POS1379 A NOVEL MEASURE OF END-STAGE KNEE OSTEOARTHRITIS REDUCES THE DURATION AND SAMPLE SIZE REQUIRED FOR OBSERVATIONAL STUDIES AND TRIALS
Notice bibliographique
Résumé
Background Total knee replacement (TKR) has been used as an outcome measure in research into the causes and possible treatments for knee osteoarthritis (KOA). However, because KOA progresses slowly, and because TKR has a low incidence, research using TKR as an outcome measure necessitates long duration and/or large sample sizes. Moreover, TKR is influenced by multiple factors (such as education and income) besides the progression of KOA. Objectives We defined a novel outcome measure that signifies end-stage KOA (esKOA); and determined whether esKOA was sensitive enough to detect the effect of an exposure that is known to have a modest effect on reducing TKR, namely weight loss. Methods A knee was considered to have esKOA if any of the following two conditions were met: 1) moderate, intense, or severe KOA symptoms (i.e., the sum of the pain and disability scores on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) ≥ 12) and severe radiographic knee osteoarthritis (RKOA), defined as a Kellgren and Lawrence Grade (KLG) of 4); or 2) intense or severe KOA symptoms (i.e., the sum of the pain and disability scores on the WOMAC ≥ 23) and frequent knee pain (i.e., knee pain on most days of one or more months in the past 12 months) and mild or moderate RKOA (KLG = 2 or 3). We used data from two prospective cohort studies: the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST). We analyzed the data in two ways: as an observational study; and as an emulated trial. In the emulated trial, participants who lost ≥ 5% of their weight between baseline and 1 - 1.25 years (weight loss group) were matched with participants who gained ≥ 5% of their weight in that same period (weight gain group), using 1:1 nearest-neighbour matching on propensity scores. In both analyses, we used a multilevel mixed-effects generalized linear model. In the observational study, we investigated the association of weight loss between baseline and the following time points with esKOA and TKR at these time points: 1 - 1.25 years; 2 - 2.5 years; and 4 - 5 years. In the emulated trial, we compared the odds of incidence of esKOA and TKR between the weight loss and weight gain group at the following time points: 2 - 2.5 years; and 4 - 5 years. Results The observational study included 7107 participants (58.4% female, mean ± SD age and BMI 61.4 ± 8.8 years and 29.2 ± 5.1 kg/m2 at baseline, and an incidence of esKOA of 2.9, 6.8, and 10.4% at 1 - 1.25 years, 2 - 2.5 years, and 4 - 5 years, respectively, and a corresponding incidence of TKR of 0.1, 0.5, and 1.6%. While weight loss was associated with a reduced adjusted odds ratio (aOR) for both esKOA and TKR at 4 - 5 years (for 5% weight loss: 0.85 [95% CI 0.79 - 0.92] for esKOA and 0.79 [0.67 - 0.93] for TKR), weight loss was only associated with a reduced aOR for esKOA - and not TKR - at the earlier time point of 2 - 2.5 years (for 5% weight loss: 0.80 [0.72 - 0.90] for esKOA and 1.05 [0.76 - 1.45] for TKR). At 1 - 1.25 years, there was no association between weight loss and esKOA or TKR. The sample size required to detect a 50% reduction in the odds of esKOA was 6% to 13% of the sample size required for that of TKR (236 versus 3990 at 2 - 2.5 years; 162 versus 1286 at 4 - 5 years). In the emulated trial, compared to the weight gain group (367 participants), the weight loss group (also 367 participants) had significantly lower odds of esKOA but not TKR at 4 - 5 years (0.43 [0.22 - 0.84] for esKOA and 0.39 [0.06 - 2.67] for TKR). There was no difference between groups in the odds of esKOA or TKR at the earlier time point of 2 - 2.5 years in the emulated trial. Conclusion Given that our novel measure of esKOA could detect an association with weight loss at a time point 1.5 - 3 years earlier than TKR in an observational study, and in a sample size that was too small to detect an association with TKR at 4 - 5 years in an emulated trial, esKOA is recommended as an outcome measure for observational studies and trials investigating causes and possible treatments for KOA. Our powerful novel measure of esKOA enables shorter and smaller – hence cheaper – studies, which can boost the research on effective treatment for KOA. Acknowledgements We acknowledge the provision of datasets and/or research tools from two cohort studies: the Osteoarthritis Initiative (OAI) study and the Multicenter Osteoarthritis Study (MOST. Disclosure of Interests Zubeyir Salis: None declared, Jeffrey Driban Consultant of: Consultant for Pfizer Inc and Eli Lilly and Company., Timothy McAlindon Consultant of: Consultant for Remedium-Bio, Anika, Chemocentryx, Grunenthal, Kolon Tissue Gene, Novartis, BioSplice, Organogenesis, and Pfizer Inc., Amanda Sainsbury-Salis Speakers bureau: Received presentation fees and travel reimbursements from Eli Lilly and Co, the Pharmacy Guild of Australia, Novo Nordisk, the Dietitians Association of Australia, Shoalhaven Family Medical Centres, the Pharmaceutical Society of Australia, and Metagenics, and serving on the Nestlé Health Science Optifast VLCD advisory board from 2016 to 2018.
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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 | 0,073 | 0,173 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,003 |
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 ».