-reported outcome measures (PROMs) are the only systematic approach through which the patient's perspective can be considered by surgeons (in determining a procedure's efficacy or appropriateness) or healthcare systems (in the context of value-based healthcare). PROMs in registries enable international comparison of patient-centered outcomes after total joint arthroplasty, but the extent to which those scores may vary between different registry populations has not been clearly defined. QUESTIONS/PURPOSES: (1) To what degree do mean change in general and joint-specific PROM scores vary across arthroplasty registries, and to what degree is the proportion of missing PROM scores in an individual registry associated with differences in the mean reported change scores? (2) Do PROM scores vary with patient BMI across registries? (3) Are comorbidity levels comparable across registries, and are they associated with differences in PROM scores? METHODS: Thirteen national, regional, or institutional registries from nine countries reported aggregate PROM scores for patients who had completed PROMs preoperatively and 6 and/or 12 months postoperatively. The requested aggregate PROM scores were the EuroQol-5 Dimension Questionnaire (EQ-5D) index values, on which score 1 reflects "full health" and 0 reflects "as bad as death." Joint-specific PROMs were the Oxford Knee Score (OKS) and the Oxford Hip Score (OHS), with total scores ranging from 0 to 48 (worst-best), and the Hip Disability and Osteoarthritis Outcome Score-Physical Function shortform (HOOS-PS) and the Knee Injury and Osteoarthritis Outcome Score-Physical Function shortform (KOOS-PS) values, scored 0 to 100 (worst-best). Eligible patients underwent primary unilateral THA or TKA for osteoarthritis between 2016 and 2019. Registries were asked to exclude patients with subsequent revisions within their PROM collection period. Raw aggregated PROM scores and scores adjusted for age, gender, and baseline values were inspected descriptively. Across all registries and PROMs, the reported percentage of missing PROM data varied from 9% (119 of 1354) to 97% (5305 of 5445). We therefore graphically explored whether PROM scores were associated with the level of data completeness. For each PROM cohort, chi-square tests were performed for BMI distributions across registries and 12 predefined PROM strata (men versus women; age 20 to 64 years, 65 to 74 years, and older than 75 years; and high or low preoperative PROM scores). Comorbidity distributions were evaluated descriptively by comparing proportions with American Society of Anesthesiologists (ASA) physical status classification of 3 or higher across registries for each PROM cohort. RESULTS: The mean improvement in EQ-5D index values (10 registries) ranged from 0.16 to 0.33 for hip registries and 0.12 to 0.25 for knee registries. The mean improvement in the OHS (seven registries) ranged from 18 to 24, and for the HOOS-PS (three registries) it ranged from 29 to 35. The mean improvement in the OKS (six registries) ranged from 15 to 20, and for the KOOS-PS (four registries) it ranged from 19 to 23. For all PROMs, variation was smaller when adjusting the scores for differences in age, gender, and baseline values. After we compared the registries, there did not seem to be any association between the level of missing PROM data and the mean change in PROM scores. The proportions of patients with BMI 30 kg/m 2 or higher ranged from 16% to 43% (11 hip registries) and from 35% to 62% (10 knee registries). Distributions of patients across six BMI categories differed across hip and knee registries. Further, for all PROMs, distributions also differed across 12 predefined PROM strata. For the EQ-5D, patients in the younger age groups (20 to 64 years and 65 to 74 years) had higher proportions of BMI measurements greater than 30 kg/m 2 than older patients, and patients with the lowest baseline scores had higher proportions of BMI measurements more than 30 kg/m 2 compared with patients with higher baseline scores. These associations were similar for the OHS and OKS cohorts. The proportions of patients with ASA Class at least 3 ranged across registries from 6% to 35% (eight hip registries) and from 9% to 42% (nine knee registries). CONCLUSION: Improvements in PROM scores varied among international registries, which may be partially explained by differences in age, gender, and preoperative scores. Higher BMI tended to be associated with lower preoperative PROM scores across registries. Large variation in BMI and comorbidity distributions across registries suggest that future international studies should consider the effect of adjusting for these factors. Although we were not able to evaluate its effect specifically, missing PROM data is a recurring challenge for registries. Demonstrating generalizability of results and evaluating the degree of response bias is crucial in using registry-based PROMs data to evaluate differences in outcome. Comparability between registries in terms of specific PROMs collection, postoperative timepoints, and demographic factors to enable confounder adjustment is necessary to use comparison between registries to inform and improve arthroplasty care internationally. LEVEL OF EVIDENCE: Level III, therapeutic study.

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)0,133
score de la tête « metaresearch » (Gemma)0,342
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 enseignants0,133
Score d'incertitude au seuil0,706

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,1330,342
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0100,016
Études des sciences et des technologies0,0010,002
Communication savante0,0060,006
Science ouverte0,0020,005
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,119
Tête enseignante GPT0,401
Écart entre enseignants0,282 · 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

Citations38
Publié2022
Routes d'admission1
Résumé présentoui

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